This marks a clear shift. Organisations can no longer rely on speculative investment or scattergun implementation approaches. There was a time when momentum alone, fuelled by this escalating trend and a persuasive transformation pitch, was enough to secure budget and goodwill. Now, Investors and shareholders are equipped – and willing – to draw a much sharper distinction between AI hype and AI value.
“The questions being asked in boardrooms have shifted; it is no longer enough to say that AI is being explored or piloted. Stakeholders want to see what it has actually delivered.”
Guillaume Devaux Partner, Head of Technology, Media, Telecommunications, Forvis Mazars Group
Six months on from our initial 2026 C suite outlook, fresh findings from our global mid-year insights reinforce the confidence leaders have in AI’s potential and now reveal initial returns from AI investments. However, you can’t really value the overall returns without considering the scale of investment.
To what extent of ROI should leaders, their businesses (and their investors and stakeholders) be expecting?
None of this is to suggest that organisations should stall in their AI ambitions. The pace of innovation is not slowing; rather, it is accelerating exponentially and organisations that fail to embrace its potential risk falling behind. Being strategic about use case evaluation and definition, along with laying strong foundations in data and governance, will allow organisations to move quickly and generate real value as the technology continues to evolve.
AI reflects such a fundamental shift in operating models and priorities, touching so many different parts of the business, that it cannot be approached as a bolt-on technology. This means a solid foundation of data and governance is required, as is clarity around the business problem the technology is actually helping to solve.
The most mature organisations are not testing AI models in isolation. They are making sure they can govern and retain control of AI systems as they are deployed and connected to data, tools, workflows, users, third parties and business processes.
Sofia Ihsan AI Consulting Leader
In any use case, AI should be treated as a potential tool – albeit a very powerful one – for solving a strategic problem, rather than an end in itself. It is not a silver bullet, and approaching AI implementation with the sole priority of finding a way to leverage it is unlikely to yield real value and is likely to result in disproportionate, ineffective “solutions”. AI is one tool in a vast toolkit of technologies to leverage for any given business problem. The key lies in knowing when to reach for it, versus when something else will better solve the business problem at hand.
However, simply identifying a business problem AI can solve is not enough. Effective, value-generating implementations require clarity of purpose, proportionality of response and a relentless focus on outcomes that matter.
Valuable use cases for AI implementation are those that address real business problems and present with quantifiable success metrics.
Not every business challenge requires AI. Organisations generating the greatest value start by identifying the problems AI is best placed to solve and defining what success looks like from the outset.
The most successful AI initiatives are built around clearly defined outcomes, whether that is improving efficiency, enhancing decision-making, reducing risk or creating a better customer experience.
AI is an extreme solution and should be proportionate to the business value of the problem it is solving. Leaders should resist the urge to crack a nut with a sledgehammer; if a rules-based automation, a process redesign or a better-built dashboard will do the job just as effectively, that is usually a better answer than AI implementation. Environmental concerns and data quality and availability should also be weighed at this stage, before any commitment is made.
Indeed, sustainability concerns belong at the scoping stage rather than as an afterthought. Quantifying an AI footprint is difficult, even with your own GPUs, and AI is established to be environmentally damaging – this is especially true for agentic AI systems. It is important to consider sustainability ambitions, goals and compliance early, because there may be equally effective (or nearly as effective) solutions that are less environmentally taxing. Non-AI solutions can often get you most of the way there without the same sustainability (or even monetary) cost.
If AI is the right tool, the next question is what type. The benefits and challenges vary significantly across chat interfaces, retrieval-augmented generation, agentic systems and multi-agent architectures.
Task-level AI is often a waste of potential; one use case's leveraging of AI can frequently enable another, and thinking in terms of capability ecosystems rather than isolated tasks tends to yield better returns. In this way, one implementation (or coordinated implementation) can help address multiple business problems, or a single business problem with multiple contributing factors across different areas of the business.
 | “The organisations getting real value from AI aren't thinking task by task – they're thinking in capability ecosystems. One well-designed implementation can address several business problems at once, but only if you're willing to step back from the immediate use case and look at the wider operating model.” – Laurent Inard, Partner, Head of Research & Development, Forvis Mazars in France |
Established LLMs (versus proprietary models) are the most common design choice; they will usually be more robust and efficient and will be kept up to date with the right capabilities. They do, however, come with risks and requirements that must be considered, including heightened third party risk management (TPRM) considerations. Borrowed models do not reduce the maturity required for governance and oversight; if anything, they increase it.
One of the biggest hurdles for organisations in moving from “capability talk” to “performance talk” is defining the wrong success metrics for implementation. Every use case’s KPIs should be tied back to the strategic problem being solved and demonstrate real business value in their articulation. Quantifiable value like reduced turnaround time, improved exception rates and shortened value generation cycles will always be better than productivity, milestone or vague “efficiency” metrics.
Even if a use case isn’t performing against the relevant success metrics, in most instances, it should be operational without AI. If the process is poorly defined or ineffectual as is, adding AI will only exacerbate those failings, not solve them.
“Human-in-the-loop decision-making is key. AI is delivering clear value for corporate tax teams, but the real value of AI lies in what you do with the insight it gives you and the time saved in the process.”
Catherine Hall Partner, International Tax Lead, Forvis Mazars in the UK
Successful AI adoption requires more than the right technology. Organisations need clear governance, trusted data and defined processes to ensure AI is used responsibly and delivers consistent results.
Organisations must align their AI ambitions with their actual level of data maturity, as premature or misaligned implementations will generate little to no value, even if “successfully” implemented.
“Organisations tie themselves up in knots because they don’t have right-size governance in place. They’re governing every use case the same way, regardless of the risk profile or the opportunity for value. This is when governance becomes a blocker to value generation.”
Sofia Ihsan AI Consulting Leader, Forvis Mazars in the UK
Maturity alignment also matters: ambitions should be aligned to the organisation's current readiness across data integration, architecture and workforce skills, not to where leadership wishes the organisation were. If ambitions and maturity don’t align in reality, that signals the need for deep transformation prior to implementation.
To move from ambition to action, a "workflow contract" should be established for each use case. This contract sets out five key elements:
| Workflow boundary – identifying exactly where the process starts and ends. |
| Outcome metric – defining the specific, measurable change expected. |
| Decision boundary – explicitly stating what the AI can do autonomously, versus what requires human review. |
| Data boundary – specifying which data the system is permitted to use, and what is prohibited. |
| Project owner – assigning a specific leader who is accountable for adoption and results. |
Data readiness underpins all of this. Leaders must align their ambitions with the reality of their maturity along a practical maturity continuum, or their AI implementations are likely to fail.
At best, premature or misaligned implementations will generate little to no value, even if “successfully” implemented.
Security and compliance should be embedded into AI initiatives from the start, not applied as a final review gate.
As AI regulation evolves, organisations need repeatable governance approaches, clear data and audit standards, and proportionate cybersecurity measures aligned to risk.
Just as importantly, they should define ownership, monitoring processes and clear fallback procedures before deployment, ensuring continuity if AI quality drops or a model behaves inappropriately.
Organisations seeing the greatest value from AI use it to enhance workforce efficiency and support growth, rather than replace employees.
Clear communication is essential. Employees are more likely to embrace AI when they understand how it supports their role, contributes to business objectives and creates new opportunities. Visible results, ongoing education and clear governance help build confidence and encourage wider adoption.
Successful AI adoption starts at the top. Leaders who actively use AI, communicate openly about its opportunities and limitations, and encourage responsible experimentation create the conditions for lasting change.
The tone set by leadership shapes how employees perceive, adopt and engage with AI across the organisation.
The main risk isn’t doing too little – it’s doing too much without delivering impact.
Treating AI like a traditional IT investment is a mistake. Value is progressive, linked to adoption, and uneven across use cases.
Chasing the latest AI trend without strong foundations will increase fragility rather than value.
Ensure the organisation is pursuing the right AI opportunities, managing risks appropriately and creating long-term value.
For organisations who have either delayed AI implementation or seen little value return from their use cases, how can they pivot quickly, establishing the right foundation without losing ground?
The good news is that, while true transformation takes time, there are ways to pivot current AI endeavours effectively:
*Footage from our NED Series events
Artificial intelligence (AI) adoption is accelerating rapidly, driving investment, transforming business operations and facing increasing regulatory scrutiny across industries and global markets.
Digital transformation strategy at the frontier of growth.
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