2026 is the year enterprises prove AI’s value

Almost every CEO is tasking their leadership to implement AI, but so far, most companies have focused on rushing to adopt out-of-the-box tools before understanding what they want to achieve. It may sound simple, but leaders must first know what challenge they want to solve, rather than trying to retrofit a tool to that use case. Consequently, many have yet to see the payout of their investment. With failure rates for enterprise AI projects reported as high as 95%, and organisations we know of claiming an 80% success rate, the truth must lie somewhere in the middle.

In 2026, that narrative will flip. Organisations will move from hype-driven adoption to strategies that prioritise measurable outcomes, structured experimentation, and governance that enables scale. Here are four predictions for the year ahead.

1. ROI becomes the non-negotiable benchmark

Throwing money at AI just won’t cut it in 2026. Boards will demand clear evidence of impact, whether that’s revenue growth, operational efficiency, or improved customer experience. Technology conversations will shift from “what’s in the stack” to “what outcomes does it deliver?” And the same lens will be applied to AI investments.

This means leaders will need to start with defined objectives and link AI initiatives directly to business performance. However, ROI must move beyond cost-cutting to include performance improvements and risk reduction. Organisations will expect AI projects to demonstrate tangible value quickly, and those that fail to do so will be stopped or reprioritised.

A critical enabler will be building a common data environment (CDE) for each priority use case. A CDE creates a single, consistent view of project files and structured data, preventing AI tools from making incorrect assumptions due to fragmented inputs. By aligning workflows and centralising relevant datasets, organisations can safely and accurately test and train models, delivering outputs that stand up to scrutiny.

2. Use-case-first strategies replace tool-first thinking

Some organisations we speak to have deployed AI tools in their hundreds. While this can fuel experimentation in some cases, it can lead to a huge money drain. With AI ROI the priority for 2026, the days of retrofitting generic tools to vague problems are numbered. Instead, success will depend on use-case clarity. Leaders will start by articulating the specific challenge AI should solve, rather than selecting a tool and trying to make it fit the business.

Practically, that means beginning with a defined outcome, such as revenue growth or operational efficiency, scoping the data environment required for that use case, and developing proprietary systems tailored to the organisation’s processes. Off-the-shelf tools that offer transcription or summarisation features will continue to deliver quick wins, but sustainable, long-term value will come from solutions built around the organisation’s unique data landscape and objectives.

Critically, this won’t be achieved by the CEO or IT team alone. The most effective AI initiatives will be driven by stakeholders who understand the challenge space and can work with analysts to integrate the AI solution into everyday workflows.

3. Governance and structured experimentation unlock scale

In 2026, AI won’t be treated as a black box where data goes in, and magic comes out. Enterprises will normalise structured, low-risk experimentation. Pilots will be kept siloed from core systems so teams can test safely, evaluate outcomes, and decide what’s ready to scale into business-as-usual.  If the use case can’t be proven in the first three to six months, it should be cut. Having such a stringent approach will allow organisations to analyse results, pinpoint improvements, and avoid committing to projects that can’t demonstrate value.

This evolution also hinges on governance and cultural readiness. Organisations will need to embed guardrails that enable safe experimentation while maintaining compliance and trust, particularly as data sovereignty rules tighten under legislation such as the EU Data Act. In 2026, businesses will recognise that change management is just as important as technology integration for implementing meaningful improvements. This will include equipping their employees to work confidently with AI, and ensuring people know how to use, review, interrogate and escalate outputs so pilots can become a repeatable practice or be put to one side.

Partnerships will also play a pivotal role here when organisations don’t have the right skillsets in-house to build and manage proprietary AI tools, as well as bring their team on the journey. Working with experienced providers will accelerate adoption by bringing proven approaches and technical expertise to the table. Crucially, however, these partners won’t replace in-house talent. They’ll help organisations roadmap for what they’re looking to achieve, whether that’s cost savings or go-to-market objectives.

This will start with the question, ‘Where are we today?’, including reviewing and optimising the data and infrastructure environment, and on to ‘Where do we want to be tomorrow and beyond’, evaluating what’s needed to future-proof the business. This pragmatic step-by-step approach will minimise disruption and ensure sustainable change as firms can operationalise governance, integrate AI into processes, and ensure that the workforce can work productively and safely with AI.

4. The final takeaway

2026 will be the year that enterprises flip the narrative on AI, from rushing to adopt tools to proving outcomes through proprietary technology. Those that start with ROI-anchored objectives, adopt a use-case-first mindset, and combine structured experimentation with governance and stakeholder leadership will move AI from pilot to true performance.