# Fifty Five and Five > We build AI tools for sales and marketing teams Fifty Five and Five is a London agency that builds AI tools for marketing, communications and sales teams at large technology companies, much of it for partner marketing. For Microsoft we built the Partner Benchmarking Tool, which has analysed $10m of marketing development funds across more than 10,000 partners. For SAP we verified and enriched around a thousand partner accounts in under two weeks, matching 93% of them to active companies. For TCS we built Compass Agents, which produces more than 80 social posts a month and saves the team over 60 hours a month, and an AI finish-line video experience at the 2025 London Marathon that made more than 1,500 personalised videos in three days and won Bronze at The Drum Awards for B2B 2025 (Campaigns Powered by AI). We have also worked with Google and Algolia. Not fifty-five (the data consultancy) or Fifty & Five (US social agency). --- ## Case studies ### Turning a thousand stale partner records into sales-ready data URL: https://fiftyfiveandfive.com/work/partner-data-enrichment/ > SAP had invested heavily in partner marketing but their PRM data was outdated, incomplete and unreliable. We cleaned, verified and enriched around a thousand partner accounts into a targeted, sales-ready dataset with personalised conversation starters for every contact. Client: SAP Results: - 93% of partner accounts successfully verified and matched - ~70% of contacts validated with verified email addresses - 2 weeks from raw data to sales-ready output #### Challenge SAP's partner marketing managers had around a thousand partner accounts sitting in their PRM across multiple regions. They had built co-branded campaigns, content kits and activation playbooks, but none of it was going anywhere because the underlying data was a mess. Company names were inconsistent, contacts were outdated or missing entirely, and there was no way to know if they were reaching the person who could actually act on the material. Some partners had merged. Others had rebranded. A fair few had no web presence at all. Previous attempts using traditional data providers had been slow, expensive, and still left gaps. They needed a different approach, and they needed it before their campaign launch date. #### Solution We started by running the full dataset through our AI-powered data cleaning pipeline. Bespoke tools resolved company name inconsistencies, caught mergers and rebrandings, and flagged partners with no verifiable web presence. Over 93% of the original partner accounts were successfully verified and matched to active companies. With clean account data in place, we then identified the most relevant contacts at each partner. We ran every contact through multi-stage email verification. Around 70% came back with verified, deliverable email addresses. The rest were flagged or removed rather than left in to pollute the dataset. We applied a prioritisation layer on top. Not every contact is equally useful for partner marketing, so we built a selection hierarchy: marketing leads first, then sales, then leadership, then business development. The result was one best contact per partner, the person most likely to engage on a co-marketing conversation. The final step was the bit that made this genuinely useful. We enriched every contact with LinkedIn profile data, time in role, recent posts and articles, relevant certifications, and AI-generated intent signals. Each contact came with a concise summary and bullet-pointed conversation starters tailored to their role and activity. #### Outcome SAP went from a messy spreadsheet of company names to a fully enriched, verified dataset ready for their partner marketing team to action immediately. Every contact came with verified email addresses, LinkedIn profiles, role context, and personalised conversation starters. The partner marketing team could pick up the phone or send an email knowing exactly who they were talking to and what to talk about. More importantly, all that enablement content they had invested in now had somewhere to go. Instead of blasting generic emails to outdated contacts and hoping for the best, they had targeted data built around the right people at the right partners. The entire project, from raw data to sales-ready output, was completed in under two weeks. A process that would have taken months using traditional data providers and manual research. Quote: "Working with Chris and the Fifty Five and Five team has been an absolute pleasure. They are extremely thorough, flexible, and easy to work with. More importantly, they provided a high-value deliverable that directly propelled SAP's business success within our partner channel. Their ability to combine strategic insight with seamless execution makes them a standout partner. They know how to drive results for their customers." (Justin Molloy, Senior Director, SAP Global Partner Marketing) --- ### Creating finish-line moments with AI URL: https://fiftyfiveandfive.com/work/tcs-marathon-2025/ > From a quick selfie to a finish-line moment. This AI-powered activation let thousands of runners see themselves complete the TCS London Marathon 2025 before it even began. Client: TCS Results: - 1500+ Videos generated over 3 days - 40,000 Machines rendering content in parallel - 9 Different AI tools used in production #### Challenge TCS wanted to deliver a memorable brand experience and tech demonstration at one of the biggest sporting events of the year. The idea was simple but ambitious. Create a digital activation where visitors could see themselves finish the race before it even began. To make it work, we needed clean, branded footage of the iconic finish line, without any runners in shot, so that each user's 3D avatar could be added in. But existing footage was expensive, crowded, and out of date. AI tools offered promise, but also introduced challenges with realism, landmark accuracy, and branding consistency. We also had to work within tight limits. The experience had to run smoothly on mobile, render quickly, and avoid long queues. All of this had to happen within a month, under budget, and inside a busy event environment with limited Wi-Fi. #### Solution To deliver a smooth, memorable experience at scale, we designed a fast and automated flow from photo to finish line. Visitors accessed the microsite via a QR code, where trained brand ambassadors helped capture their photo. This image was then analysed using AI, which suggested avatar preferences like body type, skin tone, and hairstyle, including options to support prosthetic limbs. Once confirmed, the data was passed to our avatar platform via API. A 3D model was generated automatically, placed into pre-rendered race scenes, and composited into a personalised finish-line video. This automation allowed us to render and return each video quickly, keeping queues short and the experience moving. When the video was ready, users received an email notification inviting them back to the microsite. There, they could watch and share their video directly using their device's built-in sharing options. Every step, from avatar creation to video delivery, was optimised for speed, accuracy, and mobile use, making the experience both seamless and scalable. #### Outcome The activation was a success, with thousands of personalised finish-line videos generated and shared over the course of the event. After some early fine-tuning, the system ran smoothly and consistently, handling large volumes of users without delays. Visitors returned to the microsite to view and share their videos, often bringing friends or family back to take part as well. The experience created a real sense of excitement and connection, helping people imagine the finish-line moment before they even started the race. The activation also gained coverage from BBC News, adding to its reach and visibility. Feedback from the client was positive, and the project proved how AI-driven experiences can deliver high-quality personalisation at speed, even in high-pressure environments. The work won Bronze at The Drum Awards for B2B 2025, in the Campaigns Powered by AI category. Quote: "What Fifty Five and Five built in the time they had was amazing. 1500 runners had a photo realistic AI video created in near real time." (Anmol Patel, Social Media & Insights Manager, TCS) --- ### Transforming ABM with precision data URL: https://fiftyfiveandfive.com/work/northern-data-group/ > Northern Data Group switched to Compass Data and within three months identified thousands of high-quality prospects, doubling industry-standard email open rates. Client: Northern Data Group Results: - 2x Email open rates versus industry standard - 1000s High-quality prospects identified in three months - 3 Months to transform marketing ROI #### Challenge Northern Data Group is a leader in AI and high-performance computing, but their marketing data infrastructure wasn't keeping pace. Existing data enrichment tools were expensive, inconsistent and full of hidden costs that made it difficult to plan and budget effectively. The team needed a reliable way to identify and reach high-quality prospects at scale without the constant pricing surprises. Their ABM campaigns were underperforming because the underlying data wasn't accurate or targeted enough to drive meaningful engagement. #### Solution We introduced Northern Data Group to Compass Data, our fully managed data enrichment service built specifically for B2B marketing teams. Compass Data replaced their patchwork of tools with a single, transparent solution that delivered clean, enriched prospect data with precision. Using AI-driven insights, we helped the team build highly targeted audience segments for their ABM campaigns. The platform identified thousands of qualified prospects and enriched their profiles with the data points needed to personalise outreach at scale - all without the hidden costs and inconsistent results they had experienced before. #### Outcome Within three months, the results spoke for themselves. Northern Data Group's ABM campaigns were reaching the right people with the right message, and the numbers proved it. Email open rates hit twice the industry standard, generating multiple high-value marketing qualified leads. By removing hidden costs and streamlining their enrichment workflows, the team transformed their marketing ROI and gained confidence in their pipeline data for the first time. The partnership demonstrated how the right data foundation can turn underperforming campaigns into a consistent source of qualified opportunities. --- ### Content that works for AI search URL: https://fiftyfiveandfive.com/work/algolia-ai-search/ > We help Algolia turn complex ideas into clear, high-performing content that boosts SEO and drives engagement. Client: Algolia Results: - +27% Keywords ranking on page 1 of Google SERPs - +15% Increase in overall blog sessions on Algolia's website - +24% Keywords ranked in the top 3 positions on page 1 of Google SERPs #### Challenge Being a thought leader requires insights and content that not only engages your audience but steers the conversations that are taking place across your industry. While an in-house team has its benefits, sometimes you need to find a partner – someone else who can speak your language to help you reach your audience. Algolia required a B2B content agency that could grapple with the complexities of their industry, write engaging content that fitted their tone of voice, performed well with their readers, and boosted organic traffic to their website. With their AI search and discovery platform serving over 17,000 customers, all content had to appeal to this large audience. #### Solution We knew that our content team was the right pick for Algolia. Our writers have a strong track record of interpreting briefs and delivering high performing content for our B2B tech clients that engage their target audience and provide immense value. Effective content marketing in B2B requires strong collaboration and planning to get it right. We assessed our weekly output and devised a suitable timetable to enable our writers to complete multiple content drops a month, ensuring we could deliver on quality and quantity. We put in place a methodology that made it easier for both sides to deliver and upload the very best content. Working with Algolia's digital marketing managers and freelancers, we aligned with their comprehensive guidelines and established robust working relationships that enabled edits to go smoothly. We also used the latest AI driven content techniques in our editing and proofing processes to speed up execution, without sacrificing quality. #### Outcome Algolia's blogs are one of their website's most powerful and effective tools for attracting relevant and qualified organic traffic (especially brand-new users). While the volume of organic traffic to their blogs was already quite substantial, our B2B content agency have made a big impact by increasing the traffic and awareness of Algolia's products. The growth in numbers and percentages in both organic traffic volume, as well as keywords ranking on the page 1 of Google SERPs, is impressive. As the first agency they've brought in to help achieve SEO content goals, we've delivered high-quality and top-performing content. Quote: "The Fifty Five and Five team quickly understood our product and purpose, as well as our audience needs. They have consistently delivered top quality content that really resonates with our users and made an enormous impact on our ability to grow our lead pipeline." (Vince Caruana, Senior Digital Marketing Manager, SEO, Algolia) --- ### Using AI to track ROI URL: https://fiftyfiveandfive.com/work/microsoft-content-scoring/ > We built a custom platform that analyses partner content using AI, helping Microsoft see which efforts deliver real value. Client: Microsoft Results: - 40+ Tests run across all kinds of content from multiple channels - 10k+ Partners helped across the globe, from huge multinationals to startups - $10m Marketing development funds analysed across the globe #### Challenge Fifty Five and Five has an ongoing partnership with many parts of Microsoft – we have been working with them since day one of the company. Our relationships with the corporate Partner Marketing teams in the US and UK are very strong. On one of our many visits to Seattle, we got chatting to some of the teams about the day-to-day challenges they face. One of the most fundamental issues they have is putting ROI on the marketing development funds (MDF) they give to partners around the world. Naturally, they decided to enlist Fifty Five and Five's B2B web development expertise and creativity to solve these challenges. #### Solution We developed a bespoke web platform that allowed Microsoft to scan, analyse and score Partner marketing efforts at scale. Our product, 'The Partner Benchmarking Tool', was able to analyse data from thousands of companies and tens of thousands of channels. It used a combination of machine learning and AI algorithms to deliver real time insights. It did this by running over 40 tests across all types of content. Not only can it check spelling and grammar, but it can also check the complexity of language and tone. It can report SEO signals and find technical errors and bugs in page code. Ultimately it was able to answer the question... 'Did giving marketing funds to this company result in better outcomes for us?' #### Outcome For the first time ever Microsoft was able to look objectively at their use of MDF funds and understand the return on investment. We gave them a state of the art AI platform that generated insights they couldn't get anywhere else. Microsoft has always worked with partners to understand the financial performance of the partnership. With this exclusive platform from Fifty Five and Five they can now understand the partners marketing performance. Quote: "Fifty Five and Five have developed an AI marketing platform that totally changes the game when it comes to understanding our financial investment in partners." (Jennifer Tomlinson, Global Channel Marketing Leader) --- ### Empowering TCS with Compass Agents URL: https://fiftyfiveandfive.com/work/tcs-compass-ai/ > TCS uses Compass Agents to power their social content - creating over 80 posts a month and saving more than 60 hours in production time. Client: TCS Results: - 80+ Posts generated by Compass Agents per month - 60+ Hours production time saved per month - 3x Faster from ideation to posting #### Challenge Having worked with TCS for many years, Fifty Five and Five has a deep understanding of their brand, goals and unique challenges they face in this evolving tech world. So when TCS's CMO, Ashish Babu, wanted to explore AI's potential in enhancing efficiency and creativity of their content marketing efforts, he turned to us for our guidance and expertise. With a history of delivering real-time, impactful content, we were the natural choice. After some initial workshops and demos around "The art of the possible", TCS's Head of Social, Suhail Adam, asked us to help him and his team enhance their content creation. #### Solution Enter Compass Agents. Compass Agents is an AI powered SaaS product developed in-house at Fifty Five and Five. It is designed specifically for enterprise marketing teams, and aims to give them 'agency super powers'. After working with Suhail on his brief we knew Compass Agents would be perfect the perfect tool to enhance their content marketing efforts. With a little fine tuning we set him and his team up on the platform, trained it on the TCS brand and tone of voice, and the team were away. Now, the team rely on Compass Agents for social media content, as well as converting video into copy quickly. The platform is trained on the best outputs for all social media channels and consistently produces great copy. #### Outcome Compass Agents is now embedded within TCS's social media team, relying on Compass Agents for real-time content generation and converting video into copy quickly. We are talking to key stakeholders regularly about new features, like the recent one which automatically detects people, and companies mentioned in social media copy, then populates their profile links. This is a great example of AI expediting an otherwise very manual and time-consuming process and highlights the positive impact of AI in social media marketing by making content creation more efficient and impactful. By automating these processes, TCS's social media team can focus more on creative and strategic efforts. Quote: "Compass Agents has significantly enhanced my control over the social post creation process. Whether I need to plan well in advance or make last-minute adjustments, Compass Agents enables me to surpass our objectives." (Anmol Patel, Social Media & Insights Manager, TCS) --- ### Smart sales content for Surface URL: https://fiftyfiveandfive.com/work/microsoft-surface/ > We created an interactive microsite that brings the Surface story to life, driving more leads and deeper engagement. Client: Microsoft Results: - 8+ Minutes average user session duration - 60% Of users returned to the interactive web page - 4x Leads generated compared to their traditional e-book #### Challenge Modern work comes in all shapes and sizes. So do Microsoft Surface devices, which are ideal for modern, flexible working, and on-the-go go-getting. A device with a difference - this project required more than just a microsite. Microsoft wanted a hero asset that married together tradition and modernity, just like the Surface device itself, and which drove up engagement for prospective buyers. The microsite would be a subsidiary piece for the Surface device web page, and a lead generation tool. One of the main priorities for Microsoft was to drive user engagement to their costs page and business cases. #### Solution As experts in crafting B2B solutions for engaging content, we wanted to flex our creativity. Along with Microsoft, we decided on an interactive microsite - not quite a web page, not quite an eBook. Something in-between, which pushed the boundaries of content creation at Fifty Five and Five and interactive content for B2B. We approached this interactive content asset differently, assigning more collaboration time between content writers and designers. Our writers picked out copy that was ideal for visual representation, crafting an experience that was engaging and immersive. Our design team worked to create something bespoke that perfectly complemented the devices themselves, incorporating interactive breakouts and eye-popping imagery. #### Outcome The end result is unique, just like the devices themselves. The microsite is informative, and provides prospective leads with in-depth content, but also has playful elements, which has kept engagement rate high. Currently, the interactive content is being used as both a sales tool - with a wealth of information to support the sales lifecycle - and a showcase piece for new leads. It has been well received by Microsoft and its partners, generating a significant amount of engagement in three months. The microsite has a very high session duration of just under 9 minutes (industry average is 2-3 minutes) - a testament to both the content and user experience. The cost section of the site is a top performer on the page. We drew users to this section with a structured user journey, interactive elements, and high-contrast colour emphasis. Quote: "This site really raised the bar in terms of the quality of content we have taken to Partners. The copy is beautifully written, and the interactive elements are keeping prospective buyers engaged." (Rob Stacey, Partner Marketing Lead, Microsoft) --- ## Resources (blog) ### AI enablement, or how to get your marketing team past "I tried it once" URL: https://fiftyfiveandfive.com/resources/ai-enablement-for-marketing-teams/ > AI enablement for marketing teams: how to get everyone using AI, not just two enthusiasts, with lessons from TCS's social team and from our own mistakes. Author: Chris Wright Date: 2026-10-22 Quick answer: Give the team one regular job where AI does the first draft, in a tool that has been taught your brand and your rules. Then spend proper time training people on their own briefs, and let leaders use it where everyone can see. A licence on its own leaves AI with the early adopters. This is for anyone running a marketing team where two people use AI all day and everyone else tried it once. AI enablement means putting AI inside a weekly job, set up with your brand rules, then training people on their own work. The enthusiasts are brilliant. I’m one of them. But to everyone else they can look like they know a secret, and meanwhile there’s a campaign that has to go out on Friday. The best example I have of a team getting past the early adopters is TCS’s social team. Our case study counts more than 80 posts a month from them with a tool we built and trained on their brand. Why does AI stay with two enthusiasts? For most people AI is still a blank chat box. It hasn’t been told the team’s rules, and people haven’t been given time to learn it, so the enthusiasts fill those gaps in their own time. Everyone else waits for someone to set it up for them. BCG surveyed 11,749 workers across 14 markets this year (BCG press release ). 74% of frontline employees now use AI every day or a few times a week, but only a third say leadership’s communications about AI are clear, and across everyone surveyed only 36% feel they’ve had adequate upskilling (BCG ). Back in 2024, Microsoft and LinkedIn found 78% of AI users were bringing their own AI tools to work (Microsoft Work Trend Index ). Your two enthusiasts are probably in that 78%. They found a tool and worked it out in their evenings, and now they’re quicker than everyone else. The rest of the team never got the same head start. When I look at teams where AI hasn’t spread, the same four things are usually missing: The tool doesn’t know your rules. Every prompt starts from nothing, so it has never heard of your brand voice or which clients you can’t name. The output sounds generic, someone has to fix it, and fixing it takes longer than writing it. There’s no job attached. “Use AI more” isn’t a task anyone can do on a Tuesday. Nobody’s been shown. People get a licence and a link, but never an afternoon on their own work. The boss doesn’t use it, or uses it quietly, so it never looks like part of the job. What does AI enablement mean for a marketing team? AI enablement for a marketing team is the work of getting AI into everyone’s normal week. It has four parts. First, pick the jobs where AI will help, such as first drafts of social posts or turning a webinar into copy. Second, set the tools up with the team’s own brand voice, tone and rules, so the first draft already sounds like the company. Third, put those tools where people already work, so nobody has to remember to open something new. Fourth, train people on their own briefs and channels, and keep a person checking the output before anything is published. AI sales enablement is a separate job, using AI to get the right content and data to a sales team (we’ve written about how marketing teams can use AI to help sales … --- ### What an AI marketing agent actually does all day (and what it needs from you first) URL: https://fiftyfiveandfive.com/resources/marketing-ai-agents/ > What an AI marketing agent actually does for a B2B marketing team, what it needs from you before it's any use, and how TCS gets 80+ posts a month from one. Author: Chris Wright Date: 2026-10-20 Quick answer: An AI marketing agent is software that takes on a marketing job, works out the steps and carries them out with your tools, then stops for a person to approve the result. For TCS's social team, Compass Agents drafts 80+ posts a month and saves 60+ hours of production time. Before it's any use it needs your brand rules, examples of good work and a clear sign-off rule. Running a B2B marketing team and being told to get “agents”? An AI marketing agent takes a job, like turning a webinar into a week of LinkedIn posts, does it with your tools and hands it back for a person to sign off. TCS’s social team already works this way. They get 80+ posts a month out of Compass Agents, the agent platform we built, and save more than 60 hours of production time a month (TCS case study ). I wrote the first version of this page in November 2024, and said every marketer would soon have “a full team of autonomous AI agents”, basically their own department. Half right. The agents turned up. “Autonomous” was the wrong word (I don’t use it now), and nobody got a department without doing a lot of writing down first. So this version is about what an agent does in a normal week, and what your team has to give it before it’s any use. What is an AI marketing agent? An AI marketing agent is software, built on a large language model, that takes a marketing job, works out the steps, uses your tools to carry them out and checks back with a person at set points. Anthropic, who make Claude, draw a useful line here. Workflows are systems where the AI follows “predefined code paths”. Agents are systems that “dynamically direct their own processes and tool usage”, and they “can then pause for human feedback at checkpoints or when encountering blockers” (Anthropic ). In marketing terms, a workflow is “every new webinar becomes three LinkedIn posts”. An agent is “here’s the webinar, work out what’s worth posting, draft it in our voice and show me”. They’re spreading fast. Microsoft’s 2026 Work Trend Index says the number of active agents in the Microsoft 365 ecosystem grew 15x in a year, and 18x in large enterprises (Microsoft ). How is an AI marketing agent different from marketing automation? Marketing automation runs rules a person wrote in advance. If someone downloads the guide, send email two three days later. It does exactly what it’s told, every time, and it can’t cope with anything the rules didn’t expect. An AI marketing agent gets a goal and some limits instead of a rule, for example “turn this webinar into a week of LinkedIn posts in our voice, and flag any customer we can’t name”. It reads the material, decides what to do, uses tools to do it and hands the result to a person to approve. An agent copes with messy material that would break an automation rule, and it can also get things wrong in ways a rule never would, so someone checks its work. Most B2B teams end up with both. The automation platform keeps sending the nurture emails, and the agent drafts the next batch for a person to approve before they’re loaded in. What can an AI marketing agent do for a B2B team day to day? The jobs agents do well right now have lots of material going in and a clear … --- ### Microsoft ECIF explained: the partner money your marketing plan can't spend URL: https://fiftyfiveandfive.com/resources/microsoft-ecif-explained/ > Microsoft ECIF pays for proofs of concept and deployments, decided by Microsoft's field teams. How it differs from co-op and MDF, and how partners get chosen for it. Author: Chris Wright Date: 2026-10-16 Quick answer: ECIF (End-Customer Investment Funds) is money Microsoft's own sales and marketing teams use to pay for proofs of concept and deployment work that help customers adopt Microsoft products. It's field-led, so Microsoft's account team decides where it goes, and it pays partners for services at a fair market price. Co-op funds and MDF are different. They pay for a partner's marketing, and the partner earns or applies for them and claims the money back. If you run marketing or alliances at a Microsoft partner and have heard “ECIF” in a meeting and nodded along, this is for you. Microsoft ECIF (End-Customer Investment Funds) is money Microsoft’s own field teams spend on proofs of concept and deployment work for a customer, and they choose the partner who does it. It doesn’t pay for your marketing, and you can’t claim it like co-op funds. I worked for Microsoft partners before I started Fifty Five and Five, and we’ve worked with Microsoft ever since. ECIF works differently from the funds most partner marketers know. If it’s your marketing funds you’re after, our Microsoft MDF guide covers those. What is Microsoft ECIF? Microsoft defines it in its own supplier toolkit. End-Customer Investment Funds “are used to drive growth and close pipeline by conducting a proof of concept or helping customers deploy Microsoft products and enhance the customer and partner experience through consulting services from Microsoft and our partners” (Microsoft ECIF toolkit ). So Microsoft pays a partner to help a customer actually start using its products, with a proof of concept or a migration, say. The same toolkit sets two rules worth knowing. ECIF paid to suppliers “has to reflect the fair market price of these services”, and “ECIF must never be used as a sales discount”. So you can’t use it to knock money off a licence. Who decides who gets ECIF? Microsoft’s account team. Jill Armour, a community manager on Microsoft’s partner community, put it simply: “ECIF Funding is a Microsoft field-led investment, not partner-led”, and partners should talk to their partner development manager or account team (Microsoft Tech Community ). That’s the opposite of co-op funds, which partners earn through Microsoft’s incentive programmes and claim back. With ECIF there’s no balance waiting in Partner Center. A Microsoft account team decides a customer project is worth funding, then picks who delivers it. To be picked, a partner has to be set up as an ECIF supplier. Microsoft’s toolkit says the request for quote “is a mandatory part of the ECIF Supplier onboarding in parallel with a vetting and compliance process”, and “No delivery can start without the full onboarding of your company”. It also says Microsoft “intends to select a limited number of suppliers” (Microsoft ECIF toolkit ). The rules for individual programmes change every year. One example, from Microsoft’s partner community team in January 2025: “Partners who provide a SoW with projected Azure Consumed Revenue at a 10:1 ratio can apply for ECIF funding” (Microsoft Tech Community ). And in July 2026 Microsoft said that for ECIF or field-nominated engagements, partners need a one-time activation to use its Cloud Accelerate Factory delivery programme (Partner Center announcements ). … --- ### How to get your company recommended by ChatGPT (we tested ourselves first) URL: https://fiftyfiveandfive.com/resources/how-to-get-your-company-recommended-by-chatgpt/ > We asked ChatGPT and Claude 18 buyer questions twice each and were named in 10 of 72 answers. What got us named, what didn't, and what it means for B2B brands. Author: Chris Wright Date: 2026-10-15 Quick answer: Have a page that answers the exact question a buyer asks, with specific proof such as a named client and real numbers near the top. Then get onto the third-party lists and directories AI assistants read, and keep what they say about you up to date. When we tested ourselves, ChatGPT mostly cited providers' own pages and Claude mostly repeated names from "best of" lists. If you run marketing at a B2B tech company and have typed your own category into ChatGPT to see who comes up, this is for you. To get your company recommended by ChatGPT, you need a page that answers the buyer’s exact question with real proof near the top, and you need to be on the lists and directories the AI assistants read. Most of ChatGPT SEO is those two things. I know this partly because we tested it on ourselves last week, and it wasn’t flattering. GEO (generative engine optimisation) is getting a company named in AI answers, and we test it on ourselves. When we asked ChatGPT and Claude the questions our own buyers ask, we came up in 10 of 72 answers. On the GEO questions themselves we came up in none. Below is what we ran and what we found. If you want the basics first, our piece on the difference between GEO and SEO covers them. What did we test? We wrote 18 questions a buyer might put to an AI assistant when looking for a company like ours. They fell into four groups: Microsoft partner and channel marketing, B2B tech agencies in the UK, AI tools and agents for marketing teams, and GEO. We asked each one twice on ChatGPT and twice on Claude, both with web search switched on and set to London. Then we recorded whether we were named, where we came in the list, and which pages each answer cited. The results, in short: Named Out of ChatGPT 2 36 Claude 8 36 Both 10 72 We also asked three questions about us by name. Both assistants got who we are right every time. These were answers through each company’s API, not the ChatGPT and Claude apps, which add their own instructions and memory. Two runs per question shows a pattern but isn’t precise, so trust the totals more than any single answer. Where do ChatGPT and Claude get their recommendations? They use different sources, and it was the most useful thing we learned. ChatGPT goes to the provider’s own page. Of the 174 pages it cited, 91% were agencies’ and vendors’ own service pages and articles. It found a page whose title and first paragraph answered the question, then built a shortlist of four or five names. It cited a “best of” list once. Claude reads other people’s lists. 61% of its 178 citations were “best agencies” and “top 10” articles, plus 21 directory and review pages. It then repeated the names it found. It even pointed out, in its answers, that a lot of these lists are written by agencies that rank themselves first. It used them anyway. So you need your own page for ChatGPT, and a place on the lists for Claude. When Claude doesn’t search, it’s different. It answered 6 of its 36 questions from memory, and from memory it doesn’t know us. In a pilot run, asked “What is Fifty Five and Five?” without searching, it said it couldn’t identify us. So we only get named if a live search finds the right page. What got us named, and what didn’t? Almost every time we were … --- ### Marking 10,000 partners' homework: how to see what MDF funds produce URL: https://fiftyfiveandfive.com/resources/what-did-the-mdf-funds-buy/ > Vendors know what MDF funds each partner claimed, but rarely what the money produced. How we built Microsoft a tool that scored 10,000+ partners' marketing. Author: Chris Wright Date: 2026-10-13 Quick answer: Score the marketing each funded partner actually published, alongside the claim they filed. Check whether it reads well, sounds right, can be found and works. Then compare funded partners with ones you didn't fund, and with their own work before the money arrived, and check the result against whatever deal data partners will share. If you run a partner programme at a software or cloud vendor, you can see what MDF funds each partner claimed last half. You usually can’t see whether the marketing they bought was any good. This piece is for the people who sign those funds off, and for the partner marketers who spend them. The way to find out is to score what the money produced, partner by partner, and compare. That’s what we built for Microsoft. I worked for Microsoft partners before I started Fifty Five and Five, and we’ve worked with Microsoft since day one. The Partner Benchmarking Tool was the first product we ever built. Until then we only made marketing. If you’re on the partner side, our Microsoft MDF guide covers what the funds are and how to claim them, and why MDF funds often don’t turn into pipeline covers how they get spent. This post is about what the money produced. Why is it so hard to measure what MDF funds produce? The paperwork shows the money was spent. It says very little about the result. Microsoft’s own co-op rules ask partners to keep “proof of execution” documents for 24 months after an activity (Microsoft Learn ). A claim shows that a webinar ran or an ad went out. It doesn’t show whether the landing page behind it worked. The vendor is also a long way from the sale. Brett Kahnke, a principal analyst at Forrester, wrote that “being one (or more) steps removed from the deal, channel organizations face challenges acquiring the level of detailed data necessary to fully understand the impact that channel marketing provides” (Forrester ). The partner has the customer and the deal data. The vendor gets a claim form. Then there’s the size of it. A big vendor funds partners in dozens of countries, from multinationals to ten-person firms. Reading every partner’s website and social feeds by hand, then doing it again next quarter, isn’t realistic. On one of our trips to Seattle, Microsoft’s partner marketing team told us they couldn’t put a number on what their funds bought. They wanted to know if funding a partner got them anything back, and they had no way to check. What did we build for Microsoft? A web platform that reads partners’ marketing and scores it. We called it the Partner Benchmarking Tool, and the project ran for eight months. It ran more than 40 tests across all kinds of content, from spelling and tone to SEO signals and bugs in page code. It covered thousands of companies across tens of thousands of channels, websites, blogs and social accounts, using machine learning and AI to score them as it went. Microsoft teams in the UK, the US and Canada piloted it. It was built to answer one question: did giving marketing funds to this company get a better result for Microsoft? The tool covered more than 10,000 partners, from huge multinationals to startups, and analysed $10m of marketing development funds. Our case study puts it this way: for the first … --- ### Lead magnets got interesting again (thanks, AI) URL: https://fiftyfiveandfive.com/resources/lead-magnets-got-interesting-again/ > The best lead magnet gives buyers an answer about their own business while they're still on the page. Two interactive builds, for Microsoft Surface and Nintex, show how, and AI coding tools have made them much quicker to build. Author: Chris Wright Date: 2026-10-08 Quick answer: A tool that works something out for them, such as a maturity score or a costed option, so they leave with a result they can act on rather than a file to read. If you run demand gen at a B2B company and your main lead magnet is still a PDF behind a form, this piece is for you. It’s about what to give buyers instead, and why that has got much easier to build. Your buyers can get general advice out of ChatGPT in seconds now, without handing over an email address. So an eBook of general advice is a harder trade than it used to be. What still earns someone’s details is an answer about their own business, while they’re still on the page. We’ve built two lead magnets that work that way: an interactive microsite for Microsoft Surface, and a process maturity calculator for Nintex. The Surface one has the numbers. People stayed for over 8 minutes on average, against an industry norm of 2 to 3. 60% came back, and it produced four times the leads of their traditional eBook (case study ). Why is gated content struggling? I’ll own this one first. We’ve made a lot of gated eBooks over the years, for Nintex among others, and some were very good. But the deal gated content offers the buyer is a poor one. You hand over your email, you get a file, and the file sits in your downloads folder next to the last three. The marketer gets a name in the CRM with no idea whether that person cares. The buyer gets homework. It’s worse for sales. A download tells them someone was curious for about a minute. It doesn’t tell them what the buyer’s problem is, how big it is, or whether they’re anywhere near doing something about it. So the follow-up call opens with “I saw you downloaded our eBook”, and everyone on that call knows how it goes. The fix is changing what the buyer gets back for their details, so both sides leave the exchange knowing more than they did. What did the Surface interactive microsite do differently? Microsoft wanted a hero asset for the Surface range that drove up engagement with prospective buyers. It had to sit alongside the Surface device pages, double as a lead generation tool, and get people through to the costs page and the business cases. So we built something that was, in our words at the time, “not quite a web page, not quite an eBook”. The writers and designers spent more time together than they would on a normal asset. The writers picked the copy that would work best visually, and the design team built interactive breakouts around it, so the reader clicked into the parts they cared about rather than scrolling past everything else. It worked. Average sessions ran past 8 minutes, and 60% of people came back to it. The costs section, the one Microsoft most wanted people to reach, was one of the best performers on the page. It got there through a structured journey and high-contrast colour, and it ended up doing two jobs, as a sales tool and as a showcase for new leads. This site really raised the bar in terms of the quality of content we have taken to Partners. The copy is beautifully written, and the interactive elements … --- ### Your 2027 budget is being set now. Don't ask for AI money, name what it replaces URL: https://fiftyfiveandfive.com/resources/ai-budget-2027/ > The easiest AI budget to win is one you already have. Find a line you pay for and don't love, cost the replacement honestly, and answer finance's questions before they ask. Author: Chris Wright Date: 2026-10-06 Quick answer: Move money rather than asking for more. Pick one line you already pay for, such as a per-seat tool, a service doing repeat work or a manual job, and cost its AI replacement in full, including setup and the person who checks the output. Then ask to test it before the renewal date. Somewhere in your inbox there’s a 2027 planning spreadsheet with a deadline on it. If you want AI in next year’s plan, don’t add a new line called AI. Find something you already pay for that isn’t working, and make the case for replacing it with something that does. A finance director who’d never sign off an AI budget from nothing will often happily move money from a tool, a service or a manual process that everyone already moans about. If your company runs a calendar year, that conversation is happening now. The plans for 2027 get argued over in October and November, and by January the numbers are mostly set. So the time to write the case is this month. I don’t have to ask anyone for budget, because I own the company. But I do have to convince my own team, and I know what some of them think when I walk in on a Monday with a new idea. Another one from Chris. The ideas that land are never “let’s try some AI”. They’re “this thing we all hate doing, we could stop doing it”. Why does asking for an AI budget so often fail? Because a new line called AI is the easiest thing in any plan to cut. It has no history and no owner who’ll fight for it, and nobody can hold it to a number. The person asking usually can’t say what it will stop costing, either, which is the first thing finance will ask. When the plan has to come down by 10%, the experimental line goes first. It also asks the wrong question. “Can we have money for AI?” makes the finance director judge a technology they’ve mostly read about in the papers. “Can we stop paying for this and get a better result for less?” asks them to judge a trade, and trades are what finance directors are good at. So the job, before you write a word of the business case, is to find the trade. What should you look for in the budget you already have? Look for the lines that are expensive, inconsistent or quietly growing, and that nobody would defend in a meeting. In marketing and sales budgets they usually fall into four groups: Per-seat software that a handful of people use. Approval tools, survey tools, dashboards built for one purpose. The renewal creeps up every year and nobody checks who actually logged in. An agency or freelancer doing repeatable work by hand. The same report, the same posts, the same list build, month after month. Data your team no longer trusts. If people double-check a provider’s records before they use them, you’re paying twice. A manual job someone does every Friday. Nobody has ever costed it, because it’s “just part of the role”. Each of those has two things a new AI line doesn’t. A number already in the budget, and people who’d be glad to see it go. The case moves money to something that works better rather than adding any, and the person who used to do the Friday job gets their Friday back. When you’ve found the line, write down … --- ### Is SaaS dead? The rise of throwaway software, and what it means for marketing teams URL: https://fiftyfiveandfive.com/resources/is-saas-dead/ > 20 tiny tools in a week, built by AI for one job each and then thrown away. What that means for the software a marketing team pays for. Author: Chris Wright Date: 2026-10-01 Quick answer: For some jobs, yes. A throwaway tool is right when the job is one thing, made by a few people, over a few days, on data that wouldn't hurt anyone if it broke. Your CRM, marketing automation and data warehouse stay. Is SaaS dead? No. But the thin end of it is being eaten, and I’m doing some of the eating. In the last week of September my various AI tools (mainly Claude Code, we are an Anthropic partner here at Fifty Five and Five) built me 20 tiny web tools that each did one job, usually needing one or two decisions, and most were discarded within a day or two. Nobody logged in and nobody paid for a seat or licence fee. For a marketing or sales team at a large company, the sort of client we work with, there is something here to think about. Increasingly in AI circles people talk about AI and bespoke SaaS, where every company builds its own version of the tools it rents today. Maybe. I can see that. What I see practically in my own usage right now is a bit smaller and maybe a bit weirder. I build software on a Tuesday, use it on a Wednesday and never open it again. I’ve started calling it throwaway SaaS. This piece is what I’ve learned from doing a lot of it, which jobs it suits, and where it’s a genuinely bad idea. What is throwaway software? Throwaway software is a small tool, usually a single web page, that AI builds for one job and that you delete or forget once the job is done. It has no users beyond the handful of people making that one decision, normally just me, and no roadmap nor maintenance. Its whole life might be an afternoon. My first proper example came back in July. And it wasn’t even a work thing. I got Claude Code to scrape a load of holiday resorts against my criteria, then build a booking.com style page to compare them, with the filters I wanted and a flight-time cone on the map. I did write about it at the time on LinkedIn: “It was a single HTML file, local on my machine, took me 30mins in total, never looked at the code once.” My wife and I used it for 25 minutes to pick where to go. Then I never thought about it again. Small, personal software isn’t new. In 2020 Robin Sloan wrote about building a messaging app for his family: “It is ruthlessly simple; we love it; no one else will ever use it” (Robin Sloan ). He called it a home-cooked meal, and it had four daily active users with “zero churn”. His app was built to be kept for years. My holiday page lived for one evening, and that shorter lifespan is the really interesting thing here. Anish Acharya at a16z put a name on it last year, “Disposable Software”: “small, personal apps and tools that only make sense for you or maybe a couple of friends”. His line on why it’s happening is the one I’d underline: “Software creation used to be constrained by ROI. Now it’s constrained only by imagination” (a16z ). What did we actually build in one week? I built 20 single-use pages between 24 and 30 September. Most were built, used and archived within a day or two. That felt like a week things changed. A few of the pages from that week: Approval pages. A lot were for content. So … --- ### How fast is AI advancing? (October 2026 update) URL: https://fiftyfiveandfive.com/resources/how-fast-is-ai-advancing/ > How fast is AI advancing? October 2026 update: every ChatGPT and Claude model since 2022, checked against Epoch AI and METR, and what it means for your team. Author: Chris Wright Date: 2026-09-25 Quick answer: As of October 2026, AI is still improving by about a GPT-4-sized jump a year: the top score on Epoch AI's Capabilities Index rose 17 points in 12 months, to 167 (Claude Opus 5.5). The length of task AI can finish on its own doubles roughly every four months. Last updated 15 October 2026. What changed: Epoch AI has now scored Claude Opus 5.5 itself, at 167 on its index, and it’s still the top score. Claude Sonnet 5.5 and GPT-6.1 Sol came out since and didn’t beat it. METR has stopped updating its task-length results, and its current figure for GPT-4 is about 4 minutes, not 5. People expect a new AI model almost every month now. GPT-5.4, then 5.5, then 5.6. Opus 4.6, 4.7, 4.8. Most launches barely get a reaction any more, even from me, and I’m the sort of person who reads the release notes. But under every AI decision a marketing team makes, there are questions nobody has a straight answer to yet. Do we commit to a tool now, or wait for the next model? Is it expensive? Do we pick one model or keep switching? Is this stuff actually getting smarter, or are there just more launches? Are things really moving that fast? A month ago I’d have told you progress had flattened. So I went digging in the data to see how fast things are really moving. Is AI improvement speeding up? Have we hit a wall? And what does it mean for marketing teams trying to build with it today? I sat down with Claude* and went through every model ChatGPT and Claude have put in front of us since ChatGPT launched in November 2022. All 57 of them. Dated, checked against the release notes, and scored against two independent measures: Epoch AI’s Capabilities Index and METR’s task-length results . *Claude did most of it. A lot. Definitely. My PC crashed halfway through, which felt about right. Why does it feel like AI has stalled? In July I had breakfast in the garden while Opus finished the first version of a client tool. Then I asked Fable to check it. It ran for 45 minutes. 76 separate agents. 7 million tokens. It found the bugs, wrote a seven-step plan to fix them, and when I said I had to cycle in, it moved itself to the cloud, carried on, and wished me a nice ride in. I put the Stones on and got on the bike. That should feel like a revolution. It didn’t, quite. Because a month earlier a big AI-assisted data job had come back with over half its links wrong, and I found myself typing “just thought we were beyond this kind of rookie AI error”. (I did not spell it that well.) That’s the whole story in two mornings. Every launch is a bit better. None of them is a “wow” on its own. In the last 12 months the two apps launched 15 new top models between them, up from 6 two years before, and each new record added about 2.3 points on average. (Quick one on the points, because they matter for the rest of this. Epoch’s index squashes more than 50 benchmarks into one number. It allows for how hard each test is, so it doesn’t stop working when the easy ones max out, and it isn’t a percentage, so there’s no 100 to hit. It’s pinned so Claude 3.5 Sonnet, from June 2024, scores 130 and GPT-5, from August 2025, scores 150. GPT-4 is about 126. Today’s … --- ### AI agents in partner marketing: the work no human was going to do URL: https://fiftyfiveandfive.com/resources/ai-agents-in-partner-marketing-the-work-no-human-was-going-to-do/ > AI agents in partner marketing: why partner programmes were always understaffed, the agentic workflows vendors already run, and what teams can now build. Author: Chris Wright Date: 2026-05-19 Quick answer: Partner marketing has always been structurally understaffed: 500,000+ Microsoft partners, 7,500 more joining monthly, no human team to cover them. AI agents aren't replacing partner marketing managers; they're filling the work no team was ever going to do. Brand audits at scale, content matching across thousands of partners, intent signals on partners your PRM doesn't surface. Partner marketing’s maths problem, and what AI agents change AI agents in partner marketing isn’t a future trend. It’s already doing work humans were never going to get to. Microsoft has more than 500,000 partners worldwide, with another 7,500 joining every month, per Microsoft . 75% of world trade flows through indirect channels, Forrester reports . 34% of new marketing roles are now in ecosystem, partner, or events functions, according to MKT1 . And The Channel Company finds 80% of new partners leave vendor programmes without ever selling anything. Partner marketing has always been structurally understaffed. The maths has never worked. What’s changed is that AI agents are doing work no human team was ever going to do. Not replacing partner marketing managers. Filling work that quietly never happened. That’s the through-line of an AI marketing agency working at this scale , and it’s the substance of what follows. Three things to look at: why scaling partner programmes was never a tooling problem to begin with, what AI agents are already doing in production at the biggest software vendors on the planet, and what partner marketing teams can now build for themselves. Why scaling partner programmes was never a tooling problem Scaling partner programmes was never a tooling problem. It’s a coverage problem, and tools have existed for years without changing the math. Any large partner programme runs the same way. Top partners (Accenture, TCS, Infosys) get dedicated everything: a named partner manager, custom co-marketing, the full vendor playbook. They’re fine. The middle, your top 100 or top 1,000, gets a partner manager who’s stretched too thin to be strategic. The long tail gets nobody. They get carbon-copy enablement, a portal login, and a quarterly email. That long tail is still 20 to 40% of channel revenue, according to MarketSource . And the existing tooling doesn’t fix it. Portal adoption sits at 17% industry-wide, per Channel Futures . Through-channel marketing automation, partner relationship management, marketing development funds: these categories have been around for decades. They are not new. The work that doesn’t get done doesn’t get done because there aren’t enough humans to do it. The tools were never the bottleneck. The numbers underneath get worse the closer you look. 66% of partners expect AI to improve their marketing effectiveness in the next 18 months. Fewer than 10% of vendors currently provide AI-powered partner activation tools, The Channel Company found. That gap, between what partners want and what vendors can deliver, is the coverage problem in numbers. The work is worth doing. There’s just nobody to do it. What changed isn’t that we got better tools. What changed is that agents can do work the human team was never going to get to. Not 10x productivity, which is the consultant version of this story. Coverage that didn’t exist … --- ### How I use Claude Code as a designer URL: https://fiftyfiveandfive.com/resources/how-i-use-claude-code-as-a-designer/ > How I use Claude Code as a designer: prototyping UI from a prompt, building design systems with Figma MCP, shipping production work, and when not to use it. Author: Fergus Hannant Date: 2026-05-08 Quick answer: By reaching for it at three points in the workflow: prototyping a UI from one prompt instead of starting in Figma, building design systems with Claude Code and Figma MCP, and shipping production work end-to-end. Some of design still belongs in Figma and Miro. A lot of what I used to do there now starts in Claude Code. For most of my career as a designer, my workflow started in Figma. Seven years of work across legaltech, healthtech, insuretech, and B2B SaaS, and Figma was where every project began. Over the last year, that has quietly changed. I use Claude Code for design by reaching for it at three points in my workflow. Prototyping a UI from a single prompt instead of starting in Figma. Building design systems with Claude Code and Figma MCP. And shipping production work, like the Fifty Five and Five website you’re reading this on. Some of design still belongs in Figma and Miro. A lot of what I used to do there now starts in Claude Code. The shift isn’t unique to me. According to UX Tools’ State of Prototyping Spring 2026 survey of 1,478 designers, 38.4% now use Claude Code weekly, and five of the top ten weekly design tools are now AI (UX Tools ). A year ago, none of those five were on the list. This post is one part of how an AI marketing agency uses Claude Code , covering the design side. My colleague Owen has covered the marketing side, and Chris is writing the partner marketing one. Quick note in case you haven’t come across Claude Code before. It’s a terminal-based AI coding tool from Anthropic. It runs locally, reads and writes files, and works alongside developer workflows. Designers haven’t traditionally been the audience. I’m writing this because I’ve found a clear set of design tasks where Claude Code for designers earns its keep, and a clear set where it doesn’t. From Figma starts to Claude Code UI: my new prototyping workflow I used to start all of my work in Figma. I still do a lot of the time. But I have noticed myself reaching for Claude Code to plan out an idea and build a prototype off the bat. More often, I find myself sketching out ideas in a notebook first, and using those to feed into the work I do in either Figma or Claude Code. The decision point isn’t “which tool do I open?” any more. It’s “which tool fits this sketch?” A lot of the design work I used to start in Figma now starts in Claude Code. That isn’t because Claude Code replaces Figma. It’s because the design process is different now, and Figma is still the right tool for some of it. The Claude Code UI prototype that changed how I think about this came from a recent partnerships project at Fifty Five and Five. We wanted a flexible colour palette so partner brand colours could really shine. A colleague was worried about how this would work in practice, with so many possible partner colour combinations. I one-prompted a solution in Claude Code that previewed what the branding would look like across a full spectrum of colours. It took less than ten minutes, and it helped me show how the proposed branding could actually work in real life. Communicating ideas instantly is a design problem, not a coding problem. The prototype is for stakeholders, not engineering. Figma is great for a … --- ### How I use Claude Code as a marketer URL: https://fiftyfiveandfive.com/resources/how-i-use-claude-code-as-a-marketer/ > How I use Claude Code as a marketer: the skills I rely on for ABM research, blog writing and social listening, and how to build Claude Code skills of your own. Author: Owen Steer Date: 2026-05-05 Quick answer: Turn all of your experience and the ways that you work into Claude Code skills. Do the work, write down the process, and then turn it into a skill. Free up your time to focus on strategy and creative, and let the skills you create remove the busywork from your day. More and more people are waking up to the potential of AI, and to how fast it’s advancing . We’ve moved from a period of whether to adopt, into a period of how to adopt. You can clearly see this when you look at search volume for Claude Code, Anthropic’s terminal-based AI coding tool, which grew by roughly 20x in 12 months (DataForSEO via Google Ads ). Claude Code itself passed $2.5 billion in run-rate revenue in early 2026 (Yahoo Finance, citing Anthropic ). Many marketers like me (and you?) are part of that growth. This blog is my marketing perspective as part of a wider set of content that shows how Fifty Five and Five as an AI marketing agency uses Claude products across multiple roles. I use Claude Code as a marketer by distilling what I already know in my brain into skills and context files. The skills run the deep work (research, drafting, and monitoring) at speeds I could never match manually. I focus on the strategic decisions and creative judgement that still demand a human. A quick aside before we go further. If you’re new to any of this, three definitions will save you time. What is Claude Code? Anthropic’s AI coding assistant. It runs in a terminal on your computer and can read and write files, run commands, call APIs, and use other tools on your behalf. Originally built for developers, but it works just as well for anyone whose work can be written down as a process. What’s a terminal? The black-and-green text window you’ve seen in films when someone’s “hacking”. Less dramatic in real life: it’s just a way of typing commands to your computer instead of clicking. Claude Code lives in there, but you talk to it in plain English, not code. What’s a skill? A plain text file describing how to do a task, step by step. Claude Code reads it and runs the steps. Pair it with context files (the background knowledge about your offer, brand, and clients) and the skill works the way you would. Think of a recipe: the steps are the process, and the surrounding notes (ingredients, ratios, tips) are the knowledge. Together they let anyone, or any AI, recreate the result. There’s a more user-friendly alternative in Claude Cowork, though I’ve found it lagging behind on the more technical work that revolves around APIs. The Claude Code skills I actually use as a marketer This may sound strange coming from me, a marketer, but the truth is a large part of my career work (things like keyword research, account research, content writing, campaign performance, and so on) can ultimately be boiled down to repeatable steps. With the guidance and wisdom of my experience, those steps can be turned into knowledge files readable and executable by an AI tool. Manual processes can be automated through APIs. Keyword research, for example. I used to log into Ahrefs to find keywords with promising data. Then take those keywords into a content brief. Then a draft. Then a CMS. Then an SEO … --- ### AI agent orchestration: who owns the plan when every tool has its own agent? URL: https://fiftyfiveandfive.com/resources/ai-agent-orchestration-who-owns-the-plan/ > Cowork, Breeze, Agentforce and Adobe Coworkers all shipped in 60 days. None of them own the plan. Here's what AI agent orchestration actually looks like in 2026. Author: Chris Wright Date: 2026-04-21 Quick answer: AI agent orchestration is the coordination layer that holds a persistent plan and delegates work across task-shaped agents like Copilot Cowork, HubSpot Breeze and Salesforce Agentforce. Without it, a marketing stack with 10 agents has no one in charge. I’ll be honest, I didn’t set out to write about orchestration. I set out to figure out whether our own product had a category. If you’re a marketing leader in 2026 you already know the shape of the problem. HubSpot has Breeze. Salesforce has Agentforce. Adobe rebranded Experience Cloud as CX Enterprise and put Coworkers at the centre of it. Microsoft and Anthropic launched Copilot Cowork together. And on 1 May, Microsoft Agent 365 goes live as the control plane for all of them. Your marketing stack is about to contain ten or more agents from five or more vendors, each good at its own slice of work, each shipping on its own release cadence, none of them talking to the others. AI agent orchestration is the coordination layer that sits above all of that. It holds a persistent plan. It delegates to task-shaped agents. It follows up when things slip. Without it, you have a room full of smart helpers and no one in charge. I know the feeling. On a busy day I run 20 AI agents in separate terminal windows , and most of the coordinating still happens in my head. That matters more than the agents themselves. A recent Salesforce report found that large enterprises now run an average of 957 applications, but only 27% are actually integrated. Roughly half of AI agents already live in silos, disconnected from the broader marketing stack (ConvertMate analysis of Salesforce 2026 Connectivity Report ). The agents are the easy part. The plan ownership is the hard part. I’ve been building Fifty Five and Five for eleven years, and for the last two of them we’ve been quietly building an orchestrator. I’ll say upfront: this post is about the category, not a pitch. If the category didn’t exist, we wouldn’t have a product in it. What is Copilot Cowork? A plain-English explainer Copilot Cowork is the most interesting launch of the last 60 days because it proves the pattern. So let me define it properly before anything else. Copilot Cowork in one box What it is. A multi-step agent inside Microsoft 365 that turns a single instruction into a chain of actions across emails, files, meetings, and research. Tell it to prep for a customer meeting and it assembles the deck, pulls the financials, drafts the emails, and books the prep time. Who built it. Microsoft, with Anthropic. Claude powers the reasoning. Anthropic also provided the agentic harness, the scaffolding that lets the model invoke tools safely. It is the same harness Anthropic ships in its standalone Claude Cowork product, wrapped in Microsoft’s enterprise data plane. Who it is for. Microsoft explicitly targets “busy professionals”, not a specific role. Cowork is horizontal by design. What it does not do. Hold a persistent plan between sessions. Proactively check on work. Operate outside Microsoft’s own data plane, because Cowork is scoped to Microsoft 365. How to get it. Available in the Microsoft Frontier programme now. Generally available on 1 … --- ### AI search visibility tool: how to check if AI is recommending your business URL: https://fiftyfiveandfive.com/resources/ai-search-visibility-tool-how-to-check-if-ai-is-recommending-your-business/ > Is AI recommending your business? How an AI search visibility tool checks ChatGPT, Claude, Perplexity, Gemini and Grok, and how to read what it finds. Author: Owen Steer Date: 2026-04-15 Quick answer: Run one of the many AI search visibility tools out there (or use ours!), which essentially send the questions you think customers will be asking to LLMs a bunch of times, and catalogue who appears in the responses. Don't be put off from trying, it's far simpler than AI search visibility vendors make it sound. An AI search visibility tool checks whether your business appears in the answers AI platforms generate for your customers’ questions. It works by sending prompts to ChatGPT, Claude etc., multiple times each, and cataloguing who gets mentioned. The good ones go further. If you put the hard work in to understand how your audience queries AI, and learn what words AI engines use when they search (something called query fan-out, more on this later), you can start to build a genuinely relevant picture of where you appear. Most businesses have never measured their AI visibility, and it shows. 88% are invisible in AI search (Omni Eclipse , 1,700 businesses tested across 32 industries). I spend a lot of my time at Fifty Five and Five building AI tools that marketers can use, and one of the things I keep coming back to is this: you cannot improve what you cannot measure. We built our own AI search visibility tool, the GEO conversation opener , so we could baseline a business’s AI visibility before any GEO changes are made. This blog walks you through how it works. Once you see it, you can pick any tool and use it properly, or do this yourself. The AI visibility gap: why ranking on Google doesn’t mean AI recommends you Ranking on Google does not mean AI recommends you. 77% of businesses sitting on page 1 of Google are still invisible in ChatGPT (Omni Eclipse ). Your Google position is one signal among many that AI engines use to decide which sources to trust. It is not the whole picture, and treating it as one is why so many businesses are caught out. Here’s the thing: historically, if I was asked to improve organic visibility, I would focus on keyword volume and keyword strength for Google ranking. High-volume keyword, low competition, build the right page, chase the ranking. That playbook still works. It is not dead. But it is no longer the whole job. AI search uses Google ranking as one of many factors, and what is far more interesting is query fan-out. When someone asks an AI a question, the AI does not just search for that question. It breaks it into smaller sub-queries and searches for each one separately. Some of those sub-queries target keywords with low traditional search volume. A volume-first keyword audit would skip straight past them. But AI uses them to contextualise what your customer is really asking, which means they carry value even when the volume looks unexciting. The sweet spot is when you find query fan-out terms that also have meaningful search volume. I call these synergy keywords. They rank on Google (so they still earn traditional traffic), and they appear in the sub-queries AI engines fire (so they show up in AI answers too). Every post in this AI search optimisation series was built around synergy keywords because they work in both channels. To really understand query fan-out, to the point of learning that you can test and scrape the exact terms AI search uses, read the query fan-out post in this series. … --- ### What happens when marketing teams start building their own AI-powered tools URL: https://fiftyfiveandfive.com/resources/what-happens-when-marketing-teams-start-building-their-own-ai-powered-tools/ > What happens when marketing teams build their own AI powered marketing tools: working prototypes in a day, demos that sell, and when to build rather than buy. Author: Barnaby Ellis Date: 2026-04-01 Quick answer: Far more than content. Working prototypes, games, event activations, sales tools. I built a playable game for a client pitch in a day. The gap between having an idea and showing someone what you mean has collapsed. But the output only matters when the thinking behind it is sound. Most marketing teams use AI to write things. Blog posts, social captions, email subject lines. 73% of marketing teams now use generative AI (Gartner ), but the vast majority still treat it as a content tool. The teams pulling ahead are the ones that have moved past writing and started building their own AI marketing tools . Working prototypes. Interactive demos. Event activations. Personalised sales materials. Internal platforms. Things that used to need budgets, timelines, and specialist teams that most B2B organisations simply didn’t have. I’m Barnaby Ellis , Head of Strategy at Fifty Five and Five. I’ve spent nearly 30 years in digital agencies, and the hardest part has always been the same: turning an idea into something someone can actually see and react to. You can describe a concept brilliantly in a deck, and half the room still won’t get it until they can pick it up and play with it. AI hasn’t solved the thinking. But it has removed the resource constraints between a sketch on a whiteboard and a working thing in someone’s hands. That changes everything about how we pitch, how we prove ideas, and how quickly we can move from “what if” to “here, try this.” From idea to demo in a day: AI prototyping for marketing teams A few weeks ago, I had coffee with a contact at an existing client. They were new to the company and we hadn’t met before. Through a natural conversation about what we do, I found out they had an event coming up: the Retail Tech Show in London. Off the back of our TCS London Marathon activation , which won a Bronze Drum Award, I offered to go away and think about how we might support with an activation for their stand. The first step was research. I spent half a day looking into the retail industry and the common challenges it faces. “Returns” stood out immediately. Whether it’s people ordering multiple sizes and sending one back, wearing something once and returning it, or the environmental cost of processing and shipping returns, it’s a problem everybody can relate to. You don’t need to work in retail to understand the frustration. The game concept came from an old Nintendo handheld device from the late 80s that I remember playing. I wanted to create something that challenged the player to use both hands, each controlling a different part of the game. I sketched the idea on a whiteboard, drew a detailed diagram with labels, and described each label in a brief. Then I went straight into Claude Code. I gave it the diagram and the descriptions, and within a day I had a playable game. But I didn’t send it to the client. Not yet. I took it to Owen Steer first. Owen is a keen gamer, and he brought a pure gameplay perspective: how do you win, how do you lose, do you have lives, how do you score points, does it speed up, do you get rewarded at certain levels? All things that I’d made some assumptions about, but Owen brought … --- ### Channel partner marketing strategy: unlock revenue beyond top performers URL: https://fiftyfiveandfive.com/resources/channel-partner-marketing-strategy/ > A channel partner marketing strategy built on momentum signals, not last quarter's sales: how to find the partners generating revenue you can't see yet. Author: Chris Wright Date: 2026-03-31 Quick answer: Stop fixating on partners who sold last quarter and start looking for momentum signals: hiring surges, marketplace activity, co-sell engagement. We built the Partner Benchmarking Tool for Microsoft because they couldn't answer whether giving marketing funds to a partner actually improved outcomes. 20-30% of influenced revenue comes from partners your PRM labels 'inactive.' Channel sales was built for transactions, not ecosystems You get more revenue from your channel partners by looking beyond who sold something last quarter. Most channel partner marketing strategies only measure transactions. The real growth sits in partners showing momentum signals: hiring patterns, marketplace activity, and co-sell engagement. Signals your dashboards never capture. An ecosystem intelligence approach surfaces these partners before competitors spot them. This is one of the four challenges we explored in our guide to the secrets of channel partner revenue . I’ve spent over a decade inside Microsoft partner marketing ecosystems across six countries. Sat in enough quarterly business reviews to know that everyone nods along to the 80/20 rule and nobody does anything about the 80%. It’s background noise at this point. But the question nobody asks is more interesting: what about the next 20%? Not the underperformers. The partners who aren’t yet producing but are showing every sign they’re about to. That’s where your next wave of channel revenue sits. Not in squeezing more from partners who are already performing, but in spotting the ones gathering momentum before anyone else does. The shift isn’t a new enablement programme or a better partner portal. It’s a different way of seeing your partner ecosystem. Channel sales was built for transactions. Ecosystem intelligence is built for influence. One looks backwards at what happened. The other looks forwards at what’s about to happen. Channel sales was designed for a simpler world. Resellers, distributors, value-added resellers (VARs), rebate tiers, quarterly business reviews (QBRs). The model worked because the value chain was linear: vendor makes product, partner sells product, everyone gets paid. That world is gone. Revenue now moves through a much broader partner ecosystem. Independent software vendors (ISVs) building on your platform. Managed service providers (MSPs) wrapping your technology into managed services. Co-sell partners influencing deals they never close. Specialist consultancies recommending your solution in boardrooms you’ll never enter. The partner landscape has expanded far beyond the traditional reseller model, but the systems managing it haven’t kept pace. Most partner relationship management (PRM) platforms track who transacted. They were designed for order processing, not influence tracking. A partner can drive six figures in influenced pipeline and show up in your PRM as “inactive” because they never submitted a deal registration. That’s not a reporting glitch. It’s a structural blind spot. 95% of Microsoft’s commercial revenue is tied to partners, and 62% of total partner revenue now comes from services, not product resale (Microsoft/IDC ). Partners generate between $8.45 and $10.93 for every $1 of Microsoft revenue. That’s an economy, not a sales channel. And most vendor … --- ### Query fan-out: how to find out what AI search engines actually look for URL: https://fiftyfiveandfive.com/resources/query-fan-out-how-to-find-out-what-ai-search-engines-actually-look-for/ > Query fan out shows what AI search engines actually search for. How to pull the sub-queries from Gemini and OpenAI, sort them and plan content around them. Author: Owen Steer Date: 2026-03-31 Quick answer: Query fan-out analysis extracts the actual sub-queries AI engines fire when answering a question: Google AI Mode generates 8-12 per prompt, ChatGPT fires 4-20. You classify these by strength, then structure your content around them. Pages with strong fan-out coverage are 161% more likely to be cited by AI engines. Query fan-out is how you find out what AI search engines actually look for. When someone asks ChatGPT, Perplexity, or Google AI Mode a question, the AI doesn’t just match keywords against a database. It generates multiple sub-queries, searches for each one separately, then synthesises the results into a single answer. Query fan-out analysis lets you extract those sub-queries so you can build content that matches what AI actually searches for, not just what humans type into Google. Google AI Mode fires 8-12 sub-queries for a standard prompt. ChatGPT generates 4-20 depending on complexity. Each sub-query is a separate retrieval path, and your content either appears in those paths or it doesn’t. If you’re only optimising for the original question, you’re visible to one retrieval path out of a dozen. That’s why pages with strong fan-out coverage are significantly more likely to be cited by AI engines. At Fifty Five and Five , we use query fan-out analysis to plan and structure every piece of content we produce, including the posts in our AI search optimisation series. This page explains the methodology: what it is, how to extract the sub-queries using real APIs, and how to turn that data into content structure and editorial decisions. This isn’t theory. It’s the process I run on every piece of content we publish. How semantic SEO connects to what AI engines actually search for Semantic SEO is the reason query fan-out data looks the way it does. Traditional SEO targets keywords: specific search terms with specific volumes. Semantic SEO targets meaning: entities, relationships between concepts, and topical depth. When you run query fan-out analysis, the sub-queries AI engines generate map to semantic components, not keyword variations. Take a real example. When I ran QFO on the question “How to build content that AI search engines actually cite?”, the sub-queries Gemini fired included “E-E-A-T and AI content ranking”, “structured data for AI search”, “semantic SEO for AI”, and “how to improve brand visibility for AI models.” Those aren’t keyword variations of the original question. They’re the semantic components AI needs to answer it: expertise signals, technical infrastructure, content structure, and offsite presence. Each one is a concept the AI researches independently. AI engines process meaning through entity relationships, not keyword matching. When Gemini decomposes a question, it identifies the entities involved (E-E-A-T, schema markup, AI Overviews), the attributes it needs to evaluate (how they work, why they matter, what the data shows), and the comparisons it needs to make (SEO vs GEO, old approach vs new). The sub-queries it fires reflect that decomposition directly. A question about “AI content marketing” doesn’t generate sub-queries like “AI content marketing tips” or “AI content marketing … --- ### The secrets to channel partner revenue: according to real partner marketers URL: https://fiftyfiveandfive.com/resources/the-secrets-to-channel-partner-revenue-according-to-real-partner-marketers/ > How to grow channel partner revenue, from real partner marketers: segment by potential, find hidden partner revenue, prove MDF ROI and get content used. Author: Chris Wright Date: 2026-03-31 Quick answer: To increase channel partner revenue, focus on treating partners according to their potential, demonstrating MDF ROI, and providing relevant content that partners will use. Avoid a one-size-fits-all approach to maximise growth. If you want more channel partner revenue, it comes down to four things most programmes get wrong. Treating all partners the same. Ignoring partners with huge growth potential. Failing to prove marketing development funds (MDF) ROI. And producing mountains of content nobody uses. I know this because we’ve spent 11 years working inside the partner marketing ecosystem. Building tools, analysing data, and having the same conversations with channel teams across Microsoft, Google, SAP, and hundreds of partners worldwide. But I don’t want this to be another blog where someone like me tells you what to do. I went and spoke to partner marketers who live this every day. People running programmes at ServiceNow, people advising vendors on channel strategy, people who’ve been doing this for decades. If you’ve got hundreds or thousands of channel partners and you want to get more revenue from them, this blog cuts out the theory. It brings together the lived experience of partner marketers who’ve been in the trenches: what actually moves revenue, what consistently stalls it, and where most vendors waste time. What they told me was remarkably consistent. Four problems kept coming up, and they’re the same four problems we see across every enterprise client we work with. Roughly 80% of partners generate zero revenue. That number should bother you. It bothered me. Why do most channel partners generate zero revenue? It’s rarely because they’re lazy or incapable. It’s because most partner programmes are designed around the top 20% and the other 80% get the scraps: thin onboarding that doesn’t account for their maturity or market, one-size-fits-all enablement that serves nobody well, no co-sell support to help them close their first deal, and portal fatigue from being asked to log into yet another vendor system they didn’t ask for. The partners aren’t failing. The programmes are failing them. And companies have normalised it, treating 80% inactivity as an unavoidable cost of running a channel rather than a structural problem worth fixing. Four out of five new partners fail to generate sales in their first year. That’s a programme design problem, not a partner problem. Four things that decide whether your channel partner marketing strategy grows or flatlines Across every conversation, four problems surfaced again and again. They’re not minor operational headaches. They’re the things that decide whether your channel partner marketing strategy grows revenue or slowly flatlines. Partner marketing needs vary hugely. You want to support each partner’s individual marketing needs, but lack the insights and resources to do it. That’s why channel management fails when every partner gets the same enablement . Hidden revenue in your channel. You know some partners have huge growth potential, but you don’t have the data or insights to find or prioritise them. Here’s … --- ### Turn unused partner enablement into channel revenue URL: https://fiftyfiveandfive.com/resources/turn-unused-partner-enablement-into-channel-revenue-audit-optimise-activate/ > How to audit your partner enablement content, find the small share that drives deals, and push it to partners so unused assets turn into channel revenue. Author: Chris Wright Date: 2026-03-31 Quick answer: Forrester puts unused B2B marketing content at 60-70%, and in partner enablement audits I've run, it's often higher. The fix is an AI-driven audit that maps usage signals against partner performance and finds the 10-15% that correlate with deal velocity. You don't need more content. You need to recover value from what you've already built. I’ll be honest: most partner enablement content never gets used, and the reason is simpler than you’d think. Nobody knows what’s there, what’s working, or what’s worth keeping. The way to work out what to keep and what to cut is straightforward: audit everything you have, correlate it against partner performance data, find the 20% that actually drives revenue, retire the rest, and push the right assets to the right partners at the moment they can convert. You don’t need more content. You need to recover value from what you’ve already built. This is one of the core problems we uncovered in our guide to the secrets of channel partner revenue . I’ve spent over a decade working inside partner ecosystems, mostly across the Microsoft Partner Network, and the pattern is always the same. Vast libraries stuffed with playbooks, certifications, co-brandable decks, training modules, vertical guides, and videos. All built with good intentions. All uploaded neatly. Almost none of it used. Forrester puts the number at 60-70% of B2B marketing content going unused. In partner enablement audits I’ve been involved in, that number is often higher. We’re talking about multi-million-pound content estates generating zero revenue impact. What if 80% of your partner enablement is invisible? Not deleted. Not deprecated. Just buried. Your job isn’t to create more enablement. It’s to make what you already built start earning. And that is possible. That’s the good news. The partner engagement problem: vendors build content, partners never see it Partner engagement fails for the same three reasons in almost every programme I’ve worked with. It’s not that partners are lazy or disinterested. It’s that the content never reaches them in a way they can actually use. The evidence is specific. A cross-sell PDF with zero downloads in 18 months. A beautifully animated training module nobody finished beyond slide six. A localisation kit for a region where no partner ever registered a single deal. Partners don’t have marketing muscle. Most partners, especially in the mid-market, don’t have campaign teams. They don’t have spare designers or digital budgets. When a vendor says “ready-to-use,” a partner hears “more work.” I’ve seen full demand-gen campaign bundles, three emails, landing page copy, ad assets, downloaded twice in a year. Both times by the vendor’s own channel marketing manager. Onboarding gates kill momentum. Over-engineered portals, 12-step certification paths, enablement assets hidden behind irrelevant prerequisites. Your best material is often three clicks too far. I’ve worked with vendors where every year a new team builds a whole new set of portals for their content, on top of existing portals, layering technical and UX debt year on year. Partners give up before they find the good stuff. You’re just another … --- ### Why channel management fails when every partner gets the same enablement URL: https://fiftyfiveandfive.com/resources/why-channel-management-fails-when-every-partner-gets-the-same-enablement/ > Why channel management fails when every partner gets the same enablement, and how reading partner signals helps you tailor support and measure it by revenue. Author: Chris Wright Date: 2026-03-31 Quick answer: 80% of new partners leave vendor programmes without ever selling anything, and it's not because they're lazy. Vendors confuse fairness with sameness and broadcast identical enablement to everyone. Channel management improves when you read partner signals and intervene with precision at the moment it matters, not on a quarterly content calendar. Where channel partner marketing breaks down Channel management fails when vendors confuse fairness with sameness. You improve it by doing the opposite of what most channel teams do: stop broadcasting the same enablement to every partner and start intervening where it actually matters. This is one of the four problems we identified in our guide to growing channel partner revenue . More than 75% of worldwide commerce flows through some kind of indirect channel (Forrester ). And yet 80% of new partners leave vendor programmes without ever selling anything (The Channel Company ). Read that again. Four out of five partners, gone before they’ve sold a thing. That’s not a partner problem. That’s a channel management problem. I’ve been building tools for sales and marketing teams for over a decade. The pattern is always the same. Walk into a channel marketing team and everything looks controlled. Clean enablement calendar. Branded templates hitting the partner portal like clockwork. Marketing development funds (MDF) approved through a neat process. Leadership loves the slides. It looks efficient right up until you measure ROI and realise partner influence, engagement, and sourced pipeline are all suffering. The problem isn’t the partners. It’s the model. Somewhere along the way, channel partner marketing became an administration function. Its job: distribute, archive, repeat. Vendors ship quarterly content kits. They run a webinar and call it enablement. They drop a perfectly formatted campaign-in-a-box into a portal and wait for partners to “fully engage.” It’s partner marketing by broadcast, disguised as strategy. But partners don’t operate in a controlled environment. They operate in chaos: shifting headcount, inconsistent deal velocity, random market pressure, one AE leaving their role and stalling all co-sell momentum. Into that chaos, vendors drop a generic, on-brand asset designed for the lowest common denominator, which means it’s designed for no one. The data confirms what we see in every partner audit. 36% of channel partners disengage from programmes because the rewards aren’t compelling, while 31% leave because the rules are too confusing (Maritz ). These aren’t edge cases. This is more than a third of partners telling vendors: what you’re offering doesn’t connect with what I need right now. The result plays out the same way across every programme I’ve reviewed: High performers skim whatever is available and carry on doing what already works. Mid-tier partners skim portals and hope one day they’ll have bandwidth to “fully engage.” Low-engagement partners stopped checking the portal two quarters ago. That’s not a partner problem. That’s a channel partner marketing problem. What actually kills channel partner engagement Partner disengagement isn’t emotional. Nobody’s offended by the vendor enablement pack. … --- ### Why your Marketing Development Funds aren't generating pipeline URL: https://fiftyfiveandfive.com/resources/why-your-marketing-development-funds-aren-t-generating-pipeline/ > Why marketing development funds often fail to generate pipeline, how MDF allocation rewards persuasion over performance, and how to tie MDF spend to revenue. Author: Chris Wright Date: 2026-03-31 Quick answer: Up to $35 billion in co-op and MDF funds go unclaimed annually in the US because they're managed as grants, not capital. When we built the Partner Benchmarking Tool for Microsoft, we could finally trace every pound of MDF back to pipeline. Partners chosen by conversion signals outperformed those chosen by persuasion four to seven times. MDF funds were designed as fuel, not subsidies Your Marketing Development Funds aren’t generating pipeline because they’re being managed as grants, not capital. Money moves based on who asks first, not who converts best. And without connecting MDF spend to revenue systems, you’re funding activity, not outcomes. This is one of the four problems we found when we asked partner marketers about growing channel partner revenue . That’s the blunt version. The longer answer involves a structural problem that most channel leaders already sense but struggle to put into words. MDF was designed to accelerate partner go-to-market within the Microsoft AI Cloud Partner Program . Fuel for growth. But somewhere along the way, the definition shifted. The compliance framing, “resources provided to partners to support co-marketing activities,” turned MDF into operational support. A subsidy. And subsidies don’t get measured like investments. The numbers tell the story. 60% of MDF goes unused every quarter (Zinfi ). Up to $35B in co-op and MDF funds go unclaimed annually in the US alone (LSA/MediaPost ). These aren’t rounding errors. They’re symptoms of a system that treats marketing capital like birthday money. A channel lead in one of our partner workshops put it perfectly: “We treat MDF like birthday money. Everyone loves getting it. Nobody remembers what they spent it on.” Why does so much MDF go unclaimed? The reasons are consistent across every programme I’ve reviewed. Complex claim processes that require more effort than the campaign itself. Partners who don’t know what’s available because the information is buried in a portal they stopped checking. Smaller partners who lack the marketing resources to execute even if they receive funding: 52% of them rely on part-time or shared marketing staff (The Channel Company ). Unclaimed MDF isn’t just wasted vendor budget. It’s missed revenue on both sides: the partner doesn’t grow, the vendor doesn’t get pipeline. The fix isn’t just making claiming easier (though that helps). It’s connecting MDF to activities partners are already doing, so the funding accelerates motion that’s already happening rather than asking partners to start something from scratch. When MDF behaves like capital, it gets tracked. It gets defended. It demands performance. When it behaves like a grant, it drifts across dozens of partners with no forecast, no expected return, and no connection to pipeline. I’ve seen both versions up close, having spent over a decade working with Microsoft Partner Marketing teams across six countries. One of the clearest examples was a campaign we built for Microsoft partners around the Surface Hub for the academic sector . The MDF investment was £25k for the build, paired with a £7k per-partner activation model. Every partner got a full go-to-market kit: audience personas, messaging … --- ### GEO vs SEO: what's the difference, and do you need both? URL: https://fiftyfiveandfive.com/resources/the-difference-between-seo-and-geo-and-why-you-need-both-in-2026/ > GEO vs SEO: SEO gets your pages ranked in Google, GEO gets them cited by ChatGPT, Perplexity and Google AI Mode. Where they overlap, and how to run both. Author: Owen Steer Date: 2026-03-30 Quick answer: SEO gets your pages ranked in Google. GEO (generative engine optimisation) gets your company named in AI answers from ChatGPT, Perplexity and Google's AI Mode. You need both: GEO builds on SEO, since AI assistants mostly quote pages search engines already find. SEO gets your content ranked. GEO gets it cited. You need both because they solve different problems, and in 2026, solving only one means you’re invisible in the other. SEO (Search Engine Optimisation) makes your content findable in traditional Google search results. GEO (Generative Engine Optimisation) makes your content citable by AI engines like ChatGPT, Perplexity, and Google AI Mode. The overlap between them is significant (structured content, E-E-A-T, technical hygiene), but the gaps are where companies get caught out. Gartner predicted traditional search volume would drop 25% by 2026 (E-Commerce Times ). So far it hasn’t happened: Graphite found no downward trend in visits to Google or to search engines as a whole (Graphite ). What has changed is where answers come from. AI-referred traffic converts at 14.2% compared to Google organic’s 2.8% (Exposure Ninja via Superlines ), so if you only optimise for one channel, you miss the other. I’m Owen Steer , and at Fifty Five and Five I’ve built AI search optimisation systems that cover both SEO and GEO, for B2B technology clients and for our own site. The pattern I keep seeing: companies that treat GEO as a separate initiative from SEO end up doubling their work for half the result. The ones that integrate both into a single content strategy get better outcomes from less effort. This piece covers what each one does, where they overlap, and how to run them together. GEO vs SEO side by side SEO GEO What it’s for Getting your pages ranked and clicked Getting your company named, quoted or linked in AI answers Where it happens Google and Bing results ChatGPT, Perplexity, Claude, Gemini, Google AI Overviews and AI Mode What gets picked The most relevant, trusted page for the search A clear passage that answers the question, with named sources and real numbers How authority shows Backlinks and domain strength Mentions on other sites, named authors, and claims other sources back up How pages are built Comprehensive, well-linked pages Sections that each answer one question and still make sense when quoted alone How you measure it Rankings, impressions and clicks in Search Console How often AI names you when you ask it your buyers’ questions, several times over Both need the same foundations: pages search engines can crawl and index, content worth reading, and clear authorship. That’s why GEO sits on top of SEO rather than replacing it. The last row is where most teams get stuck. Google gives you Search Console, but no AI assistant gives you a report, so you have to ask them. We did this to ourselves: we put 18 questions our buyers ask to ChatGPT and Claude, twice each, and were named in 10 of 72 answers. Almost every mention traced back to a page with specific numbers on it, or to a directory list. The full results are in how to get your company recommended by ChatGPT , and our AI search visibility tool post shows how to run the same check on your own … --- ### How to build an AI content marketing process that earns citations URL: https://fiftyfiveandfive.com/resources/how-to-build-an-ai-content-marketing-process-that-earns-citations/ > AI content marketing that earns citations: author profiles, sections that stand alone when AI quotes them, and a five-stage workflow that checks every post. Author: Owen Steer Date: 2026-03-30 Quick answer: You earn AI citations by building a system, not writing better individual pieces. Three layers: documented author profiles capturing real voice, content structured so every section works standalone when AI extracts it, and a repeatable workflow enforcing E-E-A-T on every post. Content with verifiable facts gets 89% higher selection probability in AI Overviews. You create an AI content marketing process that earns citations by building a system, not by writing better individual pieces. The system has three layers: documented author expertise (captured through author profiles before you write a word), structured content designed so AI engines can extract any section in isolation, and a repeatable workflow that enforces every quality rule on every piece. Without that system, you’re relying on individual writers to remember every AI search optimisation rule every time. That doesn’t scale. 94% of marketers plan to use AI for content creation in 2026 (HubSpot State of Marketing Report ). Nearly everyone is using AI to write. Almost nobody is using it to build the kind of AI content marketing process that actually earns citations. The gap isn’t the writing. It’s the system around the writing: the expertise signals, the content structure, the editorial rigour that AI engines look for when deciding which sources to trust. The pattern I keep seeing across the companies I work with is the same: they invest in AI content tools, produce more content faster, and still don’t get cited. Because the tool was never the bottleneck. The process was. I’m Owen Steer at Fifty Five and Five , and I’ve built that process for B2B technology clients and for our own site. E-E-A-T SEO is the filter AI uses to decide whether to cite you E-E-A-T SEO (Experience, Expertise, Authoritativeness, Trustworthiness) isn’t a quality signal that nudges your rankings up a few positions. For AI citations, it works as a binary filter. Your content either demonstrates documented expertise and gets considered, or it doesn’t and gets skipped entirely. An analysis of 15,847 AI Overview results across 63 industries found that content with verifiable facts and recent citations gets 89% higher selection probability (Wellows ). That’s not a marginal improvement. It’s the difference between being in the running and being invisible. What does E-E-A-T SEO mean in practice for an AI content marketing process? Four things: Documented author expertise: Named authors with real backgrounds, not company bylines or anonymous posts. AI engines evaluate who wrote the content, not just what the content says. Proper attribution: Every claim sourced, every statistic linked to its origin. Unsourced claims get treated as opinion. Real case studies: Specific examples with specific outcomes. Not “a client saw improved results” but named clients, named challenges, and quantified results that AI engines can cross-reference. Verifiable facts: Data that AI engines can cross-reference against other sources. If your content makes claims it can’t verify, it moves on to a source it can. Semantic SEO reinforces all four of these signals. Where traditional keyword SEO targets individual search terms, semantic SEO builds topical authority through entity relationships, topic clusters, and internal … --- ### How to build content that AI search engines actually cite URL: https://fiftyfiveandfive.com/resources/how-to-build-content-that-ai-search-engines-actually-cite/ > AI search optimisation: how to build content AI search engines cite, with answer-first sections, named experts, offsite posts and the technical basics. Author: Owen Steer Date: 2026-03-30 Quick answer: AI search optimisation is about building a system where content answers real customer questions, is grounded in documented expertise, and is structured so every section works as a standalone answer AI can extract. 96% of AI Overview citations come from sources with strong E-E-A-T, and mid-ranked pages with strong E-E-A-T get cited 2.3x more than top-ranked pages without. The key to AI search optimisation isn’t better writing. It’s a better system. Content that AI search engines cite is built through a structured process: organised around real customer questions, grounded in documented expertise, and designed so every section works as a standalone answer an AI engine can extract and reference. The difference between content that gets cited and content that gets ignored isn’t quality alone. It’s architecture. This piece covers the full picture: how to build an AI content marketing process that earns citations , how to earn AI citations through offsite engagement on Reddit, LinkedIn, and Quora , how to run a GEO audit so AI search engines can actually find your content , the difference between SEO and GEO , and how query fan-out reveals what AI engines actually search for . Generative AI traffic to US websites jumped 1,200% between July 2024 and February 2025 (Adobe Analytics ). That’s not a trend you can wait out. That’s a structural shift in how people find and consume information. The content that earns the citation is whichever source AI selects from its options, and AI is selective. If your content doesn’t meet its criteria for structure, expertise, and extractability, it moves on to someone else’s. I’m Owen Steer , and at Fifty Five and Five I’ve been building AI content systems for B2B technology companies. The process covers everything from keyword research through to writing and editorial, and I’ve seen firsthand what separates content that gets cited from content that doesn’t. The pattern is consistent: companies that treat content as a system (with documented expertise, structured sections, and both onsite and offsite layers) earn citations. Companies that just write good blog posts and hope for the best get passed over. GEO optimisation: why ranking isn’t the finish line anymore GEO optimisation (Generative Engine Optimization) is the practice of structuring your content so AI engines cite, reference, or recommend it when answering questions. It builds on SEO. It doesn’t replace it. I cover how SEO and GEO work together in a separate piece. Your technical SEO foundation still matters, and so does what happens after someone clicks, which is what SXO, search experience optimisation , covers. But ranking on page one of Google is no longer enough if AI engines are answering the question before anyone clicks through to your site. The researchers at Princeton and Georgia Tech who coined the term GEO found that applying GEO techniques can boost visibility in generative engine responses by up to 40% (Aggarwal et al., 2023 ). That’s a meaningful shift, and it applies directly to how B2B companies should think about their content investment. How AI overviews decide which sources to cite Google’s AI Overviews don’t just pull from whatever ranks highest. They apply a separate set of filters on top of traditional … --- ### How to earn AI citations on Reddit, LinkedIn, and Quora URL: https://fiftyfiveandfive.com/resources/how-to-earn-ai-citations-through-offsite-engagement-on-reddit-linkedin-and-quora/ > How to earn AI citations on Reddit, LinkedIn and Quora: why brand mentions beat backlinks, what AI cites on each platform, and how your experts keep up. Author: Owen Steer Date: 2026-03-30 Quick answer: You earn AI citations offsite by getting real experts from your business into the conversations AI engines already pull from: Reddit, LinkedIn, Quora, and Medium. Brand mentions now correlate 3x more strongly with AI visibility than backlinks, and brands are 6.5x more likely to be cited through third-party sources than through their own domains. You build an offsite engagement process that earns AI citations by showing up where AI engines actually pull their answers from: Reddit, LinkedIn, Quora, and Medium. Not with corporate marketing posts. With genuine expert responses from real people in your business, posted in conversations your audience is already having. The process has three layers: finding the right conversations through AI social listening, generating genuine responses using author profiles that capture each expert’s real voice, and running a repeatable workflow that makes the whole thing sustainable. Most companies trying to earn AI citations focus entirely on their own website. That’s half the picture. Brand mentions on third-party platforms now correlate 3x more strongly with AI visibility than backlinks do (Ahrefs ). Your website matters. But what people say about you across the rest of the web matters more. If you’re only optimising onsite content (which I covered in my piece on AI search optimisation ) and technical GEO readiness , you’re running one layer of a system that needs at least two. Owen Steer here. At Fifty Five and Five I spend most of my time building the systems that turn expert knowledge into AI-visible content. For a tax compliance software client, that meant designing a process that identifies relevant online conversations, generates expert responses using author profiles, and evolved from a managed service into a tool their subject matter experts log into and use. This piece covers the research behind why offsite engagement drives AI citations, what actually works on Reddit and LinkedIn, and how to make it scalable without losing the authenticity that makes it work in the first place. Brand mentions are the new backlinks for AI search engines Brand mentions have replaced backlinks as the primary signal AI search engines use when deciding which sources to cite. The Ahrefs study of 75,000 brands puts specific numbers on the shift: brand web mentions show a correlation of 0.664 with AI Overview visibility, compared to just 0.218 for backlinks (Ahrefs ). That’s roughly 3:1. The top three factors driving AI visibility are all off-site signals: brand web mentions, brand anchors, and brand search volume. Not links. Not domain authority. How often you get talked about. This makes sense when you think about how AI engines work compared to traditional search. Google’s PageRank followed links like a trail of breadcrumbs. AI engines read the web more like a researcher would: scanning conversations, forums, articles, and discussions to understand who gets referenced in the context of a specific topic. If multiple independent sources across Reddit, LinkedIn, and industry forums mention your brand when discussing tax compliance automation (to use a relevant example), that tells the AI engine something meaningful about your authority. The numbers get starker when you look at where citations actually come from. Brands are 6.5x more … --- ### How to run a GEO audit: a seven-point checklist for AI search URL: https://fiftyfiveandfive.com/resources/how-to-run-a-geo-audit-so-ai-search-engines-can-actually-find-your-content/ > A GEO audit checks whether ChatGPT, Claude and Perplexity can crawl, read and cite your site. The seven-point checklist we use, and the AI crawlers to allow. Author: Owen Steer Date: 2026-03-30 Quick answer: A GEO audit covers five areas: AI crawler access, JavaScript rendering, schema markup quality, content extractability, and a technical checklist for AI search. A site ranking well on Google can be invisible to ChatGPT and Perplexity because they don't render JavaScript, don't follow all links, and read only raw HTML. 50-80% of content on JS-heavy sites never reaches AI bots. You make your website technically ready for AI search engines by running a GEO audit that covers five areas: whether AI crawlers can actually access your pages, whether your content renders without JavaScript, whether your structured data is attribute-rich (not just present), whether each section of content can stand alone if extracted, and whether the full technical foundation passes a checklist built for AI search, not just Google. Most of this overlaps with good SEO hygiene. But the differences are where companies get caught out. A site that ranks well on Google can be completely invisible to ChatGPT, Claude, and Perplexity. Different crawlers, different rendering capabilities, different rules. If you’ve been building your AI search optimisation strategy around onsite content and offsite engagement , the technical infrastructure underneath is what makes both layers actually work. Without it, you’re publishing content that AI engines can’t read and building brand presence that AI engines can’t connect back to your site. Owen Steer at Fifty Five and Five . I’ve run these audits on B2B technology sites and on our own, and the issues I find are remarkably consistent. This piece walks through what I check, what I find, and the specific technical gaps that make the biggest difference to whether AI engines can find and cite your content. AI indexing is not Google indexing: what actually changed AI indexing works fundamentally differently from Google indexing, and that difference is why sites that rank well on Google might not exist as far as AI search engines are concerned. Google crawls your pages, renders JavaScript, follows links, and builds a searchable index. AI engines skip most of that. The first thing to understand is that AI crawlers serve two distinct purposes. Training crawlers (GPTBot, ClaudeBot, Google-Extended) harvest content to build and update the model’s knowledge base. Retrieval crawlers (ChatGPT-User, Claude-SearchBot, PerplexityBot) fetch content in real time when a user asks a question. Training builds what the model knows. Retrieval is what happens when the model needs to cite a source right now. These are separate systems with separate access controls, which means your content can exist in the model’s training data without being accessible for real-time citation (or vice versa). AI crawl volume is growing fast. JetOctopus data shows AI bot activity now sits at roughly 40-50% of Googlebot-level activity across the web (JetOctopus ). But the traffic return is asymmetric. Anthropic’s crawlers generate approximately 38,000 crawl requests for every single referral back to your site. OpenAI’s ratio is roughly 400:1 (Am I Cited ). That’s a lot of crawling for very little direct traffic. The value isn’t in click-throughs. It’s in citations. Google has a massive technical advantage over every other AI engine when it comes to understanding your content. Google … --- ### Account intelligence: deep ABM research in minutes, not weeks URL: https://fiftyfiveandfive.com/resources/account-intelligence-how-ai-does-deep-abm-research-in-minutes-not-weeks/ > Account intelligence cuts deep ABM research from 20-40 hours per account to 15-30 minutes, so the same team can cover 100-200 target accounts, not 5-10. Author: Owen Steer Date: 2026-03-26 Quick answer: AI cuts deep account research from 20-40 hours to 15-30 minutes per account, so a team that managed 5-10 accounts can cover 100-200 with comparable depth. The key is using AI for profiling priorities, mapping decision-makers, and building account-specific propositions, not skipping planning. Most ABM is account-listed, not account-based. Account intelligence is the difference. You do deep account research for ABM without it taking weeks by using AI to handle the research layer. Account intelligence is the step that separates ABM programmes that deliver from ones that flounder: profiling target accounts, mapping decision-makers, and building account-specific propositions. AI has changed the economics of doing all three properly. Manual ABM account research takes 20-40 hours per account (Influ2 ). At 50 accounts, that’s a full-time job for six months. AI-assisted approaches cut that to 15-30 minutes per account, enabling the same team that previously managed 5-10 accounts to cover 100-200 with research that’s comparable in depth to what a senior strategist would produce manually. Account intelligence is the research step in a broader AI data enrichment approach. Clean data feeds enriched intelligence, which feeds deep account research, which feeds personalised execution. Skip the research step and you’re running ABM without the account part. Account planning is where ABM is won or lost (and most teams rush it) ABM is 80% setup, 20% execution. I keep coming back to that ratio because I keep seeing it confirmed. Account planning is the core of that setup, and it’s the part most teams rush because the research doesn’t scale manually. I’ve practised ABM since 2019, across three agencies and dozens of accounts. The pattern is always the same: the strategy is sound, the targeting looks right, but the account planning is shallow. Teams have a list of companies they’d like to sell to, a set of generic emails, and a hope that volume will compensate for depth. That’s not account-based marketing. That’s account-listed marketing. Most ABM programmes I’ve encountered are the latter, even when they don’t realise it. So what should account planning actually cover? The data points that matter go well beyond firmographics. Strategic priorities (sourced from earnings calls, press releases, leadership statements, and hiring patterns) reveal where a company is actually investing budget and attention. Competitive landscape analysis shows who they’re up against and what kind of value proposition will resonate. Organisational structure and recent leadership changes identify who makes decisions and when openings exist. And recent activity from key contacts (publications, certifications, role changes) tells you what specific people care about right now. Most ABM “research” stops at company size and industry. Real account planning requires the context that changes how you approach each account, not just whether you approach it. The distinction matters because 87% of marketers say ABM delivers higher ROI than other strategies (ITSMA ), and ABM sales cycles run 28% faster than non-ABM approaches (Mailmodo ). But those numbers come from programmes with genuine account intelligence behind them. Surface-level planning produces surface-level results, and the ROI … --- ### Buyer intent data isn't working: how AI turns enriched data into real insights URL: https://fiftyfiveandfive.com/resources/buyer-intent-data-isn-t-working-how-ai-turns-enriched-data-into-real-insights/ > Why buyer intent data rarely tells you who to contact or what to say, and how AI research turns enriched data into insight you can personalise outreach with. Author: Owen Steer Date: 2026-03-26 Quick answer: Buyer intent data only tells you a company is researching a topic, not which person to contact or what to say. 52% of sales professionals report frequent false positives. AI-native enrichment closes that gap by surfacing per-contact intelligence: tenure, recent activity, and tailored conversation starters that make personalisation at scale actually work. Your enriched data doesn’t help you personalise at scale because buyer intent data gives you surface-level signals without the context to act on them. Most intent tools tell you a company is researching a topic. They don’t tell you which person to contact, what they care about, or what to say. That gap between signal and action is where personalisation breaks down. AI-native enrichment closes that gap. Instead of appending fields (job title, company size, industry), it surfaces research-grade intelligence per contact: tenure, recent activity, certifications, strategic priorities, and tailored conversation starters. The difference between “this company is in-market” and “this specific person, in this specific role, cares about this specific problem, and here’s how to open the conversation.” That’s what makes AI data enrichment work as a complete approach rather than just another data source. Half of all teams using intent data report too many false positives (Forrester ). And those are the teams that actually try to use the signals. Most buy intent data and never operationalise it at all. The B2B intent data problem: 52% false positives and account-level guesswork Most B2B intent data is built on three sources, and each one has accuracy problems. Content consumption tracking monitors publisher networks for topic-related reading. Bidstream advertising data captures ad interactions. IP-based web tracking identifies companies visiting web pages. All three tell you a company might be interested in something. None of them tell you who at that company is interested, or whether the interest is genuine. B2B intent data produces so many false positives because its foundation, bidstream data, tracks browsing activity at the IP level rather than the individual level. The signal resolves to a company, not a person. When your intent tool flags “Acme Corp is researching CRM software,” it can’t distinguish between a VP of Sales evaluating vendors, a student writing a dissertation, or a journalist researching an article. More than half the time, the “buying signal” isn’t real. The signal tells you a company might be researching a topic. It does not tell you that a specific person is ready to buy, what their priorities are, or what a relevant conversation would look like. That’s the gap between a signal and something you can act on. The false positive problem is well documented. 52% of sales professionals report frequent false positives from intent data signals (Salesforce ). More than half the time, the “buying signal” isn’t real. Employees browse for professional development. Students research for coursework. Journalists investigate for articles. A content consumption spike at Acme Corp might mean their VP of Operations is evaluating tools, or it might mean an intern is writing a report. Your intent tool can’t tell the difference. Then there’s … --- ### AI agents and ADHD: what running 20 at once is really like URL: https://fiftyfiveandfive.com/resources/ai-for-adhd/ > AI agents and ADHD traits, from a founder who runs 20 at once: what holding context does at work, where it goes wrong, and how we use AI at Fifty Five and Five. Author: Chris Wright Date: 2026-03-25 Quick answer: Updated October 2026. I run up to 20 AI agents at once at Fifty Five and Five, and I have a lot of ADHD traits. What the agents do for me at work is hold the context I drop, so I can switch jobs and pick each one back up. The cost is knowing when to stop. I’m dyslexic. I show a lot of ADHD traits, though I’ve never had a formal diagnosis. Ideas arrive fast. Attention shifts faster. I work in bursts, not blocks. I’ve been like this my whole life, and for most of it, the tools I had to work with punished that. Then I discovered agentic AI. Not chatbots. Not “ask a question, get an answer.” I mean proper AI agents that hold context, run tasks, and work alongside you. It started with OpenAI’s Codex, then Claude Code, then tools like Cowork and GitHub Copilot agent mode. I now run 20 terminal windows simultaneously, each one holding context for a different project, a different thread, a different half-finished thought my brain dropped 40 minutes ago. For the first time in my career, the way my brain works isn’t fighting the system. The system finally works like my brain. But I’ll be honest: I’m not sure this is entirely healthy. And I think that’s a conversation worth having. How my brain actually works I’ve never been formally diagnosed with ADHD. These things are all spectrums, and honestly I don’t feel a special need to pin down exactly where I sit on this one. Dyslexia was different. That had me properly struggling at school and then at uni. I got tested for it in my first year at Newcastle and it genuinely made a difference, suddenly things made sense. But ADHD? I’m 46 this year. I know a thing or two about myself by now, how my brain works, how I cope, and what happens when I don’t. I’ve built a marketing agency over 11 years, worked with Microsoft, Google, SAP, TCS, and hundreds of B2B tech companies. By any external measure, things have gone well. But internally? It’s not always been chaos. Sometimes things run smooth. But it’s always been full on. Ups and downs. Luckily I’ve always had good people around me, at home and at work. But yeah, it can be a lot. My brain can do “sit down and work through this methodically for three hours.” Sometimes. But it takes real focus to get there, and it’s not the default setting. I have a mix of working styles, but I tend towards more things rather than fewer. It’s more like “have seven ideas before breakfast, hyperfocus on one for 90 minutes, completely forget the other six existed, then remember them all at 2am.” I misspell things constantly. I lose track of conversations mid-sentence. I can build an entire analytics dashboard in a morning but forget to reply to the email that started it. For years, I compensated. Lists. Reminders. People around me who held the threads I dropped. Asana. God, Asana. I used it religiously for years, and so did the whole company. Every personal task, every project, everything tracked. I knew we’d taken it too far when a project manager created a task called “Be Creative”, assigned it to three people, gave it a date and a 90-minute time slot. Madness. But it worked, … --- ### AI data enrichment: clean, enrich, and activate B2B data URL: https://fiftyfiveandfive.com/resources/ai-data-enrichment-how-to-clean-enrich-and-activate-b2b-data-at-scale/ > AI data enrichment for B2B: how to clean, verify and enrich account and contact records at scale, then turn them into research your ABM can run on. Author: Owen Steer Date: 2026-03-25 Quick answer: AI data enrichment uses AI to clean, verify, and activate B2B data, fixing incomplete records at the foundation rather than just appending fields. It goes beyond tools like ZoomInfo and Clay, which add data points but can't fix data quality at the source. Bonus tip: it can also unlock better intent data than your household platforms promise. AI data enrichment is the process of using artificial intelligence to clean, verify, enhance, and activate B2B data, turning incomplete or outdated records into actionable intelligence that drives personalisation and account-based marketing at scale. It’s changing B2B sales and marketing because it solves the data completeness problem that quietly breaks everything downstream, from lead scoring to pipeline forecasting. Most B2B teams already know their data isn’t great. Fewer realise that the tools they’re paying for are part of the problem. Platforms like ZoomInfo and Clay will give you firmographics and what they call “intent signals,” but that’s surface-level enrichment. AI-native enrichment goes deeper: it fixes your data at the foundation, surfaces research-grade insights per account, and activates those insights for 1:1 outreach. Poor data quality costs the average organisation $12.9 million per year (Gartner ). That’s not a typo. And it’s not just about wasted spend. Bad data cascades through your entire funnel, poisoning every decision that depends on it. AI data enrichment is how you stop that cascade at the source. What B2B data enrichment actually means (and what most tools get wrong) B2B data enrichment is the process of enhancing your existing prospect and customer data with additional information from external sources. That includes firmographic data (company size, industry, revenue), technographic data (what tools they use), demographic data (job titles, seniority, department), and behavioural or intent data (what they’re researching, what content they’re consuming). That’s the textbook definition. In practice, most teams experience B2B data enrichment as “we bought a tool and now we have more fields in Salesforce.” Which is technically true, but it misses the point entirely. Tools like ZoomInfo and Clay are good at what they do, within limits. ZoomInfo has one of the largest B2B contact databases on the market and genuinely useful search filtering. Clay’s waterfall enrichment (chaining multiple data providers in sequence) is a clever approach to coverage gaps. I’ve used both. I’ve evaluated nine or more enrichment platforms at this point, and I’ve written about the best data enrichment tools and the hidden costs of data enrichment tools in detail. The problem isn’t that these tools don’t work. The problem is what they give you. Job titles, company size, basic firmographics, and intent signals that are often more noise than signal. ZoomInfo’s intent data has a well-documented accuracy problem: users frequently flag false positives among the buying signals, and the underlying methodology (bidstream advertising data and IP-based tracking) struggles with anyone working from home, on a VPN, or just researching out of curiosity. Clay’s waterfall enrichment is only as good as the sources it pulls from, and if your … --- ## About Fifty Five and Five builds AI tools for sales and marketing teams at large companies, and runs campaigns for technology companies worldwide. We're based in London. - Website: https://fiftyfiveandfive.com/ - Email: chris.wright@fiftyfiveandfive.com - LinkedIn: https://www.linkedin.com/company/fiftyfiveandfive/ - Instagram: https://www.instagram.com/fiftyfiveandfive/