Workday: The Evolution of Using AI in the M&A Process

Mergers and acquisitions come with tedious piles of paperwork. Dealing with fragmented operations systems proves to be a constant headache.  

In the UK, a striking 79% of finance respondents report that teams frequently disagree over whose numbers are correct, while 91% admit to spending significant time translating data between disconnected systems. 

During high-stakes merger and acquisition (M&A) integrations, these fractured information flows severely hinder agility, forcing finance professionals to act as manual bridges rather than strategic advisors.

The question on every professional’s mind: is AI the answer? 

Far from replacing human insight, AI serves as an intelligent partner – automating complex reconciliations, highlighting anomalies and surfacing variances in real time. Crucially, algorithms cannot assume accountability or evaluate strategic risk; human gatekeepers remain indispensable for high-stakes decision-making.

To truly unlock value and accelerate post-merger integrations, CFOs must prioritise building a secure, connected data foundation first.

By embedding AI directly into trusted workflows, finance functions can finally shift away from reactive reporting towards early intervention, disciplined execution and long-term value creation.

As Vice President of Product Management for EMEA and APJ at Workday, Tim Wakeford drives the strategic direction of financial products across the region. A Fellow of the Chartered Institute of Public Finance and Accountancy, he draws on extensive leadership experience, including serving as UK CFO at Cushman & Wakefield. 

In an exclusive interview with Finance Chief, Tim explains how, in his experience having managed complex private and public-sector operations, M&A is not as complicated as it needs to be with the introduction of AI.  

What is a unified finance platform and how has it evolved in the last few years?

A unified finance platform connects the core work of finance in one environment: financial management, planning, procurement, reporting and the workforce context that influences financial outcomes.

Instead of relying on separate systems and spreadsheets that need to be constantly reconciled, finance teams can work from common data, processes, controls and definitions.

The scale of that challenge is clear in our latest research.

51% of UK finance respondents say their organisation mostly works around core systems, while 79% say different teams often disagree about whose numbers are correct. That’s the operational cost of fragmentation – finance spends too much time reconciling information before it can act on it.

The platforms we can leverage as finance leaders have evolved.

In years past, a finance platform was limited to just a system of record that captured transactions and produced reports. Today, AI platforms are actively assisting the team, shifting from passive reporting to highlighting anomalies and automating complex reconciliations before humans even look at them.

But the key is governance. General-purpose AI tools lack the financial context, security, controls and audit trails required to support high-stakes decisions.

AI only becomes transformational in finance when it is designed to operate within established workflows and permissions.

What are some common issues with an M&A that finance leaders can run into?

The hard part of M&A is rarely limited to just signing the deal. Creating one reliable operating model is equally hard.

Finance leaders often inherit different charts of accounts, planning models, approval structures, reporting definitions and financial processes.

Data may sit in disconnected systems, leaving integration teams to spend critical weeks or months reconciling information, rebuilding reports and determining which numbers are reliable. That slows decision-making at exactly the point when leaders need visibility into cash, spend, performance and risk.

It reflects a wider challenge for the finance function.

In the UK, 91% of finance respondents say they spend significant time coordinating or translating work between teams or business systems. During an integration, that burden becomes even more acute: finance can end up acting as the manual connection point between two organisations.

Controls can also come under pressure.

New entities, processes and regulatory requirements add complexity to close, audit and compliance activities, while the business expects finance to demonstrate that the deal is delivering its intended value.

The strategic priority is to establish a trusted, connected foundation early, so finance can focus on accelerating integration and value creation rather than repairing fragmented information flows.

How are those hurdles best mitigated with AI versus ‘old-fashioned’ human methods?

We don’t want to frame this as AI versus people. The better model is AI and finance professionals working on what each does best.

Traditionally, finance has depended heavily on manual coordination: extracting data, reconciling differences, chasing approvals and updating reports.

These activities will always be necessary, but they consume time that finance teams could be applying to risk assessment, scenario planning and strategic decision-making.

AI can support defined, repeatable parts of that work, such as identifying exceptions, preparing reconciliations for review, explaining variances and routing issues to the right person.

That leaves finance teams the capacity to concentrate on judgement, oversight and exceptions rather than acting as the glue between disconnected systems.

However, this only works when AI is embedded in trusted finance processes.

In high-stakes activities, it needs access to the relevant financial context and must operate within established permissions, controls and audit trails. That is how AI can help finance move faster without weakening governance.

Will finance leaders look to AI to replace human counterparts in the M&A process?

No one is looking to clear out the finance department.

The opportunity with AI is to take pressure off of the work that slows them down and give finance the capacity to re-focus on value creation. 

M&A involves judgement that AI cannot assume: assessing strategic risk, interpreting the operational implications of a deal, setting priorities and making difficult post-merger trade-offs. And AI can support the process by spotting anomalies, preparing information for review and flagging integration risks or changes that need attention.

Ultimately, a machine cannot carry regulatory or strategic accountability.

You still need human experts to act as the final gatekeepers and own the final decisions.

What is a ‘single source of truth’ and how can CFOs aim to hold that philosophy close?

A single source of truth means the organisation works from a shared, trusted view of its financial and operational reality.

That’s not to say that every decision is simple and automated. But it means that people are not wasting time debating which spreadsheet, report, or data extract is correct before they can begin to make the decision.

There is a clear, trusted view for the team to work from. 

For a CFO, this requires common data definitions, connected planning and actuals, clear ownership and controls that carry across the finance process.

During M&A, this is particularly important because the acquirer and target may each have different reporting structures, assumptions and ways of managing performance.

AI can make this foundation more useful by helping finance surface variances, track changes and identify risks more quickly. But AI cannot create trust on top of inconsistent data or fragmented processes.

First establish a secure and governed source of truth and then use AI to turn it into faster insight and action. 

Is there a productivity tax for using AI?

Yes, and it’s something we see organisations struggle with frequently.

Especially when a tech stack becomes a number of disconnected tools that employees have to learn, govern, check and reconcile with the systems they already use. In that situation, an organisation may speed up isolated tasks while adding a new layer of work elsewhere.

The alternative is to integrate AI in the flow of finance work.

When AI agents operate within the systems that manage financial data, plans, approvals and controls, they can reduce handoffs, duplication and manual coordination. When AI is integrated thoughtfully, you both enable teams with greater speed and remove friction from the end-to-end process.

The research suggests finance is already seeing some of those gains.

60% of UK finance respondents say AI has reduced task time. However, only 41% say it has mainly accelerated work in a productive way. That gap matters: faster individual tasks do not automatically create a more connected, effective finance function.

The lesson is that AI needs reliable data, business context and clear controls. Without those foundations, employees can end up checking one tool against another. With them, AI can help finance shift from reporting and reacting towards earlier intervention and better-informed action.

Could AI form part of a central strategy during the M&A process in the next five years?

Yes. I expect AI to become a core part of M&A integration strategy, rather than a side project or a set of productivity experiments.

There is still a significant gap between ambition and adoption.

Only 20% of UK finance respondents say AI is deeply embedded in their organisation’s core systems for managing people, money and plans. 

The most valuable applications will be practical and tied to business outcomes.

AI agents can help bring together financial and operational data, monitor integration milestones, identify potential control gaps, accelerate close activities and track whether the expected value of a deal is being realised. In other words, AI can help make integration more visible, more disciplined and more responsive.

The teams that lead the way will be those that have AI embedded in the trusted systems where finance decisions are made and governed.

Organisations that combine AI with reliable data, clear workflows, security and human oversight will be better placed to integrate faster while maintaining confidence in their numbers.

That’s the promise of agentic finance: AI as a governed teammate that helps finance deliver outcomes. 

What should a CFO do first to prepare for AI-enabled M&A?

The first step is to identify where integration depends on people manually connecting systems, reconciling data, or chasing information. Those are often the places where the business is losing the most time and where controls are most likely to be strained.

CFOs should then prioritise a small number of high-value workflows, such as financial close, reconciliation, audit preparation, scenario planning or synergy tracking.

For each workflow, define the business outcome, the trusted data source, the control requirements and where human judgment must remain in the loop. 

Organisations should not try to automate everything overnight. The goal is to build a connected, governed foundation and apply AI first where it can safely reduce repetitive work. That gives finance teams more capacity to focus on judgement, accountability and value creation.