The Intent Data Execution Gap

Have you ever sat in a marketing strategy meeting when this happens?

You're analysing the intent data. It looks promising: hot accounts, clear buying signals, specific product interest across multiple categories. Everyone nods approvingly at the dashboard showing precisely which companies are researching your solutions. The strategic momentum is building.

Then someone asks: "Right, so how are we actually going to take this data and craft everything we need?"

The energy in the room shifts. Suddenly you're talking about resource allocation, content calendars, and whether the creative team can handle 47 different account-specific campaigns across three product lines and five persona types. The strategic momentum grinds to a halt as operational reality sets in.

The Intent Data Paradox

We've built increasingly sophisticated MarTech stacks. Global spending on marketing technology is growing to £160 billion by 2027. The intent data tools alone represent a booming market (think 6sense, Bombora, and Demandbase) with the industry projected to be worth £5.3 billion by 2027, growing at 11.5% annually.

These tools deliver actionable intelligence about which accounts are showing buying intent. Two-thirds of marketing professionals plan to invest more in intent data this year, and the insights are genuinely valuable. We can see which companies are researching our solutions and which competitors they're evaluating.

But here's the paradox: the more sophisticated our intent data becomes, the wider the execution gap grows. We have clear signals about which accounts are showing buying intent and interest in our solutions, yet we're still creating content manually, slowly, and often generically.

The Hidden Cost of the Execution Gap

When we identify hot accounts showing intent, the clock starts ticking. Intent signals have a shelf life, and our ability to capitalise on them depends on how quickly we can create relevant, personalised content.

Delayed campaign launches whilst content gets created represent missed opportunities. Even more problematic is resorting to generic content that wastes the precision of our intent insights. When content creation becomes the bottleneck, teams compromise on personalisation, sending the same template to accounts researching different solutions.

According to Gartner's 2024 CMO Spend Survey, MarTech now accounts for 23.8% of marketing budgets. Simultaneously, investment in staffing has declined, reflecting a broader trend of budgetary constraints. And so it seems that companies continue to invest in technology to generate insights, yet they are often relying on increasingly stretched human resources to act upon them.

Why Current AI Solutions Miss the Mark

The natural response to this execution challenge has been to embrace AI content creation tools. Teams across enterprises are using Jasper, Claude, ChatGPT, and countless other AI platforms to scale their content production. B2B-focused tools like Demandbase combine intent data with AI personalisation, Copy.ai targets B2B content workflows, and Persado specialises in AI-powered messaging optimisation. On the surface, this seems like the perfect solution—AI can generate personalised content at unprecedented speed and scale.

However, whilst these tools address some immediate content production challenges, they introduce new strategic complexities that organisations need to navigate.

When everyone uses similar prompts and tools, the commoditisation problem emerges. We all sound the same. The irony is palpable: we have intent data telling us exactly what makes each account unique, yet our AI-generated content makes us indistinguishable from every other vendor in the space. We're defeating the purpose of personalised, intent-driven marketing by homogenising our response.

AI tools can also confidently generate statistics, case studies, and industry insights that simply don't exist, creating hallucination risks that pose serious compliance and credibility concerns. Solutions like Ada address this by only selecting facts that you provide it with. It doesn't make things up, maintaining the integrity essential for business communications.

Enterprise users face additional data security concerns. Feeding proprietary customer insights, campaign strategies, and competitive intelligence into public AI tools raises serious privacy questions. Many organisations find themselves unable to use AI tools effectively because they cannot share the contextual information needed to create truly personalised content.

Beyond security, there's a fundamental context gap. Generic AI doesn't understand our brand voice, industry nuances, or the specific behavioural triggers that make our audience act. It can create content, but it cannot create strategic content that connects intent signals to persuasive messaging frameworks.

Most fundamentally, we're witnessing a strategy void. Tools like Jasper and ChatGPT solve the volume problem. Yes, we can generate 100 blog posts in an hour. But can we generate 100 strategically different pieces of content that each serve a specific intent signal with the right behavioural triggers for the right persona type? Volume without strategic differentiation is just expensive noise.

Teams often fall into the prompt engineering trap, spending countless hours crafting the perfect prompts to coax AI tools into generating more strategic content. But prompts remain instructions to create generic content faster. We're optimising generation speed rather than the strategic framework that makes content persuasive.

The Behavioural Science Bridge

The solution to bridging the intent data execution gap lies not in faster content generation, but in systematic content strategy. Every piece of content needs what we might call ‘a digital fingerprint’ - a clear, measurable breakdown of the themes, facts, behavioural triggers, personality types, tones, and persuasion techniques it employs.

This approach transforms content creation from an art into a science. Instead of hoping our content will resonate, we can systematically work through different variables for every piece of content we create. When each asset has its own digital fingerprint, we can test, learn, and optimise campaigns more quickly than ever before.

This is where solutions like Ada become transformative. Rather than generating generic content at scale, Ada allows teams to systematically approach content creation with behavioural science foundations. Every piece of content can be deconstructed and analysed. We can tell teams exactly what each asset has been built on, from the persuasion techniques employed to the personality types targeted.

Ada's trademarked 'Campaign in a Click' approach enables rapid creation of complete campaigns - ads, emails, landing pages, blogs - whilst maintaining strategic depth. More importantly, the digital fingerprint enables faster optimisation. When we know exactly which themes, facts, and behavioural triggers were used in high-performing content, we can systematically apply those learnings to improve future campaigns.

This systematic approach enables genuine personalisation at scale. When intent data shows us that an account is researching cybersecurity solutions with a focus on compliance, we can create multiple variations of content targeting different personality types, headline styles, CTAs, and tones of voice. Rather than one-size-fits-all messaging, we can systematically test, for example, authority-based approaches for analytical decision-makers, urgency-driven messaging for action-oriented buyers, and relationship-focused content for collaborative teams, all within the same campaign.

The digital fingerprint approach also enables continuous optimisation. When we know exactly which combinations of variables drive the highest engagement and conversion rates, this data becomes the foundation for continuously improving content strategy.

The Path Forward

The intent data execution gap represents one of the most significant opportunities in modern B2B marketing. The solution isn't better AI writing tools or more sophisticated intent data platforms. It's the systematic application of behavioural science to content creation.

When we can create content that's strategically differentiated rather than just voluminous, we unlock the full potential of our MarTech investments. The organisations that solve this challenge first will respond to intent signals faster, with more relevant content, backed by measurable behavioural science.

For teams ready to bridge this gap, tools like Ada provide the framework to transform intent insights into persuasive content at scale. The technology exists. The opportunity is clear. The only question is how quickly you'll act on it.