Emergn has launched Value-Driven AI Adoption, an advisory offering aimed at large organisations seeking measurable returns from artificial intelligence projects.
The service is designed to address the gap between companies' AI ambitions and the results many are seeing from pilot programmes. Emergn cited MIT research showing that 95% of enterprise generative AI pilots fail to deliver measurable return on investment.
Its own research pointed to problems in how AI programmes are run rather than in the underlying tools. In a survey of more than 750 enterprise leaders, 55% said they would not meet their AI goals without changes to talent, problem framing and outcome-led design.
The offering focuses on three areas: how AI investments are prioritised, how operating models are set up for broader use, and how organisations build internal skills to manage the work themselves.
Under the approach, clients are encouraged to move away from scattered pilot schemes and towards a smaller portfolio of use cases ranked against business outcomes. The process includes governance through an AI Board intended to help senior leaders decide which projects to fund and which to stop.
Organisations are also guided on setting up structures for AI deployment, including a centre of excellence, sandbox arrangements and governance frameworks. A further stage is designed to test one to three use cases, run an initial AI Board cycle and produce a roadmap for the following six to 18 months.
Emergn describes the problem it is trying to tackle as the "Intelligent Delusion", which it defines as the belief that AI tools alone will transform how a company works. It argues that operating models, governance and internal capability are the main constraints on successful adoption.
Alex Adamopoulos, Chairman and Chief Executive Officer of Emergn, outlined the company's view of the shift facing corporate AI programmes.
"AI is no longer about experiments - it's about results," Adamopoulos said.
"Boards expect systems, capabilities, and teams that can deliver measurable impact at scale. The winners will be the ones who turn AI into growth, with AI contribution margin as the new scoreboard. That's what Value-Driven AI Adoption is built to deliver."
Operating model
The offering is vendor- and model-agnostic, meaning it is intended to sit on top of the cloud, data and AI tools clients already use. The process begins with a one-week diagnostic covering executive interviews, a current-state assessment, a strategy heat map and the identification of three to five use cases.
That stage is followed by an operating model phase that typically takes between two and seven weeks. This work covers governance, value frameworks, AI leadership structures and sandbox strategies designed to support broader adoption.
The final stage is intended to validate value through an initial governance cycle and hand over reusable assets and practices to internal teams. The aim is for clients to own and extend the model rather than rely on outside advisers for ongoing execution.
The launch comes as many consulting firms and software providers try to position themselves around the difficulty companies face in moving from AI experimentation to routine day-to-day use. For many businesses, the challenge has shifted from testing models to proving commercial value and establishing oversight of spending, risk and delivery.
Emergn said that, 12 months after an engagement, clients should expect to see AI linked to revenue rather than treated as an experimental line item. Organisations should also have a funded portfolio of use cases, a functioning AI Board, and stronger internal communities and assets for future work.
The service is supported by Emergn's Value, Flow, Quality philosophy and by Praxis, its product development platform, which includes an AI product coach called Stella. Those tools form part of the broader framework the company uses in product and technology advisory work.
Adamopoulos said the issue is less about whether the technology works and more about whether companies have adapted their structures around it.
"The tools work. The wrappers around them don't yet," he said.
"Capable organizations and capable people shouldn't be left behind because the operating model can't keep up with the technology. AI should multiply what your people do - not be rented from consultants who leave with the capability when the engagement ends."