What 2025 taught us about data, AI, and investment conviction

As we close out 2025 and start to look ahead to 2026, I wanted to share my reflections on a year in which the pace of change has been relentless.  Yet one constant emerged in every boardroom and diligence call we have been part of: clarity on data foundations now sits at the centre of operational performance, investment conviction, and exit readiness.

Across our work this year, we have seen investors probe earlier and deeper into the evidence behind growth claims.  Deal teams increasingly want to understand not only what a business does, but how it really runs — the completeness, connectedness, and trustworthiness of the information beneath the IM headlines.  That shift is entirely rational.  Data maturity has become a leading indicator of both scale potential and resilience, and it is now shaping valuation outcomes more directly than ever.

Looking across our engagements, particularly diligence, a few themes repeatedly defined where value was created:

• Businesses with a single source of truth and a clear data strategy moved faster, operated more efficiently, and demonstrated stronger pricing confidence at exit.

• AI delivered meaningful gains when tied to KPIs and grounded in real system data — not when selected from a menu of possibilities.

• Pragmatism outperformed experimentation. The winners were not those who pursued the most sophisticated models or ‘shiniest’ tools, but those who aligned people, process, and technology around measurable outcomes.

These patterns are not theoretical.  They mirror what we described in last year’s review — the move from hype to implementation, the increasing importance of data governance and structure, and the need to balance automation with augmentation.  This year has reinforced that message even more strongly.

A year defined by what worked — and what didn’t

Thought leadership across the industry has continued to highlight recurring patterns in AI implementation programmes.  The organisations that succeeded this year were not the ones with the most advanced models, but those that invested early in the human and organisational capabilities required to make transformation possible. 

Where AI delivered measurable impact, it was because teams were aligned on the problem being solved, leaders understood how success would be measured, and the underlying data foundations were ready to support the work (meaning there had been a deliberate approach to data readiness, governance, structure, and ownership before deploying AI).

By contrast, the programmes that stalled tended to treat AI primarily as a technology acquisition.  Without clarity on ownership, behaviour change, or cross-functional alignment, even sophisticated tools struggled to achieve meaningful traction.

Across case studies and roundtables this year, the same lesson surfaced: success depended less on model superiority and more on redesigning how people worked.

Continuity of thinking: data as an exchange, not an artefact

In November, I wrote about the need for investors and management teams to think differently about the flow of data across an organisation — as a series of exchanges between people, systems, and decisions.  That idea has also resonated across our client base because it reframes data not as an IT asset but as an economic substrate.  When those exchanges are slow, incomplete, or fragmented, the business feels it: in lower margin, weaker customer understanding, and slower scaling.

Throughout 2025, we have helped portfolio companies re-architect these exchanges so that insight moves cleanly from source systems to decision-makers.  For some, that meant establishing a Business Data Twin.  For others, it meant decoupling legacy infrastructure, building AI-ready layers, or re-shaping how teams consume information through purpose-driven dashboards and BI. 

What distinguished the companies that progressed fastest was their willingness to redesign workflows alongside the technology.  Rather than “optimising jobs”, they focused on restructuring work itself — clarifying decision rights, simplifying processes, and ensuring AI and automation supported, rather than disrupted, the rhythm of operations.  This alignment between data, process, and people proved far more important than the specific tools chosen.

In every case, the gains were measurable — whether in EBITDA expansion, operational efficiency, or strengthened exit stories — because the work was anchored in business reality rather than technical ambition.

What it means to be a boutique in 2025

The past year has also highlighted the growing value of specialised, senior-led boutiques — not as an alternative to scale firms, but as a fundamentally different proposition.

Our model is intentionally lean, and leader driven.

Your projects are not handed to large teams of juniors; they are led by senior data, analytics, and investment specialists who understand the demands of private equity, the constraints of mid-market operations, and the commercial realities of implementation.

This depth matters because scaling AI requires judgement calls about operating-model change, guardrails, governance, sequencing, and feasibility — areas where generic playbooks or large-team delivery models can falter.  A senior-led approach enables faster iteration, clearer accountability, and more pragmatic guidance rooted in experience rather than volume.

This approach offers three advantages:

1. Depth over volume: senior expertise applied directly to the work, not filtered through layers.

2. Pragmatism: realistic guidance grounded in pattern recognition from dozens of comparable situations.

3. Flexibility: the ability to scale capacity, adapt scope, and shape fees without the overhead of a traditional pyramid model.

For many of you, this has been one of the reasons our work has felt both faster and more commercially aligned — a theme reflected in feedback throughout the year.

Looking ahead to 2026

The market is entering a period where evidence-based AI claims, data transparency, and governance will define competitive edge.  As regulatory pressures grow and exit markets reopen, firms will need to demonstrate not only ambition but reliability: clean data, defensible analytics, and explainable AI that improves — rather than obscures — decision-making.

Execution, however, will remain the differentiator. 

In 2026 the businesses that outperform will be those that treat reliability, risk architecture, and human capability as core assets — designing hybrid workflows where AI, process, and people operate together with clarity, observability, and trust.

In the Real Deals article attached, part of the wider Due Diligence Report 2025, I explore these themes in more detail and offer a practical lens on how data and AI diligence particularly is evolving as an underpin for pricing confidence and operational feasibility.  I hope you find it thought-provoking and directly relevant to your investment and strategic priorities for the year ahead.

Thank you for your continued trust and collaboration. I am always happy to discuss any of these themes with you directly.

Wishing you and yours a joyous festive season, and Happy New Year!

Chelsea

DataDiligence 2025 Year in Review.pdf

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