The world is experimenting with AI, not yet operating with it
At a global composite of 32 out of 100, organizations have adopted tooling ahead of the workflows and governance needed to extract durable value — leaving a structural gap between what platforms can do and what enterprises actually do with them.
The pillar spread is the story.
Every reading is scored through the same five pillars — so the numbers are comparable across the whole index.
At 31, the People pillar reveals that most organizations lack the AI fluency, role clarity, and upskilling infrastructure to convert tool access into practiced capability.
Processes scores 29 as the global binding constraint, indicating that AI is being layered onto unreformed workflows rather than driving genuine operational redesign.
Platforms leads all pillars at 38, reflecting aggressive procurement of AI tools and infrastructure that has outpaced every other readiness dimension.
Data registers at 30, signaling that foundational issues — quality, accessibility, governance, and integration — remain unresolved across the majority of organizations.
Leadership scores 32, suggesting that executive intent around AI exists but has not yet translated into funded mandates, accountability structures, or cross-functional coordination.
The nine-point spread between Platforms (38) and Processes (29) is the defining structural imbalance of this baseline — organizations bought the technology but have not yet re-engineered the work, creating a readiness ceiling that no additional tooling can lift.
A 32 is an open field, not a failing grade.
The global composite of 32 places the world firmly in the experimenting phase: most organizations have piloted AI use cases, secured licenses, and expressed strategic ambition, but fewer than one in four have embedded AI into core workflows with measurable, repeatable outcomes.
Every region, every business size tier, and nearly every function shares the same structural pattern — Platforms outscoring Processes and Data — which means the constraint is organizational, not technological, and will require a fundamentally different investment posture to resolve.
Who’s ahead, who’s behind — 23 industries, one scale.
Every scope is scored against the same calibration anchors, so these ranks are genuinely comparable. Each shows its leading and lagging pillar.
The same scale, cut four ways.
Who owns each pillar?
The single highest-scoring segment on each of the five pillars, across every industry, function, region and size.
Locate yourself on the index.
Pick a dimension and a specific segment to see where it stands relative to the field — and the single highest-leverage move for that position.
Professional Services and Consulting scores 40. Strongest pillar: People (42). Weakest: Data (39).
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Explore the BlueprintWhat a 32 means from each seat at the table.
The board should understand that we are at 32 out of 100 — squarely in an experimenting posture — and that our binding constraint is not technology spend but process redesign; the decision this quarter is whether to fund workflow reengineering with the same urgency we funded platform procurement.
Our Platforms pillar at 38 tells me we have already placed bets on tooling; the competitive differentiation over the next 12–18 months belongs to whoever closes the nine-point gap to Processes first, because that is where adoption converts to operating leverage.
We are carrying platform costs scored at 38 against process readiness at 29, which means a meaningful portion of our AI spend is generating cost without commensurate return — the financial case now favors reallocating toward workflow integration and data quality where the payback per point is highest.
A Processes score of 29 confirms what my teams feel daily: AI tools are being bolted onto existing workflows and creating new handoff friction rather than eliminating it — until we redesign the work itself, more tooling just means more complexity to manage.
Platforms leading at 38 while Data trails at 30 tells me we have infrastructure without the clean, governed data pipelines to feed it — the next technical priority is not more models or more vendors; it is data integration, lineage, and quality at the source.
Leadership at 32 and Processes at 29 means governance frameworks and responsible-AI protocols are not yet embedded in operational reality — the gap between tool capability and process control is exactly where regulatory, reputational, and bias risk accumulates fastest.
Read the number with a skeptic’s eye.
Risks & blind spots
- !Platform-Process Divergence Creates Shelfware at ScaleWith Platforms nine points ahead of Processes, organizations risk accumulating AI tool licenses that never reach operational integration — converting capital expenditure into recurring cost with no measurable workflow impact.
- !Data Deficits Undermine Every Downstream AI InvestmentData at 30 is the weakest pillar in four of six regions, eight of eleven functions, and four of five business sizes; without addressing data quality and accessibility, model outputs remain unreliable and ungovernable regardless of platform sophistication.
- !Bottom-Quartile Industries Face Compounding DisadvantageNonprofit (16), Mining (18), Agriculture (18), and Government (20) sit 12–16 points below the global composite; without deliberate intervention, these sectors risk being structurally excluded from AI-driven productivity gains as front-runners accelerate.
Implications for leaders
- →Process Reengineering Becomes the Strategic BottleneckEvery scope examined — industry, function, region, and size — surfaces Processes or Data as the weakest link, signaling that the next phase of AI maturity is an organizational design challenge, not a technology procurement exercise.
- →Function-Level Disparity Demands Differentiated InvestmentA 17-point gap separates Engineering/R&D (43) from Legal (26) and HR (26), meaning enterprise-wide AI strategies that treat all functions identically will over-invest in leaders and under-resource laggards.
- →Size Asymmetry Requires Ecosystem-Level SolutionsLarge Enterprises (38) score more than double SMBs (17); without shared infrastructure, simplified tooling, and accessible frameworks, the majority of the economy by headcount cannot participate in AI-driven value creation.
What the narrative over- and under-states.
- Platform procurement as a proxy for AI maturityThe market narrative equates tool adoption with readiness, but Platforms at 38 sits nine points above Processes at 29 — proving that buying AI is not the same as being AI-ready.
- Enterprise-wide AI transformation as a near-term realityVendor marketing implies organizations are on the cusp of pervasive AI; a global composite of 32 says the median organization is still running isolated experiments, not integrated operations.
- Large-enterprise AI leadership as a settled advantageLarge Enterprises lead at 38 but remain below the midpoint, and their Process constraint (weakest pillar) means their lead is fragile and built on spending power, not operational integration.
- Process reengineering as the decisive AI investmentProcesses is the binding constraint at 29 across nearly every scope examined, yet organizational-design and workflow-redesign spending receives a fraction of the attention directed at model selection and platform procurement.
- Data readiness as a universal deficitData is the weakest pillar in four of six regions, eight of eleven functions, and four of five business sizes — a systemic gap that receives far less executive attention than model capabilities or compute infrastructure.
- SMB exclusion from AI value creationAt 17, the SMB tier is less than half the global composite and barely one-third of Large Enterprise, yet market discourse focuses almost entirely on enterprise-scale AI adoption — ignoring the majority of the economy by firm count.
The prevailing market narrative celebrates platform proliferation and flagship enterprise pilots as evidence of an AI transformation underway, but this edition's data reveals a world stuck in early experimentation where the decisive gaps are in process design, data quality, and workforce readiness — none of which are solved by more tooling.
Where the index is heading.
Where the next points come from.
The highest-leverage moves to lift each pillar this cycle — the binding constraint is the strategic bet; the strength is an easy add-on.
At 31 against a target of 39, the People pillar needs an eight-point lift — organizations have hired or licensed AI but have not systematically upskilled the broader workforce or defined AI-augmented roles.
- →Launch role-specific AI fluency programs that move beyond generic training to teach each function how AI changes its daily decision-making and output expectations.
- →Define AI-augmented job architectures — updated role descriptions, competency models, and performance criteria — for the top 20 highest-headcount roles in the organization.
- →Create internal AI-champion networks that pair early adopters with skeptical functions to accelerate peer-driven adoption and surface real workflow barriers.
At 29 against a target of 41, Processes requires a twelve-point lift — the largest and most strategically critical gap, reflecting that AI is sitting on top of unreformed workflows enterprise-wide.
- →Identify the five highest-volume, highest-cost workflows per business unit and execute AI-native process redesigns with measurable before-and-after efficiency targets.
- →Embed process-mining tools to quantify current workflow bottlenecks and objectively prioritize which processes to redesign first based on AI-augmentation potential.
- →Establish a cross-functional process-transformation office with executive sponsorship, dedicated budget, and quarterly review cadence to sustain momentum beyond initial pilots.
At 38 against a target of 42, Platforms needs only a four-point lift — the smallest gap, reflecting that tooling investment is already the most advanced dimension and incremental gains require optimization, not expansion.
- →Rationalize existing AI platform portfolios — consolidate redundant tools, retire underutilized licenses, and negotiate enterprise-wide agreements to reduce cost per active user.
- →Strengthen integration layers between AI platforms and core business systems (ERP, CRM, HCM) to increase actual usage of tools already procured.
At 30 against a target of 38, the Data pillar requires an eight-point lift — organizations have data but lack the quality, governance, accessibility, and integration to make it AI-ready.
- →Implement automated data-quality monitoring on the top 10 data sets consumed by production AI models, with defined quality scores, alerting thresholds, and remediation owners.
- →Deploy a data-catalog and lineage tool to make enterprise data discoverable, traceable, and self-service for AI teams — reducing the data-preparation tax that slows every project.
- →Establish a data-governance council with cross-functional representation that defines access policies, privacy controls, and data-product SLAs for AI consumption.
At 32 against a target of 40, Leadership needs an eight-point lift — executive intent around AI is present but has not translated into funded mandates, clear accountability, or board-level governance structures.
- →Establish a quarterly AI business review at the C-suite level with standardized metrics covering adoption, value capture, risk posture, and capability development.
- →Assign named executive sponsors to each high-priority AI initiative with explicit P&L accountability and authority to make cross-functional resource decisions.
- →Develop a board-ready AI scorecard that communicates AI maturity, investment efficiency, and risk exposure in terms non-technical directors can act on.
These are market-level opportunities. For a plan scored against your own organization, explore the Business AI Blueprint.
Your move depends on where you sit.
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ReadHow the index is built.
Deep research
An independent research pass per scope — global plus every industry, function, region and size.
Five-pillar scoring
Each reading scored 0-100 through People, Processes, Platforms, Data and Leadership.
Shared calibration
Common anchors across scopes keep every rank genuinely comparable — no scope graded on its own curve.
Quarterly refresh
Re-scored each edition; momentum is measured against the prior period, not smoothed.
Questions about the index.
What does a global composite score of 32 actually mean for my organization?
It means the median organization worldwide has procured AI tools and run initial pilots, but has not yet embedded AI into core workflows, established data-quality foundations, or operationalized executive intent — placing the world firmly in an experimenting phase with substantial unrealized potential.
Why is Processes the binding constraint and not Data or People?
Processes scores lowest at 29 and acts as the throughput ceiling: even where people are skilled and data is available, AI cannot deliver sustained value if it is layered onto workflows designed before AI existed — process redesign is the gate through which all other pillar investments must pass to produce results.
How should I interpret the gap between Platforms (38) and Processes (29)?
That nine-point gap is the edition's most important structural signal: it means organizations are spending on AI tools faster than they are adapting operations to use them, creating a growing inventory of underutilized capability that inflates cost without proportionate return.
Is the top-ranked industry, Technology and Software at 51, actually mature?
A score of 51 places Technology just past the midpoint and still within the early-scaling phase; it leads every other industry but its own Data weakness confirms that even the most advanced sector has not solved foundational readiness — the gap between the leader and the laggard is narrower than the gap between the leader and true maturity.
What is the single highest-leverage action for a leader reading this edition?
Audit the gap between your Platforms investment and your Processes score: if tools have outrun workflows, redirect the next dollar from procurement to process redesign — that single shift addresses the binding constraint identified across virtually every industry, function, region, and size tier in this edition.