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According to the McKinsey report, 92% of companies plan to increase their AI investment over the next three years. That same research found that only 1% of leaders describe their current AI deployment as “mature.” Almost every organisation is spending more, and almost none of them are getting to the finish line.
It is not a technology problem. It is not a budget problem. It is a capability problem that runs deeper than most leadership teams recognise, requiring urgent attention to close the gap.
I have spent the last 20 years working with digital product teams across the UK, Europe, and the US, spanning sectors as varied as fashion intelligence, financial services, commodities, publishing and B2B SaaS. In that time, I have seen organisations invest heavily in technology and still fail to deliver. I have seen smaller, leaner teams consistently outperform their better-resourced competitors. And through all of that, one question has stayed with me: what actually determines whether a digital product team succeeds?
It is not the tech stack. It is not the size of the roadmap. It is not even the quality of individual talent in isolation. It is capability and specifically, the right kind of capability at the right level of the organisation, built systematically across five interconnected layers.
That insight led my co-founder, Himani, and me to develop the DPC™ Framework, a proven model that helps leaders systematically build the five interconnected capability layers essential for AI-driven product success.
If you are a Head of Product, VP of Digital, or Director of Technology, the DPC™ framework helps you and your team navigate the everyday challenges of product management and AI’s ongoing impact on your roles and responsibilities.
The capability gap nobody is talking about
There is a conversation happening in boardrooms right now that sounds progressive on the surface: “We need to embed AI into our products.” “We need to move faster.” “We need to become more customer-centric.”
All of that is true. But what most organisations are not asking, at least not rigorously, is: do our people actually have the capability to lead the digital products in this AI era?
The assumption is that if you buy the right AI or product tools, hire a few AI-literate people, and run a few training sessions, capability will follow. But capability is not a switch you can flick; it’s a system you systematically build across all levels-industry, structure, culture, team ways of working, skills, and product performance.
If you miss even one layer, the entire system underperforms.
5 Capability Layers of the DPC Framework
Introducing the DPC™ Framework: five capability layers that drive product success
The DPC™ framework maps five capability layers that every product team, organisation, and team member must focus on to improve their performance in product management. And critically, each layer has a distinct owner and set of actions that Heads of Product, VPs, and Directors need to drive.
Capability Layer 1: Industry forces - are you reading the market correctly?
The first capability layer starts outside your organisation, looking at the industry as a whole. It asks: Does your leadership team have a genuine, grounded understanding of what is happening in your industry, especially with AI, not in a generalised way, but at the level of your specific competitive context?
The first dimension is AI and digital strategy at the sector level. It means going beyond generic AI literacy and developing a structured view of how AI is reshaping the competitive dynamics of your specific market, which players are moving, what bets they are making, and what that means for your product strategy over the next 12 to 24 months.
The second dimension is AI tools, concepts and adoption strategy. There is no shortage of AI tools available to product teams today. The challenge is not access, but selection, sequencing, and adoption. Which tools are genuinely relevant to your product context & team?
How do you build an adoption strategy that moves beyond early enthusiasts and embeds AI into the daily operating rhythm of your teams? These are not IT questions. They are product leadership questions in the lines of jobs-to-be-done.
The third dimension is AI governance, regulation and industry awareness. It is the area most product leaders underinvest in until it becomes urgent. Regulatory frameworks around AI are evolving rapidly across the UK, EU, US, and now India.
Industry and sector-specific implications, such as in healthcare, financial services, and data-intensive industries, are significant. Building governance awareness into your product strategy from the outset is not a compliance exercise. It is a risk management discipline that protects your roadmap.
Capability Layer 2: Organisation Readiness - your operating model built for ownership and speed?
The second capability layer looks inside your organisation. It asks: Does your organisation have a defined operating model that cross-functional teams follow?
The first dimension of organisational readiness is the product operating model, which defines how product decisions are made in your organisation and by whom. Who owns the product outcomes? What are your product teams’ rituals, ceremonies, processes, and disciplines that they follow from Discovery to Delivery of the product?
Closely connected to this is the second dimension of growth mindset and digital culture. The operating model design sets the structure, while culture determines whether people actually use it.
Building a genuine growth mindset, one that is reflected in how performance is measured, how teams are recognised, and how leaders behave under pressure, is a prerequisite for everything else in this framework.
The third dimension is product thinking, ownership and collaboration. It is about whether the people across your organisation, not just the product team, understand how to think in terms of problems, outcomes, and users rather than features, releases, and deadlines.
It is also about whether cross-functional collaboration is genuinely embedded, or whether product, engineering, design, and commercial teams are still operating in silos with handoffs rather than shared ownership.
I have seen this layer be the single most decisive factor in whether transformation programmes succeed or fail. You can hire the best product managers in the market. Still, if they are operating within a structure that requires three layers of approval to change a CTA or headline on a landing page, you have an organisation-readiness problem, not a talent problem.
Capability Layer 3: Team effectiveness - can your product teams actually deliver in an AI environment?
The third capability layer moves from the organisation to the product team itself. It is here that I see some of the widest gaps in organisations today.
The first dimension is agile ways of working in an AI-driven environment. Most organisations that have adopted agile are running a version of it designed for a pre-AI world with fixed sprint cadences, manual discovery processes, and a fixed delivery rhythm.
In an AI-augmented environment, discovery compresses, and prototyping accelerates. If you are embedding LLM-based outputs, you have to be super agile to release based on LLM fine-tuning.
The second dimension is hands-on digital agile product management. Now, this sounds like a foundational concept. Still, in practice, there is a significant gap between product managers who understand the theory of agile product management and those who can execute it with confidence in complex, real-world project environments.
Writing sharp user stories, running effective discovery, managing a prioritised backlog, making prioritisation calls under pressure – these are skills that need to be actively maintained and developed.
The third dimension is stakeholder management and communication. In my experience, this is the area where capable product managers most often struggle to reach their potential. The ability to build genuine alignment across engineering, commercial, design, and senior leadership is a distinct skill that is rarely developed systematically. In an AI era where product decisions have broader organisational implications, this capability becomes even more critical.
Capability Layer 4: Individual preparedness - is every person on your team ready to lead themselves?
The fourth capability layer moves from product teams to individual digital professionals. It is here that personal accountability, responsibility and proactiveness come into play.
The first dimension is AI influence on digital product management. The role of the product manager is changing more rapidly than most job descriptions reflect. AI is not just a feature to be built; it is a capability that changes how discovery is conducted, how requirements are defined, how hypotheses are tested, and how decisions are made.
Product managers who understand how AI is specifically reshaping their roles and who are actively adapting will become increasingly valuable. Those who are not will find their contribution narrowing.
The second dimension is deliverable responsibility and accountability. It might seem a natural part of the job, but personal ownership & accountability are more important than ever.
When AI tools make it easier to produce artefacts collectively and rapidly, then who owns the decision-making? Who is accountable for the outcome? Maintaining clear personal ownership over deliverables and a culture that expects and rewards it is a discipline that product leaders need to protect actively.
The third dimension is AI productivity through tools and concepts. Here is a real-world scenario: AI is now drafting user stories, synthesising large amounts of information and producing research items instantly, and generating competitive analyses in minutes.
The value of a product manager lies no longer in producing those artefacts. It is in the judgement applied to them, their ability to ask the right questions, identify what is missing, challenge assumptions, and make decisions under uncertainty. You need to leverage these AI tools to get more efficient and productive.
That is a different capability profile from what most product managers were hired for five years ago. Individual preparedness is about closing that gap proactively, not reactively.
Capability Layer 5: Product performance - are your products delivering the right outcomes?
The fifth and final capability layer focuses on the product itself. We have looked at the industry, organisation, teams, and individuals, and all of those layers are in play as we work on the digital product.
The first dimension is the product maturity matrix. Not all products are at the same stage of maturity, and the capabilities required to manage a product at launch are fundamentally different from those needed to scale or optimise it at peak. A product maturity matrix gives teams a structured, honest framework to assess where each product actually sits — and what “good” looks like at that stage.
The second dimension is go-to-market readiness. Building a great product and launching it effectively are two very different capabilities, and organisations that treat go-to-market as an afterthought consistently underperform relative to their product quality. Go-to-market readiness means your team has the skills, the cross-functional alignment, and the market intelligence to launch with confidence, not just to deploy a release. In an AI-augmented environment, this capability also includes understanding how to position AI-powered features in ways that build rather than erode customer trust.
The third dimension is the impact of AI execution on customers. It is the measurement gap that sits at the heart of that 1% maturity figure. Most organisations deploying AI are measuring internal efficiency: time saved, costs reduced, tasks automated.
Very few product teams are systematically measuring what their customers are actually experiencing as a result. Are customers getting better outcomes? Are trust and retention improving? Is the AI capability you have built translating into competitive differentiation that customers value?
The five capability layers are interconnected
What makes the DPCTM framework different from a standard skills matrix or a training programme is the insistence on treating these five capability layers as a system.
Let us take an example. Suppose an organisation invests heavily in Layer 3 (team effectiveness) but neglects Layer 2 (organisation readiness). They will find that the structures and mindset above them block their newly capable teams. In another example, suppose an organisation focuses on Layer 5 (product performance) metrics without addressing Layer 4 (individual preparedness). They find that performance improvements are fragile and hard to sustain.
The diagnostic question I ask every senior leader I work with is this: “If you were to score your organisation honestly against each of these five layers right now, where are your gaps, and are you addressing them in the right order?”
Where does your organisation stand within the 5 capability layers?
Going back to where we started: 92% of organisations are increasing AI investment. Only 1% have reached maturity. The gap between those two numbers is not about more spending; it is about the five capability layers that most organisations are either ignoring or addressing in the wrong sequence.
The good news is that capability gaps, unlike technology gaps, are genuinely closeable. They require clarity about where you are, a structured approach to getting there, and leadership willing to invest in people, not just platforms.
If reading this has prompted you to think about which of these five layers is your biggest constraint right now, that is exactly the right starting point.
Which of these five layers would your organisation score lowest on today? I would be genuinely interested to hear how you are thinking about it. Share with us where you are struggling and the first exploratory call is on us to help you out.
Book a 15mins call with me Or, drop me a line on LinkedIn
Reference: McKinsey report
