Is there anything more tedious today than a company that proclaims to be “AI driven"? Or all the content out there about how to write the perfect ChatGPT prompt? Here’s the summary of all these stories:
- They open a (free) ChatGPT account
- They talk about “prompt engineering”
- Then say, “We’re AI driven, and we’ve saved 38 hours a week.”
Is that business really AI-driven? Is that even the direction we want to head in? Saving a few hours doing the same old stuff we do now but a bit more quickly. Hold on, let me off here, thanks.
AI has been written and talked about so much that it’s beginning to lose its meaning. All this content has that metallic smack of bandwagon jumping. The classic marketing herd mentality trap.
I’m not judging.
You see what works and you follow it. No judgement about that, but I'm worried that we’re in danger of wasting a seriously great opportunity to use this – quite frankly –magic technology to create a new world of work.
Instead of thinking about saving time we should be thinking “What if?”
Being kind, there’s lots of levels to being ‘AI-driven’. You can open a ChatGPT account or try a version of Microsoft Copilot and use it to help you do some day-to-day tasks. That’s level 0. In any case, you’re still a few levels off being AI-driven. It’s like saying you’re a digital workplace because you’re thinking about getting a new laptop.
Let's talk about “True AI”. It’s a term I want to popularise, especially in non tech circles. It is a term that means “AI doesn’t start and end with ChatGPT! Typing a bit of text and pasting the answer somewhere is not where this game ends!”. What I’m talking about is AI that breaks out of textboxes in a browser. AI agents that go off and do work for you. “Text to action” models that do actual things, like build a PowerPoint, or 4hrs competitor research. And AI that isn’t text. Audio and video is a much more natural interface. This is the sort of AI that gets integrated into the fabric of business operations. We’re going to move beyond ChatGPT here and think BIGGER.
True AI goes beyond even multiple AI tools or automating basic tasks.
It’s digital transformation (if we’re throwing buzzwords around) of marketing operations. With AI adoption challenges nailed, and success built into the foundations. The new ‘digital transformation’ will be called ‘AI transformation’.
Simply: a business model with AI at its core.
With AI transformation, we've built new ways of interacting with prospects, increasing active touchpoints and hero asset downloads by 300%.
Moving beyond surface-level AI
Many businesses have implemented AI in isolated pockets – and the result has been underwhelming. Only 12% of businesses have integrated AI into their operations in a way that’s transformative. Clearly, they’ve experienced AI adoption challenges.
This means true AI is rare.
We’ve spent a year testing, building, and integrating AI tools for sales and marketing. We’ve launched Compass, a bespoke AI marketing tool – and are selling it to clients. It’s using the latest in AI agent tech. Our teams use Compass, ChatGPT, Midjourney, HeyGen, and a host of other AI tools every day to speed things up – of course we do (Level 0) but our real focus is on reinventing how our clients engage their audiences.
But we’ve had our AI adaption challenges too. The more we’ve integrated AI into our workflows, the more we’ve realised we’ve overcome. From growing our AI expertise to cultural resistance and fear of losing the “human touch”, we’ve learnt a lot. And we can pass on a lot too.
Your AI adoption needs to be built around the work you do for your clients or customers. Adopting AI to improve internally will only go so far. Your staff won’t take to it (change is bloody hard) or you’ll lose your direction/vision. But if you keep the client/customer as the guiding light you can navigate even when things get stormy.
More about AI adoption challenges
You business leaders can be a cautious bunch. If you’re limiting AI initiatives to small, low-risk areas it means that your AI marketing tools will sit in isolation. The problem with this is that if you’re doing little bits of AI with single tools, you’re not going to see that full value.
But don't just take my word for it. Research from the University of Cambridge shows that adopting AI requires more than tools – it requires a strategic, company-wide approach that promotes training, and acknowledges the social dynamics among employees.
Business leaders must nurture this kind of environment, and invest in AI, to help facilitate this genuine AI adoption, wherein lies “True AI”. Make sense?
AI-first = client-first
Despite our own AI adoption challenges, we’ve found AI tools to be very effective. They deliver an ROI – and importantly potential ROI – that far exceeds anything else. An example of this is our AI-powered lead gen forms we built (in rapid time) for a client. It was a complex product but in a nutshell, these lead gen forms capture more customer data with less effort from the customer. How? Simple:
- Our client wanted a form for their lead gen campaign.
- The sales team wanted a ton of info on the prospects’ company e.g. how big it was and how many meeting rooms they had.
- Then, they loaded the form up with about 600 fields (I’m only exaggerating, slightly).
Magic lead gen forms turned the form into a single field (business email address). Plus, we still managed to get all the info the sales team wanted using our clever tech (yes, even the number of meeting rooms!).
The lead capture rate increased by 300%! And not a ChatGPT window in sight.
So, AI has massive ROI potential. Consider some marketing possibilities like, off the top of my head:
- Predictive “next best action” engine for sales reps
- Instant influencer identification app
- Predictive viral content generator
- Personalised kiosks for marketing events (using AI-driven facial recognition).
My point is you can see that if AI is built in as a fundamental part we can start to think of new possibilities.
"What if?”
For many businesses, AI adoption challenges will be cultural – not technological. They will be shift in thinking. In learning. And in how teams operate. Remember, true AI transformation is mostly about the environment. Fostering an environment where people are allowed to think differently.
That’s what we’re trying to cultivate here. We’ve integrated AI into our business. And it’s behind everything we do. It’s begun to change the way we think about work. I find we’re asking, “What if?” lot more:
- What if an eBook we create could be automatically customised for each reader? Interactive content!
- What if your client could ask the report, you gave them any question they wanted and get the answer they needed?
- What if a landing page form only asked a visitor for one piece of information and we still got everything we needed from them?
- What if we could personalise 6,000 emails to the right people from a static list of companies?
Not everything we try comes off. With experimentation comes failure. But it’s in the failure we learn, adapt, and push the boundaries of what AI can achieve.
Lessons learned from AI adoption challenges
A key lesson we’ve learnt in our AI journey is that experimentation matters. Think of AI like the early internet. The internet’s capability early on was: connecting people and providing access to information. Early adopters couldn’t think much past the idea of the internet as a kind of encyclopaedia.
It took about a decade for other uses to take off with novel applications like social networking or video streaming.
AI transformation will possibly take a similar trajectory, but I think it’s going to happen much, much faster. So much faster that it won’t look like the same trajectory. with the hype around “intelligence” or “automation” giving way to more innovative cases over time. We’re trying to predict what those use cases might be.
Like any technology, AI’s always moving, changing – and transforming at an exponential pace.
Already, OpenAI has developed a new series of AI models (o1) which spend significantly more time... thinking. Designed for very complex problems, these AI models add a new capability for AI, which is deep thought and strategic reasoning. Consider now what use cases this kind of technology can underpin. A regenerating, evergreen content strategy? Autonomous content curation that changes based on real-time data?
As we’ve already seen, true AI adoption is about more than tools. It’s about asking the right questions.
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