What scalable AI-native companies get right: lessons from Faculty

Faculty’s acquisition by Accenture is a rare outcome—not just in UK technology, but globally—and particularly so for AI-native businesses operating in complex, regulated, and public-interest sectors, including education.

Although Faculty is a consulting-led company, the reasons it scaled successfully are not specific to consulting. They reflect a set of deliberate choices about strategy, talent, operating model, and repeatability that increasingly distinguish AI-native organizations able to move from promise to scale. These same choices now matter deeply to edtech companies, education publishers, learning providers, and educational institutions navigating AI-driven opportunity and change.

A high-valuation acquisition by a global firm like Accenture is rarely driven by timing or market enthusiasm alone. In Faculty’s case, it was the result of a long series of deliberate decisions that compounded over time into a set of substantial and hard-to-replicate competitive advantages.

I have seen first-hand similar strategies play out elsewhere in education. The acquisition of Sana Labs by Workday, for example, reflected not just strong technology, but sustained focus on productization, repeatable capability, and successful deployment in complex operating environments. In different ways, both companies showcase what it takes for AI-native companies to scale credibly in education and adjacent sectors.

Having worked closely with Faculty over an extended period, I have had a front-row seat to see these dynamics in action. What follows is not a blueprint, but an illustrative set of strategic decisions that created competitive distances that education leaders can use to assess whether they are building defensible AI-enabled capability and distance—or not.

Why Faculty succeeded

Faculty‘s success was not the result of any single breakthrough. It emerged from a set of reinforcing choices that compounded over time into a durable competitive position. Individually, none of these choices is unique. Taken together, they created advantages that are difficult for others to replicate quickly or coherently.

1. Deep sector grounding combined with deliberate cross-sector learning

Faculty did not treat AI as a generic capability to be deployed uniformly across markets. Instead, it invested in deep understanding of specific, high-stakes sectors—including pharma, finance, and defence—where data quality, safety, governance, and real-world consequences matter.

Crucially, those sector learnings were not left siloed. Faculty deliberately transferred architectures, delivery practices, and risk-management approaches across use cases and into adjacent sectors, including education. The result was not just better models, but better judgment about where AI creates real value and where it does not.

For education companies, the parallel is clear: advantage rarely comes from applying AI features broadly, but from solving deeply one or two priority use cases and then scaling those insights thoughtfully across products and markets.

2. A genuine “strategy-to-build” discipline

Faculty collapsed the traditional gap between AI strategy and AI delivery. Strategy was not an abstract exercise conducted upstream of implementation; it was inseparable from the practical realities of building, deploying, and maintaining systems in production.

This matters because most AI initiatives fail not due to lack of vision, but at the point where ambition meets organizational, technical, and regulatory complexity. Faculty’s strategic guidance was grounded in first-hand experience of what it actually takes to deliver safely, sustainably, and at scale.

Education leaders face the same challenge. AI strategies that are not tightly coupled to delivery, operating constraints, and adoption realities quickly become shelfware. Strategy that cannot survive execution is not strategy.

3. Talent systems as a core strategic asset

From its earliest days, Faculty treated talent as a strategic asset to be deliberately cultivated. The Fellowship program began as a way to prepare exceptional STEM graduates for industry roles, but evolved into a sustained talent engine aligned with Faculty’s delivery model.

In doing so, Faculty addressed one of the hardest constraints facing organizations building AI capability: establishing a sustainable, renewable pipeline of high-quality talent, rather than relying on scarce hires or short-term fixes.

Over time, this created a cohort of practitioners able to operate at the frontier of applied AI while working effectively inside complex client environments. Importantly, this talent depth also became a capability Faculty could extend to clients—helping them build their own internal AI capacity rather than remain perpetually dependent on external expertise.

For education organizations, the lesson is not to copy a fellowship programme, but to recognise that scalable, sustainable AI capability depends on intentional talent systems—not hero hires or isolated centers of excellence.

4. Repeatability without loss of ambition

Perhaps most importantly, Faculty learned how to make its work repeatable without diluting technical ambition. Bespoke problem-solving was gradually translated into disciplined delivery patterns, shared infrastructure, and reusable components.

More recently, this same logic extended into productized capability with Frontier, Faculty’s decision-intelligence platform. This reflected a broader shift beyond time-based services toward scalable, repeatable AI systems that can be deployed, governed, and improved across multiple contexts. (See too my analysis of why educational publishing is ripe for disruption with just such an agentic solution.)

For education product companies and publishers, this pattern is directly relevant. Long-term advantage does not come from piling up AI features, but from turning hard-won insight into repeatable capability that compounds value over time.

Why Faculty is a smart acquisition for Accenture

From Accenture’s perspective, the acquisition of Faculty is both bold and highly rational. Large professional services firms are facing a structural inflection point driven by AI: long-standing consulting models built around episodic advice, strategy decks, and time-based delivery are no longer sufficient for clients grappling with AI at scale. (See also my analysis of the impact of AI in higher education and why legacy courseware is beginning to collapse.)

Clients increasingly want production systems, not proofs of concept. They want AI that is safe, governed, and embedded in real operating environments—and they want partners who can take responsibility for accelerating delivery, not just direction. This shift is particularly acute in regulated and mission-critical sectors, including education, where experimentation without durability carries real risk.

Faculty brings Accenture a set of capabilities that directly address this shift:

• A next-generation AI delivery model that integrates strategy, build, enablement, and now productized capability—collapsing the traditional gap between advice and execution.
• A deep, high-quality talent base, oriented around applied AI rather than generic consulting skills.
• A proven talent pipeline, solving one of the toughest constraints in scaling AI delivery.
• A safety-first build philosophy, grounded in real deployments where governance, risk, and trust matter.

Crucially, this acquisition is not simply about acquiring skills or capacity. It is about acquiring a different way of working—one that Accenture can now scale globally across both bespoke delivery and repeatable AI systems.

For education leaders, the signal is important. The same forces reshaping consulting are reshaping education markets: AI advantage is moving away from isolated features and pilots, toward operating models, talent systems, and repeatable capability that can be trusted at scale. Faculty’s acquisition reflects where durable value is now being created—and where education organizations will increasingly need to invest. (I recently presented to Vice Chancellors of leading UK universities a roadmap for how to reinvent higher education in the age of AI.)

A personal footnote

I have had the privilege of working closely with Faculty over a number of years, supporting the growth of their education portfolio and the delivery of AI-enabled solutions for ambitious education organizations around the world.

My role in their story has been a small one. But it has given me a front-row seat to the decisions, trade-offs, and execution discipline described above—and to the less visible work required to turn AI potential into durable capability.

Faculty’s success is richly deserved, and the acquisition makes strategic sense for both organizations. I am excited to continue supporting them through the next chapter of growth.

What this mean for leaders of education companies

AI is no longer a question of experimentation or feature adoption in education. The challenge now is how organizations translate AI ambition into operating models, talent systems, and repeatable capability that amplify their value and scale.

Through enablinginsights, I work with a diversity of edtech companies, education publishers, learning providers, and education institutions that are navigating this transition—often at moments of strategic inflection, market pressure, or organizational change.

If you are grappling with questions such as:

• How robust is our AI strategy? Is it creating genuine competitive differentiation and a path to sustainable growth—or does it risk converging on the same feature set as everyone else?
• How should our next-generation product or service portfolio evolve? Which capabilities should we double down on, which should we deprioritise, and how do we benchmark realistically against competitors while leaning into our own distinctive strengths?
• How do we credibly evidence value? What internal operational KPIs, learning outcomes, or customer-facing evidence will actually stand up to scrutiny—from boards, buyers, partners, or investors?
• What does “scaling AI responsibly” really require for our organization? In terms of operating model, talent, governance, and delivery—not just technology choices.

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