What happens when marketing teams start building their own AI-powered tools

Most marketing teams use AI to write things. For a comprehensive list of the best AI marketing tools to enhance your strategy, check out our article on the best AI marketing tools . Blog posts, social captions, and 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 .

I’m Barnaby Ellis , Head of Strategy at Fifty Five and Five. After almost 30 years of getting things wrong in digital agencies, I’ve learned that the gap between having an idea and showing someone what you mean has always been the expensive part. AI has closed that gap, and in the future, we’ll see even more transformation, as illustrated in our piece on how AI is shaping marketing . Not in theory. In practice, on real projects, with real clients.

B2B marketing teams can now create working prototypes, interactive demos, client tools, event activations, personalised sales materials, and internal platforms. Games that engage prospects at trade shows. Facial recognition tools that match video assets to press releases. Animated pitch concepts that used to take a creative team weeks. AI-powered social content engines that generate 80+ branded posts a month. Personalised video experiences delivered to a customer’s phone in near real time. The scope is sort of endless. And these aren’t theoretical use cases. They’re real projects, built by marketing teams, often without a developer in the room. The shift isn’t about using AI to write faster. It’s about using AI to build things that previously required budgets, timelines, and specialist teams that most B2B organisations simply didn’t have. But the output only matters when the thinking behind it is sound. A demo without strategy is just fluff. Nobody wants that.

From idea to demo in a day: AI prototyping for marketing teams

You can go from a whiteboard sketch to a working, playable prototype in a single day. Not a mockup. Not a wireframe with arrows. A thing that someone can pick up and use. The tools exist now, and they don’t require a development team.

A few weeks ago, I had coffee with a contact at an existing client. They’re new to the company and we hadn’t met before. Through a natural conversation about what we do and what they were up to, 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 of returning something you ordered online.

The game concept came from an old Nintendo handheld device from the late 80s that I remember playing. I liked the challenge of using both hands doing slightly different things. So 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 I might have thought of eventually, but Owen was able to bring that logic immediately.

Then I took it to Fergus Hannant, our designer. I showed him the wireframe and my mood board of what I wanted it to look like. He knew straight away, not just the vision, but how to get there. In under two hours, he turned a rough wireframe into a polished interface: colours, shapes, animations, matching hints for early gameplay. Something I was confident putting in front of the client.

We tested it with people in the office for bugs and feedback. Only then did I send it over.

The process is repeatable: research, sketch, build with AI, layer human expertise, test, and deliver. I remember working at an agency years ago where we came up with a concept for a credit card campaign, and we had this incredibly skilled art worker who sketched these beautiful diagrams of a Moroccan market. It took ages and they were beautiful. Now you can describe that concept and get it back as a visual, an animation, or a working prototype. The gap between imagining something and showing it to someone has collapsed.

Why the best AI sales enablement is showing, not telling

The most effective AI sales enablement isn’t a better slide deck or a smarter CRM plugin. It’s showing a prospect the thing you’re proposing, not just telling them about it. A working demo changes the conversation from “imagine if” to “what if we took this further?”

I saw something recently from Kieran Flanagan that stuck with me: we are in the era of show, not tell. That’s exactly where we are as an agency, and I think it applies to any B2B team pitching ideas.

Back in the day, we would prepare huge slide decks that took loads of time to put together. You’re trying to bring ideas to life in a way that clients can relate to and understand, backed with data. All those things are still true. But there’s also a moment of truth, the penny drop moment, when you want them to just get it. And that’s where AI-powered tools and this approach really help. We can describe what we’re doing, but then quite quickly show them the thing we are proposing.

With the game, I sent the client an email with as little information as possible. The whole point is that you should be able to walk up and play with minimal instruction. I gave some context, explained what it was designed to do, and challenged them to beat my score.

They loved it. They played it, told me their score, and engaged with how it connected to their business. Their technology solutions map to every stage of the retail returns problem, and I’d made that connection explicit in the game itself: subtle information highlighting the issues around returns, with gentle nudges towards the client’s technology.

Not a slide that says “we can do this.” A working thing that proves it.

So what happens when these tools become ubiquitous? When everybody has the same AI capabilities, the differentiator won’t be the output. It has to be the right output to address the outcome and reach the point you want to get to. AI is eliminating the resource constraints that you would normally have. It’s removing that hurdle going from the idea you’re telling people about to showing them exactly what you mean. But what you choose to show, and why, still requires the strategic thinking.

When to buy AI tools and when to build your own

Not everything should be built from scratch. MIT’s State of AI in Business 2025 report found that purchasing AI tools from specialised vendors or building through partnerships succeeds roughly 67% of the time. Fully internal builds succeed at approximately half that rate (MIT NANDA ). Meanwhile, 42% of companies scrapped the majority of their AI initiatives in 2025, up sharply from 17% the year prior (S&P Global ). The buy-versus-build decision matters because it determines whether your AI investment ever reaches production.

Buy when the problem is already solved. If the market is mature and a vendor is actively investing in the space, there’s no competitive advantage in rebuilding it yourself. AI-powered meeting transcription, email automation, and CRM enrichment: these are commodity problems. Buy the tool, integrate it, move on.

Build when the competitive advantage is yours. If what makes the solution valuable is your proprietary data, your unique process, or the way your team thinks about a problem, build it. Your personalisation engine, your content workflow, your client activation: these are not jobs to outsource to a vendor who serves your competitors equally.

We saw this play out with our Avalara project. Rather than signing a long retainer to deliver AI search optimisation work manually each month, we proved the process works with human delivery, then replaced ourselves with a tool. As I see it, rather than replacing roles within a company, which tends to be the thing that people kick AI with, what we’re doing is using a tool to replace ourselves in that process. More teams should consider that approach. If you’d like to explore what it might look like for your business, get in touch .

What does it actually cost to build custom AI marketing tools?

The honest answer: less than you think in tools, more than you think in thinking. I built a working game prototype in a day using Claude Code. Fergus polished the visual design in under two hours using the same AI tools with his design expertise layered on top. The AI platforms themselves cost very little compared to traditional development. Where the real investment sits is in the time before you start building: researching the problem, understanding the client, sketching the concept, and defining what good looks like. You also need the right skills on your team to validate the output, because without that, you end up with something that looks polished but isn’t grounded in reality. A prototype is fast. Getting the prototype right takes strategic thinking, domain knowledge, and people who can spot when the AI has produced something that misses the mark. The investment isn’t in the technology. It’s in the expertise to use it well.

Real examples of B2B teams creating with AI

It’s easy to talk about what’s possible in theory. Here are real projects where teams used AI to build things that weren’t content.

TCS London Marathon: 1,500 personalised finish-line videos in three days

TCS asked us to bring an AI activation to their stand at the Running Show, the expo all runners attend to collect their packs. The solution: capture each runner’s photo, process their likeness in real time using nine different AI tools, generate a 3D avatar, and composite it into a personalised finish-line video delivered to their phone, all before they’d even run the race. 40,000 machines rendering in parallel. Over 1,500 personalised videos generated across three days. BBC News covered it. It won a Bronze Drum Award for Best Use of AI in a Campaign (TCS Marathon activation ).

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. The technology really supported what we were trying to communicate on the stand, the TCS Digital Twin program.

Anmol Patel Social Media & Insights Manager, TCS

The Retail Tech Show game: from coffee chat to working prototype

The game I described earlier. Spotted the opportunity in an informal meeting, researched the industry, built a playable prototype in a day, collaborated with Owen on gameplay logic and Fergus on visual design, tested with the team, and sent it to the client. They played it and engaged. It didn’t need human rescuing, but it definitely needed human polishing.

Avalara: an AI-powered social engagement engine

For Avalara, we built a tool that identifies opportunities across Reddit, LinkedIn, Quora, and Medium where their existing content library can answer real questions. It analyses conversations, matches them to relevant Avalara resources, and crafts responses grounded in their expertise. The tool covers the full workflow from picking a product to focus on, to finding engagement opportunities, to writing responses. What started as a manual service became a capability the client owns and runs themselves.

TCS Compass: 80+ AI-generated social posts per month

We deployed Compass , our AI-powered SaaS platform, fine-tuned on TCS’s brand and tone of voice. The team now relies on it for social media content and converting video into copy quickly. Over 80 posts generated per month, 60+ hours of production time saved, and 3x faster from ideation to posting (TCS Compass ).

What changes when everyone on your marketing team has AI

Roles blur. The strategist builds prototypes. The designer writes code. The marketer interrogates data without waiting for a report. When everyone on the team has AI tools, the traditional lanes break down, and that changes how the whole team thinks and works together.

Once upon a time, everyone stuck to their lane, or you would do your part and hand over to the next person’s part of the project. Now everyone is able to think and approach challenges with the same creativity. When I can show you what I mean rather than just telling you, it’s much easier to riff off that. It’s much easier to say, I don’t get it, or I do get it, or what if?

And “what if” is the golden question. That’s when you know someone’s engaged. They’re going to add or bring a different perspective to the table. If we can break down those lanes and everybody is able to bring a “what if” question, we’re getting a diverse set of thinking. It’s not the same people with the same ideas.

McKinsey’s research backs this up at scale: a human team of two to five people can already supervise 50 to 100 specialised AI agents running an end-to-end process like launching a product (McKinsey ). Marketing executives who describe their AI use as mature have already seen 22% efficiency gains (McKinsey ). That’s not a theoretical projection. Those are numbers from teams already working this way.

The part that doesn’t get enough attention: you need to know what good looks like. Otherwise, what comes out the other end, you have no way to judge whether it’s right or not. And the only way you can do that is by having some understanding about the thing you’re trying to achieve. That comes from research, good briefs, and an understanding of the client.

We learned this the hard way. About a year ago, we were creating AI-assisted content for a client where the case studies were confidential. The constraints on what we could quote led to a stretch, a fictionalisation of evidence that went beyond the actual work. Effectively, we made up case study examples that weren’t accurate. The person who created them didn’t recognise the problem because they didn’t understand enough about what good looked like. The client and I both flagged it as not fit for purpose. We’re well past that point now, but it stuck as a clear reminder: AI output without domain knowledge to validate it is a risk.

AI tools give your team more capability. But that capability needs to be matched with the skills to know when the output is right, when it needs polishing, and when it needs to be thrown away.

How AI improves marketing analytics for B2B teams

When everyone on the team has AI tools, analytics stops being the domain of one specialist. Natural language interfaces mean anyone can interrogate data, surface patterns, and generate insights without waiting for a report or a dashboard build. A strategist can ask “which campaign drove the most qualified pipeline last quarter?” and get an answer in seconds, without writing a query or opening a spreadsheet. The advantage is shifting. It’s moving away from simply having access to information and towards knowing what questions to ask, how to interpret the answers, and how to turn insight into action. The data hasn’t changed. What’s changed is who can access it and how quickly they can act on it. That’s where diverse thinking from the whole team, not just the data person, becomes genuinely valuable. More perspectives on the same data means better questions, and better questions lead to better decisions.

What can B2B marketing teams actually create with AI?

Far more than content. Working prototypes, interactive demos, event activations, personalised sales materials, and internal tools. The resource constraints that used to sit between an idea and a finished product are disappearing.

But this isn’t a technology story. It’s a thinking story. The teams that get the most from AI are the ones who do the research first, understand the problem they’re solving, and know what good looks like before they start building. AI needs polishing, not rescuing. And when every team has the same tools, the differentiator won’t be the output. It will be the quality of the question you asked to get there.

Show, not tell. Build when it’s your competitive advantage. Layer human expertise at every step. And ask the questions other people aren’t brave enough to ask. That’s where the breakthroughs come from.

The best AI-powered marketing tools aren’t the ones you subscribe to. They’re the ones your team builds to solve a problem nobody else has thought to solve yet.

If any of this has prompted a “what if” question, I’d love to hear it. Drop me a line , and let’s explore the possibilities.

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