Why measuring operational intelligence matters — and how to benchmark it

Executive Summary

Enterprises today are investing heavily in artificial intelligence (AI) and automation. But the lion's share of these investments still deliver modest business value. According to one recent survey, only 1 % of companies believe they are mature in embedding AI into their workflows. (McKinsey & Company)

To shift from experimentation to enterprise-scale impact, organisations must evolve not just their technology, but how work itself works. That means transitioning from process-centric automation to intelligence-driven workloops, where data, decision-making, human and machine roles, and continuous learning converge.

This whitepaper introduces the concept of an AI-Native Index (AINI) — a framework to benchmark how "AI-native" an organisation's operations are. It explains why each dimension of the index matters, how to measure it, and offers examples of what high-maturity operations look like. The goal: give executives a credible tool to assess internal readiness, compare to peers, and chart a roadmap toward AI-native operations that truly deliver value.

Why a new operational lens matters

From automation to intelligence

Many organisations view AI and automation as tools: "Let's automate this process, replace repetitive work, save cost." That is valuable—but incomplete. For next-generation competitive advantage, the question is not merely what we automate, but how the system learns, adapts and continuously improves. Research shows enterprises that deploy AI with scale, across processes, people and decision-flows outperform their peers. (MIT Sloan)
For example: a study by Deloitte found organisations scaling "intelligent automation" reported average cost reductions of ~32 % in targeted areas. (Deloitte)
Likewise, Gartner reports that 45 % of "high-maturity" AI organisations keep major initiatives in production for three or more years (versus 20 % for low maturity organisations) — a signal of deeper operational embedding. (Gartner)

The gap between pilot and scale

Despite these gains, the gap remains large. For example, the 2025 Enterprise AI Maturity Index found fewer than 1 % of organisations scored above 50 on a 100-point scale. (ServiceNow)
Why? Key reasons: fragmented data, processes rigidly defined for humans (not machines), lack of governance and learning, and missing cultural readiness. In short: the tools may exist, but the operating model of work has not fully changed.

Why an index helps

For senior executives evaluating transformation, an index serves these uses:
• Benchmark: Where are we compared to our peers and where can we aim next?
• Diagnose: Which dimensions of our operations are weakest?
• Road-map: What should we invest in and sequence to become AI-native vs just "automated"?
• Communicate: A simple, credible narrative and scorecard makes the case for investment and change across the leadership team.

Introducing the AI-Native Index (AINI)

The AI-Native Index assesses operational maturity across five dimensions. Each dimension reflects a core capability of what it means to operate in an AI-native world: automation, intelligence, integration, insight velocity, and culture readiness.

Dimension 1 – Automation

Definition: The degree to which repetitive, high-volume tasks in core workflows are executed by systems (robots, bots, software) rather than humans.

Why it matters: You cannot build intelligence until you eliminate manual busy-work. Research by Deloitte shows organisations further along in automation report stronger outcomes.

What high-maturity looks like:

• End-to-end process automation: Workflows (request → action → closure) occur with minimal human intervention.
• Automated Exception Pathways: Exception handling is automated rather than purely manual.

Example: A global financial services firm automated 70% of its "customer onboarding checks" via bots, reducing human hand-offs by 60%.

Dimension 2 – Intelligence

Definition: The ability of systems to learn from outcomes, adapt decision logic over time, and refine processes based on data feedback.

Why it matters: Intelligence turns automation from static rules into dynamic, improving processes. Without it, automation may deliver cost savings but not strategic advantage.

What high-maturity looks like:

• Dynamic Models: Models are retrained periodically with outcome-data fed back into the loop.
• AI-Augmented Decisions: Decisions are increasingly driven by AI recommendations that humans regularly review and adjust.

Example: In a manufacturing company, quality-control vision systems detect defects, feed metrics into ML models weekly, and slowly reduce defects by 15% within 12 months.

Dimension 3 – Integration

Definition: The extent to which data, tools, teams and systems are connected — enabling cross-functional workflows, event-driven triggers and unified metrics.

Why it matters: Intelligence and automation live on data and event flows. If systems remain siloed (finance vs operations vs IT), hand-offs slow things down and destroy the potential for loops.

What high-maturity looks like:

• System Connectivity: APIs or event-buses connect major systems.
• Unified Data: A unified data model or "single source of truth" exists across key processes.

Example: A retail chain integrated POS, inventory, supply-chain and marketing systems; automated replenishment triggers cut stock-outs by 25%.

Dimension 4 – Insight Velocity

Definition: How quickly an organisation converts signals (data, events) into decisions and actions — and then loops back those outcomes for improvement.

Why it matters: The speed of decision loops separates "good" from "great". If insights take days or weeks, the value decays; truly competitive organisations act in hours or minutes.

What high-maturity looks like:

• Real-Time Visibility: Near-real-time dashboards and automated alerts triggering workflows.
• Empowered Action: Teams are empowered to act without layers of approval.

Example: In customer-service operations, a global telecom firm built live anomaly detection dashboards; when drop-off spikes occurred, workflows auto-triggered outreach campaigns within 30 minutes.

Dimension 5 – Culture Readiness

Definition: The human, organisational and governance layer: how ready the workforce, leadership and structure are to adopt, trust and scale AI-native work.

Why it matters: Technology without human & governance adoption fails to deliver. As noted by McKinsey, nearly all companies invest in AI but only ~1 % consider themselves mature — a reflection of the cultural and leadership gap. (McKinsey & Company)

What high-maturity looks like:

• Enabled Workforce: AI literacy programs, citizen-data initiatives, and leadership sponsorship.
• Strong Governance: Frameworks for AI ethics, performance, and monitoring.
• Trust: Trust in AI-augmented decisions from frontline teams.

Example: A global healthcare provider trained 40 % of its operations staff in "AI-enabled workflow" skills in twelve months; frontline adoption of an AI-recommendation engine exceeded 80 %.

How to Score the Index

Each dimension is scored on a 1-5 maturity scale (1=Manual, 5=Autonomous). A simple formula aggregates into a 0-100 scale:
Score (%) = (Sum of dimension scores ÷ 5) × 20.

From that score, organisations can be clustered into maturity archetypes:

By benchmarking score and individual dimension averages, executives can identify strength/gap profiles, prioritise investments and allocate transformation capitals accordingly.

Why These Dimensions Make Sense: Evidence & Logic

• Automation as the foundation: Extensive research affirms that automation is necessary but not sufficient. Deloitte's survey shows organisations with 51+ automations rate themselves significantly closer to their "ideal" transformation state.
• Intelligence completes the loop: Models and feedback matter. Without intelligence, automation becomes brittle. Literature on Industry 4.0 highlights that AI-enabled decision-making is key for transformation.
• Integration is the glue: McKinsey, MIT and others emphasise that data- and tooling-silos are major blockers in AI scale.
• Insight velocity differentiates winners: According to Bain's "Automation Scorecard 2024", companies investing heavily in automation and AI move faster on disruptive technologies and savings.
• Culture Readiness is the enabler: A recurring theme is that leadership, governance and workforce readiness are often the missing link in scaling AI-impact.

Two Illustrative Example Profiles

Practical Steps for Senior Executives

1. Run a baseline assessment using this index (or a digital version) across back-office, customer-facing and product operations.
2. Plot dimension scores and identify the two lowest scoring dimensions as priority focus areas.
3. Define a 90-day action plan, targeting:
   • One key automation enhancement
   • One intelligence or feedback-loop
   • One integration improvement (data or tool connection)
   • One insight-velocity initiative (e.g., reducing decision latency)
   • One cultural/skill building step (e.g., frontline AI-training or governance charter)
4. Monitor progress quarterly reassess the index, and benchmark against peers (industry reports show leaders widen the gap over time). (Bain)
5. Communicate narrative internally: "We are not merely automating— we are becoming an AI-native organisation." Use the index score as the headline for leadership dashboards.

Conclusion

The future of operations is less about doing work and more about how work evolves — shifting from linear processes to continuous, intelligence-driven loops. For senior executives, the AI-Native Index offers a credible, actionable framework to benchmark where the organisation is, decide where it needs to go, and build a roadmap to get there.

In an era of accelerating disruption, being AI-native is not optional. Organisations that transform how they operate—not just what they build—will gain the strategic edge. Use this index. Measure your journey. Lead the transformation.

References

• Burnham K., "What's your company's AI maturity level?", MIT CISR, Feb 25 2025. (MIT Sloan)
• Accenture, "The Art of AI Maturity", Global Report, 2023. (Accenture)
• Deloitte Insights, "Robotic process automation (RPA) and intelligent automation", June 2022. (Deloitte)
• Microsoft & IDC, "Embracing AI-Powered Operations: A Maturity Path for Manufacturers", 2025. (Microsoft)
• Oxford Economics & ServiceNow, "Impact AI: Enterprise AI Maturity Index 2024". (Oxford Economics)
• Bain & Company, "Automation Scorecard 2024: Lessons Learned Can Inform Deployment of Generative AI". (Bain)
• Gartner Inc., "Survey Finds 45% of Organizations with High AI Maturity Keep AI Projects Operational for at Least Three Years", June 30 2025. (Gartner)