Dear colleagues,

This week, while facilitating FP&A Board meetings in Germany, much of the discussion around AI came back to a very practical tension: technology can move faster than the organisation around it.

According to our 2026 FP&A Trends Survey, 98% of respondents expect AI to affect their FP&A role, yet only 19% of organisations have formal AI policies in place and followed.

That gap becomes important the moment an experiment starts to work. A finance team may be able to build something quickly, but then come the harder questions: who maintains it, who governs the data and logic, who monitors the output, and who remains accountable when assumptions, systems or business priorities change?

In this Digest issue, we feature a practical guide to the build-or-buy decision in AI adoption, looking beyond cost and capability to the organisational conditions behind the choice. Alongside it, the selected articles look at the decisions around that choice: how to get the scope right before vendor discussions begin, how to test the business case beyond headcount savings, and how to set decision rights once AI begins to influence management action.

Once AI moves beyond the pilot, the real decision becomes an operating-model choice.

Warm regards,

Larysa Melnychuk

CEO and Founder at FP&A Trends Group

Every finance leader has been asked the same question this year: "Why can’t we just build our own AI?"

According to Gartner, 91% of finance AI pilots deliver low or moderate impact. Most teams make the build or buy decision based on capability and cost. But the best decision for an organization depends on governance, security, and the ongoing maintenance that is needed to scale AI.

Download this framework to get:

• A 5-signal evaluation model covering technical capacity, finance willingness, time to value, growth trajectory, and audit risk posture.
• A 2-minute self-check scorecard that tells you whether build, buy, or hybrid is the defensible path.
• A market comparison matrix across 5 approaches, from full homegrown build to purpose-built platform.

Download Now →

Vendor evaluation is too late to start defining the problem. Tamer Abomosalam argues that FP&A should first be clear on the use case, scope, operating model and expected value. That gives Finance something concrete to evaluate vendors against, rather than choosing technology before the work itself is properly defined.

Read why good AI scoping starts before vendor evaluation →

Headcount savings are easy to put into an AI business case. The harder part is accounting for the value AI may create, the review it still needs and the cost of keeping it relevant as the business changes. Lana Ilchenko shows why Finance needs to look beyond labour savings when judging the economics.

Read what an AI business case should account for beyond headcount →

Building or buying the technology does not answer who can act on its output. Andreas Seufert looks at the decision rights, boundaries and escalation points needed when AI starts influencing management action, and where accountability still has to remain human.

Read the governance layer that sits after AI deployment →

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The upcoming sessions take this discussion further, looking at how AI changes the way FP&A creates value, supports decisions and works with the business.

As AI creates more analytical capacity, the question is what FP&A does with it. This session looks at how Business Partners can enter decisions earlier, challenge assumptions, frame better options and bring the judgement and influence technology cannot replace.

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Fast automation does not automatically create finance value. This session examines how FP&A can choose the right AI use cases, assess ROI, add financial context and connect new capabilities to the wider finance ecosystem.

Register now →