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AI in Finance: Three Conditions for Scaling Beyond the Pilot

Proof-of-concept AI initiatives are multiplying across finance functions, with tangible results to show for it. The real challenge has shifted: how to industrialize use cases at scale without compromising data reliability or team buy-in.

An Inflection Point for Finance Functions

AI in corporate finance has crossed a threshold. Across large industrial groups, early use cases are delivering measurable results, and the performance gap between pioneers and laggards is becoming quantifiable. The strategic question has shifted: no longer “should we act?” but “how do we scale without losing data integrity or people along the way?”

The finance functions making the fastest progress are those that have positioned AI as a lever for improving working conditions, rather than as yet another top-down transformation program. In a major materials group, the CFO set the tone from the outset: eliminate repetitive tasks to free up overstretched finance teams, give them space to step back and focus on higher-value analysis in service of the business. That framing unlocked team creativity: more than 50 use cases were identified within weeks, spanning the full breadth of finance activities.

A Concrete, Actionable AI Roadmap, with Proven Results on Flagship Use Cases

Automated contract analysis is a compelling first example. A group managing over 1,000 non-standard contracts across 30 typologies, with more than 200 new contracts per year, deployed a solution combining optical character recognition and AI to automatically extract key commercial terms. Previously, manual review covered only a sample; automated scanning now processes the entire contract portfolio. The results: an estimated 60–70% reduction in time spent on contract review; reading time focused exclusively on material clauses and risk areas; full contract coverage previously impossible given team size; and systematic identification of tax risks and potential penalties. The approach: start small, prove the value, then scale.

Second case: AI-powered strategic intelligence. Market share analysis, competitor financial health, sector M&A activity, target identification: analyses that previously required 4 to 8 person-weeks every two months are now completed in a matter of hours, in a repeatable, near-zero marginal cost process. AI does not replace the analyst; it gives back the time needed for interpretation and decision-making.

More broadly, our experience across other industrial clients confirms that agentic AI use cases represent a particularly powerful lever for operational transformation. Finance teams can now deploy configurable, no-code agents capable of processing complex Excel files and real-time market data to produce in-depth margin analyses by product, client and segment. In one instance, a task that took a controller three days per month is now completed in under three hours: 45 minutes of natural language Q&A with the agent, followed by two hours of review and adjustment. The rise of agentic AI is accelerating rapidly, and finance controllers are emerging as its frontline operational users.

Three Conditions for Successful Scaling

Field experience consistently points to three imperatives.

  1. Prioritize pragmatism and business relevance over technological ambition. It is essential to listen to business concerns and operational constraints, and to respond to them directly. Successful initiatives start small, demonstrate value within weeks, and then scale. A logic of cumulative quick wins consistently outperforms large integrated programs. Without this discipline around concrete use cases, AI projects become technology showcases with no measurable impact on performance.
  2. Make AI a human project. Team buy in is the single most important success factor. It is achieved when AI is perceived as a way to eliminate low-value tasks, not the people who perform them. Without that buy-in, projects stall in passive resistance: tools deployed but never used. ROI collapses, and leadership loses credibility for future initiatives.
  3. Bring IT in from day one. Data reliability remains the foundation. Without rigorous governance, including regular audits, quality controls and legal compliance, results remain fragile. IT is not a service provider to the project: it is a co-pilot. Overlooking this dimension means building on sand: models degrade, anomalies accumulate, and the credibility of the entire program is called into question.

A Question of Timing

The window of opportunity is open, but not indefinitely. Finance functions that do not begin their AI transformation in the coming quarters will face a structural disadvantage that will be difficult to reverse: in productivity, decision-making quality and talent attractiveness. The challenge is no longer technological. It is strategic, and it is now as much the CFO’s responsibility to drive it as the CIO’s.

Are you leading the finance function of an industrial group and thinking through your AI roadmap?

PMP Strategy works with CFOs and their teams to define operational AI strategies: identifying high-impact use cases, building data governance frameworks, managing change, and steering industrialization.

Drawing on hands-on experience with major industrial groups, we help finance functions move from pilot to scale, with rigor, pragmatism and measurable results.

We would welcome the opportunity to discuss your specific challenges. Contact us for a confidential initial conversation: contact@pmpstrategy.com

Alexis Sztejnhorn, Partner

Alexis Sztejnhorn is a Partner in PMP Strategy’s Finance & Performance practice.  He advises the finance functions of major international groups on strategic and operational transformation, combining analytical excellence with deep expertise in performance management and data & AI strategy. A trusted advisor to CFOs and their teams, he helps position the finance function as a genuine driver of value creation and strategic decision-making. He also teaches at ESSEC in the Master’s program in Corporate Strategy and Finance. Alexis holds a degree from the London School of Economics (LSE).

About PMP Strategy

PMP Strategy is an independent International strategic management consulting firm distinguished by Partners who bring C-level operational experience and combine deep sector expertise with strategic rigor to deliver tangible, lasting impact. 

For over twenty years, we have served as trusted advisors to executive committees and investors across North America, Europe, the Middle East, and Africa. We specialize in five key sectors where transformation is most critical: Telecoms, Media & Technology (TMT), Private Equity, Financial Institutions, Transport & Mobility, and Industry & Energy. Our Transversal Performance practice leads complex, cross-sector transformation programs, while our Innovation Lab—a dedicated team of AI experts—supports client engagements worldwide from our headquarters in Paris and our network of international offices. 

 Our approach is built on partnership—designing tailored strategies alongside clients and working hand in hand to drive implementation, delivering measurable results that evolve with their ambitions. Learn more at www.pmpstrategy.com

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