Adoption Is Not Value: The State of Enterprise AI Going Into 2027

• 88% of organisations use AI regularly. 37% attribute any EBIT impact to it, and 6% are high performers (McKinsey).
• 56% of CEOs report no significant financial benefit from AI so far (PwC).
• 57% of CDOs say data reliability blocks the move from pilot to production (Informatica).
• >40% of agentic AI projects are expected to be cancelled by the end of 2027 (Gartner).
• 93.2% of data leaders name culture, not technology, as the main obstacle (Wavestone).

By almost any measure of adoption, enterprise AI has been a success. McKinsey’s 2026 State of AI survey found that 88% of organisations now use AI regularly in at least one business function, and 44% say they have scaled it across the enterprise. AI budgets keep growing, Chief AI Officers have become common, and few large organisations lack an AI strategy.

By the measure that matters most, value, the picture is very different. The same McKinsey survey found that only 37% of organisations attribute any impact on earnings to AI. Only 6% qualify as high performers, defined as getting at least 5% of EBIT from AI. PwC’s 2026 Global CEO Survey found that 56% of chief executives have yet to see any significant financial benefit. MIT’s research on generative AI pilots concluded that the large majority deliver no measurable impact on profit and loss.

Put simply, adoption is moving much faster than value creation. Understanding why is the most important strategic question for data and AI leaders in 2027.

The research points to four reasons, and none of them is primarily about technology.

The data is not ready. Pilots usually run on carefully prepared samples. Production runs on the real data estate, with its silos, inconsistent definitions, missing owners and silent quality failures. In Informatica’s 2026 survey of Chief Data Officers, 57% named data reliability as the main barrier between pilot and production. Gartner has warned that a large share of AI projects without AI-ready data will be abandoned.

Governance cannot keep pace. Three in four data leaders say their governance can no longer keep up with how employees use AI. That challenge is growing as organisations add AI agents that act on their own.

Work is not redesigned. Too many organisations add AI to existing processes instead of rethinking the work itself. BCG’s research shows that clear strategy and workflow redesign add around 25 percentage points of measurable impact, while better tools alone add about 5.

People are not brought along. In Wavestone’s 2026 survey of data and AI leaders, 93.2% named culture and change management as the main obstacle, against 6.8% who named technology.

Several forces will make 2027 a turning point. Finance leaders are taking a far more active role in approving AI spending, and Forrester expects a significant share of planned AI investment to be deferred until value can be demonstrated. AI projects will increasingly be managed as a portfolio, and initiatives without a clear business case will be stopped.

At the same time, agentic AI is moving from demonstrations into production. Gartner expects more than 40% of agentic AI projects to be cancelled by the end of 2027 because of cost, unclear value or weak risk controls.

Regulation is becoming concrete too. Under the EU’s Digital Omnibus agreement, the AI Act’s high-risk obligations under Annex III now apply from 2 December 2027.

A quieter but equally important question will also come to the fore: what to do with the capacity AI frees up. Organisations can bank it as cost savings, or reinvest it in better decisions, new products and new markets. The leaders of the next decade are likely to be those who choose the second path.

The research is consistent about what separates the minority who capture value from the majority who do not.

• They redesign workflows rather than automating existing ones.
• They treat data quality, semantics and metadata as core infrastructure rather than side projects.
• They involve governance early, as an accelerator: Databricks found that organisations using governance tooling bring many times more AI projects into production.
• They measure value at the level of business outcomes rather than usage.
• They invest in people as seriously as in platforms.

This is the reason the 2027 Data Innovation Summit is built around the theme “From Data & AI to Enterprise Capability”. Its Foundation Days stages map directly onto the causes of the value gap:

• Data readiness: the data engineering, databases and data quality, and platform and architecture stages.
• Governance: the data strategy and governance stage and the AI strategy and governance stage.
• Redesigning work: the AI automation and transformation stage.
• Measuring value: the analytics and decision intelligence stage, and Industry Day, which shows how capabilities translate into results in specific sectors.

The Data Innovation Summit is the largest and most influential annual applied data, analytics and AI event in the Nordics, organised by Hyperight in Stockholm since 2016. The 12th edition, DIS2027X2, takes place on 18–20 May 2027 at Kistamässan in Stockholm under the theme “From Data & AI to Enterprise Capability”.

The summit brings together more than 3,500 practitioners, business leaders, technology providers, start-ups and academics from over 50 countries. The programme runs across fifteen stages: two Foundation Days dedicated to data, analytics and AI capabilities, and an Industry Day covering five sectors.
The summit is a hybrid event, with every session streamed live and available on demand.

Regional editions are held in Dubai, Singapore and Sydney.

Learn more at datainnovationsummit.com.