Who should own your business’ AI transformation strategy? It’s one of the questions every business starts with when setting out their AI roadmap.
The obvious candidate to take on the mantle is the Chief Information Officer (CIO). After all, AI is technology. So who better to define the strategic vision and oversee the plan for AI implementation, data management, system integration, and risk mitigation than the CIO?
But AI success doesn’t just depend on operationalizing the technology. There’s also a financial strategy to realizing a return on AI (RoAI).
So rather than putting the ball in the court of just one member of the C-suite, an effective AI strategy should be a cross-functional, collaborative team sport. The CIO and Chief Financial Officer (CFO) make an unbeatable doubles pairing, covering both the execution and the outcomes of AI for the business. Here’s why their partnership is so important.
Why AI can’t be owned by IT alone
To illustrate the importance of CIO and CFO alignment, let’s look at how IT-led AI transformation typically plays out.
Picture the scene: IT has been instructed to deliver business operational improvement using AI. First, the team researches AI vendors and compares solutions – or opts to develop their own AI solutions. IT then kicks off AI pilot projects to understand the feasibility and scale of the opportunity. Depending on the results, the IT team will either further develop the AI solution or deploy it in the business. After project hand-off, IT will only be involved for troubleshooting, support, and maintenance.
The issue stems from businesses mandating AI pilots without enough collaborative consideration first. Without collective insights from wider business functions, and a robust understanding of what the business wants the AI technology to accomplish, investment can easily be poured into AI solutions that only deliver generic efficiency gains. Classic examples are customer service chatbots, or automating workflows that don’t directly impact business-critical metrics. AI adoption can also stall when business users don’t see the value.
When it comes to pursuing enterprise-wide AI automation, meanwhile, there’s a risk that CIOs remove humans from processes that benefit from their involvement, such as interactions with suppliers and customers. Rather than driving continuous business improvement across the organization, IT teams can end up implementing AI solutions that don't deliver measurable benefits for decision-making and operational excellence. Deployment itself can also overtake other measures of AI success.
Post-deployment, CIOs might monitor statistics for technical issues and errors related to each AI solution. But without enough collaboration between IT and the broader enterprise, it’s hard to accurately quantify the value achieved, to draw insights for rolling out the AI solutions to other use cases and parts of the business, and to understand which types of AI technology (assistants, copilots, or agents) merit further development.
In short: When AI is owned by IT alone, it becomes a technical project instead of a business transformation effort. That explains why 69% of IT leaders say they’ve implemented AI solutions, but the business value has fallen short of expectations, according to our 2026 Process Optimization Report.
The financial accountability challenge in AI transformation
What’s missing from an IT-led AI transformation strategy is defined success criteria from the start, including direct insight into how the business quantifies value for AI initiatives.
The cost of AI experimentation can be a source of friction between Finance and IT or Operations. Establishing which AI solutions work best for the business, let alone building specialized agentic AI systems, often requires substantial upfront costs. It’s essential to bring wider stakeholders onboard with the vision and technical AI roadmap to justify the expense. IT teams can’t do it alone.
The potential value of each opportunity for AI and automation needs to be quantified so the business can prioritize initiatives according to wider strategic goals. Otherwise businesses can waste development costs and CapEx, jeopardizing ongoing funding for the AI transformation strategy.
There’s also a danger of stakeholders treating AI as a golden goose that lays its eggs consistently and predictably. While some AI initiatives might target “quick win” opportunities (automatically triaging and classifying customer enquiries, for example), others generate incremental long-term value. These include monitoring equipment for predictive maintenance, or mobilizing alternative procurement flows during supply chain volatility.
Then there are AI use cases that don’t generate easily identifiable financial gains. Clear savings can be attributed to AI detecting duplicate invoices and maverick buying, for example, but ROI is less obvious for AI deployment outside the Finance department – improving customer satisfaction through on-time delivery, for instance. In all these cases, without aligning Finance and IT, an AI project can come under fire for slow or indirect ROI if the right expectation hasn’t been set.
So what’s the solution for an effective AI transformation strategy? A different, hybrid ownership model. One that partners the CFO and the CIO throughout the project, keeping each other apprised of priorities, expectations, and limitations.
How joint leadership drives better outcomes and faster ROI
When CIOs and CFOs join forces, an AI transformation strategy isn’t just easier to execute, it also generates faster, greater value for the business. Rather than working in competing, siloed factions with different agendas, each department drives in the same direction across every phase of AI development – from planning through implementation and evaluation.
CFOs can help CIOs understand, plan, and maximize the available business resources by concentrating them on high-impact projects, rather than diluting spend across too many simultaneous AI initiatives or burning through funds too quickly. It’s the ideal combo of the CFO’s financial discipline and the CIO’s knowledge of technical feasibility. As a result, CFOs can ensure AI transformation scales at the right level and pace for the business, supporting growth rather than inhibiting it.
Here’s how it looks in practice. Before any implementation begins, the AI project’s joint leadership defines and aligns on metrics that contribute to wider business objectives. This means IT chooses the right projects that impact the priority business improvement metrics and objectives for that quarter or financial year. By pulling the right levers of growth, it’s easier to demonstrate the value of AI capabilities and secure ongoing funding.
Finance departments can also make a more holistic ROI assessment of an AI solution’s value to the business, considering cost, adoption, impact, and time to value. This builds a more precise and compelling business case. And the joint leadership of CIOs and CFOs creates stronger AI governance with more internal champions – improving business-wide adoption, adherence, and change management.
With shared priorities, metrics, and governance, joint leadership between IT and Finance helps the enterprise get maximum bang for its AI development buck.
How Celonis supports both financial and technical KPIs
CIO and CFO alignment can prove the deciding factor in whether an AI strategy transforms the enterprise or just nudges the dial in a few business functions. But what can really make a difference is having the power of Celonis behind an AI transformation strategy.
The Celonis Platform supports both CIO-level tech architecture and CFO-level impact-tracking. It does that by providing a clear and robust foundation for understanding and improving business-critical operations.
Powered by the Celonis Context Model (CCM) – the heart of the platform – Celonis works through three core steps:
First, it extracts raw data from your business systems, applications, and devices. Then it augments this raw data with operational business context (process flows, enterprise architecture, and business performance data) – creating the intelligent foundation of the Context Model. The result: a dynamic, system-agnostic digital twin of your business operations.
The Celonis Context Model delivers hindsight (what happened and why), insight (what's happening now), and foresight (what should happen next through AI predictions and recommendations). This unified operational context creates end-to-end transparency throughout the organization.
What does this mean for empowering CFOs and CIOs to drive successful AI transformation? By creating end-to-end transparency throughout the organization, the entire enterprise gains a common language for how operations run. All stakeholders are then aligned on a single source of truth from which to identify the financial and technical metrics most in need of improvement and AI intervention.
The benefits don’t stop once AI capabilities have been deployed. Businesses can monitor the live financial and technical metrics of each AI use case, with objective insights to clearly and accurately establish the value, enterprise-wide. Teams can also set up alerts that automatically notify them of any performance issues, so they can efficiently intervene and optimize to mitigate impact on KPIs.
Celonis provides a foolproof way of bridging technical execution and financial measurement.
Start making AI transformation a collective success story for your business
Several years on from AI’s breakthrough into the mainstream business world, it’s crunch time for the technology to start returning real ROI. But pulling that off is a collective team effort, rather than a burden for one single team to carry. With a joint leadership model, backed by Celonis, successful AI transformation is within reach. CFOs and CIOs: it’s over to you.
Find out more about what it takes to maximize Enterprise RoAI and support your cross-functional business transformation journey.