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Further

2026 Research Report

The State of Enterprise AI Maturity

How Enterprise Data Readiness Is Shaping the Next Phase of AI

69% of companies investing in AI are still stuck exploring, experimenting or implementing — not scaling. Based on a survey of U.S. AI decision-makers, this report breaks down the four gaps that separate real AI maturity from adoption.

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What you'll learn

Four patterns shaping the AI maturity model in 2026

  • 01

    AI is delivering real value, but the metrics organizations use to judge it are misaligned with long-term strategy.

  • 02

    Most organizations are building AI on data that's analytics-ready, not AI-ready.

  • 03

    Confidence in AI outpaces business and organizational realities.

  • 04

    Ownership of AI initiatives isn't the same as accountability for its outcomes.

Insight 01 — Measurement

Enterprise AI strategy is caught between proving short-term ROI and building long-term value.

AI value is real, but the metrics are misaligned

77%

of organizations report a positive return on their AI investment. But when you break down how they define that return, the picture changes.

“Cost savings are worth celebrating, but they're the floor, not the ceiling. The organizations that pull ahead won't be the ones spending the most on AI, but the ones that take steps to define what ‘success’ actually means.”
Price Glomski SVP of GTM, Further

Insight 02 — Readiness

AI data readiness depends on an architecture most organizations haven't built yet.

Data readiness is the real constraint

29%

of organizations have completed a formal data audit.

5%

believe their data is actually AI-ready.

What AI-ready means

Most enterprise data architectures are optimized for structured, backward-looking reporting. AI-ready systems operate on a vastly different model.

Insight 03 — Confidence

Leaders describe an AI readiness that the governance underneath it doesn't yet support.

Confidence in AI outpaces organizational realities

What the same leaders flag as challenges

25%

can't explain or trust AI outputs.

36%

lack internal AI expertise.

3%

have a documented AI-audit process.

“In reality, confidence without a documented process is exposure. Closing that gap doesn’t take new technology, it requires an auditable governance process.”
Kierra Dotson Associate Director of AI Strategy and Governance, Further

Insight 04 — Accountability

AI sits between technical execution and business strategy, but who connects them?

Ownership is split, accountability isn't assigned

40%

put AI decision-making with IT and data teams.

38%

put it with executive leadership instead.

Only 7% have a Center of Excellence

Both sides report alignment. Almost none have built a place to practice it. Alignment that has never been tested in a room together is a belief, not an operating model.

“Ownership is just one step towards accountability. A team can be responsible for AI decisions without being held to clear business outcomes.”
Jason Tabeling Head of Solutions, Further

The full findings

See where you actually sit on the curve

The report sets out where organizations actually sit on the maturity curve, what separates the ones that scale, and the benchmarks to measure your own position against.

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515 U.S.-based AI decision-makers surveyed October 2026

Respondents span IT, data leadership and executive sponsorship across enterprise and SME revenue bands.

Common questions

AI adoption vs. maturity, in plain terms

Further's benchmarks, framework and full findings live in the report — see our AI services for how we apply them.

Analytics-ready data is cleaned and modelled to answer questions you already know how to ask, in dashboards and reports. AI-ready data adds what a model needs to be trained, evaluated and governed over time: lineage, labelling, documentation and access controls.

Most pilots are designed to prove a capability rather than to run cross-organizationally or long-term, so they never acquire repeatable pipelines, a named owner or recurring funding. When the pilot ends, there is no operating model to carry it forward.

Sponsorship converts interest into budget, mandate and cross-functional access. Sponsors who define the business outcome and hold a named owner accountable for it move initiatives further than sponsors who only approve spend.

Pair a cost measure with a value measure — efficiency gained, revenue influenced, risk reduced, decision quality improved — and set a pre-AI baseline for each. Agreeing on that baseline before deployment is what makes the return defensible afterwards.

Start by inventorying your AI models, agents, and the data behind them, and assign an accountable owner to each. Document intended use, testing, and known limitations, including what each agent can access and what it can do without human approval. Then set a review cadence with logged decisions. Auditability comes from a trusted, repeatable process that you can automate as AI scales across the organization.

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See the four patterns separating organizations that scale AI from the ones stuck experimenting, and how to start taking action today.

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