Author: IRPA AI Senior Analyst, Kieran Gilmurray
This article explores how CFOs and COOs should evaluate artificial intelligence investments in 2026 as organisations move from experimentation to measurable business results. After years of pilots and tool testing, many initiatives still lack clear economic value, prompting boards to demand stronger financial discipline around AI spending.
After reading this article, you will understand how to assess AI initiatives through both financial and operational lenses, identify projects capable of delivering measurable returns, and avoid the execution failures that prevent pilots from reaching production.
Introduction: The Shift from AI Hype to Financial Discipline
Surveys of finance leaders show that many artificial intelligence initiatives have struggled to produce clear business value. Industry estimates suggest that only a small share of generative AI projects have achieved their intended objectives, prompting finance leaders to tighten approval standards and demand stronger evidence of expected returns before committing additional funding.
Operational leaders face a related challenge. AI tools promise efficiency improvements and automation, but deploying them across real workflows often proves harder than running controlled pilots. Data quality, fragmented systems, and workforce readiness frequently determine whether projects succeed once they move beyond experimentation.
These pressures have made AI investment decisions a shared responsibility between finance and operations leadership. CFOs must ensure capital is allocated to projects capable of generating measurable returns, while COOs must determine whether those initiatives can actually be implemented successfully within existing operational environments.
The CFO–COO Decision Problem
For CFOs the primary concern is financial discipline. AI projects require investment in technology, infrastructure, and training, yet their benefits can be uncertain or delayed. Finance leaders therefore demand clear ROI projections, defined success metrics, and realistic timelines before approving large initiatives.
COOs evaluate the same initiatives through an operational lens. Their focus is whether AI will meaningfully improve operational performance and integrate with real workflows. Even projects with strong theoretical ROI can fail if they depend on unavailable data, disrupt established processes, or require skills the organisation does not yet possess.
Effective decision making requires combining these perspectives rather than treating them separately. Financial evaluation alone cannot determine success if operational feasibility is ignored, and operational enthusiasm cannot justify investments that lack credible financial returns.
Many organisations are now building joint evaluation frameworks that weigh both factors simultaneously. These frameworks typically assess financial return, operational readiness, implementation complexity, and regulatory exposure before an AI initiative proceeds beyond the pilot stage.
The AI Investment Reality in 2026
The past year of intense AI experimentation has produced mixed results. Adoption has increased rapidly across many industries, yet measurable value has often lagged behind early expectations.
Survey data suggests that many organisations continue to experiment with AI while struggling to translate pilots into scaled operational capabilities. In practice, the challenge is rarely access to models or software. The real barriers tend to involve data readiness, workflow integration, and workforce capability.
This gap between experimentation and execution explains why many finance leaders have begun raising approval standards for AI spending. Rather than approving projects based solely on technical promise, CFOs increasingly require evidence that initiatives will generate concrete operational or financial improvements.
Operations leaders face a parallel pressure. They are responsible for ensuring that AI systems integrate into day to day processes without disrupting reliability, compliance, or customer experience. This operational responsibility means AI decisions must be grounded in practical feasibility rather than theoretical efficiency gains.
Aligning AI Investments with Strategic Value
The first step in evaluating AI investments is ensuring that each proposal aligns with a clear business objective. Projects that cannot be linked directly to strategic priorities often struggle to maintain executive support once budgets tighten.
Effective organisations therefore begin by asking a simple question: what measurable outcome should this AI investment improve?
In finance functions, common objectives include reducing manual processing time, improving forecasting accuracy, or identifying cost leakage. In operations, objectives often focus on throughput, cycle time reduction, scheduling optimisation, or quality improvement.
Defining the objective early makes it easier to evaluate the return on AI investments. It also ensures that projects remain focused on business outcomes rather than technology experimentation. When proposals lack this clarity, leaders risk funding investments that generate activity without delivering meaningful impact.
The CFO–COO Evaluation Framework
A disciplined evaluation framework helps organisations compare AI initiatives consistently. While each organisation will adapt the details, most frameworks assess four core dimensions.
Financial return remains the most visible factor. Leaders must estimate potential cost savings, revenue impact, or productivity improvements and compare them against expected implementation and operating costs.
Operational readiness determines whether the organisation can actually implement the solution. This includes assessing data availability, system integration complexity, workforce skills, and process maturity.
Implementation complexity measures how difficult the project will be to deploy and maintain. Projects requiring major infrastructure upgrades or organisational redesign may carry higher risk even if potential returns appear attractive.
Regulatory exposure considers whether the initiative introduces compliance or legal risk. Different jurisdictions apply different rules to AI use in areas such as financial services, employment decisions, and customer interactions. Evaluating projects across these dimensions provides a clearer picture than relying on financial projections alone.
Quick Wins and Long Term Capabilities
In practice, successful organisations pursue a combination of short term efficiency improvements and longer term capability building.
Short term projects typically focus on automating repetitive processes or applying AI features embedded in existing enterprise software platforms. Examples include invoice auditing, automated reporting, and customer service assistance. These initiatives often deliver measurable value quickly because they build on existing workflows and data sources.
At the same time, organisations invest in longer term foundations such as data infrastructure, governance frameworks, and workforce training. These investments may not produce immediate financial returns, but they enable more ambitious AI initiatives in the future.
Balancing quick wins with capability development allows organisations to demonstrate early success while building sustainable competitive advantage.
Governance, Risk and Compliance
AI deployment introduces new governance responsibilities for both finance and operations leaders.
Data protection and cybersecurity remain central concerns. Many AI tools require access to large volumes of operational or customer data, increasing the importance of strict access controls and monitoring.
Regulatory requirements also differ across regions. The European Union AI Act introduces risk based obligations that affect systems used in finance, employment, and customer decision making. The United Kingdom relies on sector regulators such as the Financial Conduct Authority to oversee algorithmic decision making in financial services.
In the United States the regulatory environment remains more fragmented. Several states have introduced AI related legislation, while federal authorities continue to develop guidance through agencies such as the Federal Trade Commission.
Because of this complexity, organisations increasingly treat governance as a core design requirement rather than an afterthought. Effective governance frameworks typically include documentation, oversight structures, testing protocols, and audit trails for AI driven decisions.
Examples of AI Investment Outcomes
Recent case studies illustrate both the potential benefits and the risks associated with AI Investments. Some organisations have achieved measurable cost savings by deploying AI in narrowly defined processes such as invoice auditing or procurement analysis.
In one example, a biotechnology company implemented an AI system that continuously monitored invoices against contractual terms. The system identified pricing discrepancies and contract violations that human teams had missed, enabling the company to recover several percentage points of spending across its supplier base.
Another example comes from the finance software industry, where AI-assisted development tools significantly increased research and development productivity. Teams using these tools reported faster coding cycles and quicker debugging, allowing organisations to deliver new features more rapidly.
However, not all deployments have been successful. A widely reported incident involved a consulting firm delivering a report containing factual errors attributed to AI-generated content. The firm ultimately refunded part of the engagement fee, highlighting the reputational and financial risks associated with insufficient oversight.
These cases illustrate why disciplined evaluation and governance are necessary. AI systems can deliver strong returns when deployed thoughtfully, but poorly managed projects may generate costs, operational disruption, or reputational damage.
Practical Guidance for CFOs and COOs
Senior leaders seeking to evaluate AI Investments can apply several practical steps.
Begin by mapping current AI activity across the organisation. This includes both formal projects and informal tool usage that may already influence workflows.
Define the business objective for each proposed initiative and identify measurable metrics that indicate success. Clear baselines help determine whether improvements actually occur after deployment.
Evaluate projects using a structured framework that considers financial return, operational readiness, implementation complexity, and regulatory exposure.
Prioritise initiatives that offer measurable value while requiring minimal organisational disruption. Early successes build credibility and generate learning that supports more ambitious deployments.
Implement governance and oversight mechanisms at the outset. Human review processes, security controls, and compliance checks reduce the risk of costly errors or regulatory issues.
Finally, measure outcomes continuously and adjust the portfolio accordingly. Successful projects can be scaled across additional departments, while underperforming initiatives should be revised or discontinued.
Conclusion
Artificial intelligence has moved beyond experimentation into a phase where financial discipline and operational execution determine success.
For CFOs and COOs the challenge is not simply adopting AI but deciding which initiatives deserve investment. Projects must demonstrate credible financial return, operational feasibility, and governance readiness.
Organisations that apply structured evaluation frameworks, prioritise high impact use cases, and implement strong oversight are more likely to translate AI capabilities into measurable business results. Those that fail to apply the same rigor to AI investments as they do to other strategic initiatives risk spending heavily without achieving meaningful outcomes.
About the Author: Kieran Gilmurray
Kieran is a globally recognized authority on AI, automation, and digital transformation, having authored multiple influential books and hundreds of articles that have earned him prestigious accolades, including being named a Top 50 Global Thought Leader and Influencer on Generative AI in 2024, a Best LinkedIn Influencer for AI and Marketing, Top 50 Global Thought Leaders and Influencers on Manufacturing 2024, Top 14 people to follow in data and one of the World’s Top 200 Business and Technology Innovators.
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Originally posted on 2025-01-28 in the IRPA AI Network — Announcements & Updates