Tuesday, October 13, 2026
Tuesday, October 13, 2026
Mike Klein, Andras Vicsek and Peter Kalmar
The recent 2026 Gartner Business Quarterly arrived at a timely moment — just days after our Strategic Magazine article, AI is not SAP, argued that many organizations are applying the wrong playbook to AI.
Read together, the two pieces tell a coherent story. Gartner provides the strategic frame: AI is pushing organizations from automation toward autonomy. Our framework adds the missing organizational layer: how to diagnose whether AI is actually improving decision-making capability without weakening the human networks that make organizations work.
Gartner’s most important contribution is the clear distinction between automation and autonomy.
Automation performs predefined tasks within known rules. Autonomy observes context, makes decisions, adapts, and self-corrects. That difference matters because the management logic for automation does not work for autonomy.
The SAP playbook — steering committees, standardized workflows, approved use cases, compliance checkpoints — makes sense for automation. But when applied too early or too rigidly to AI, it can slow adoption, cap experimentation, and prevent organizations from learning how AI actually changes work.
Gartner makes a similar point when it warns against applying enterprise-wide mandates too soon. Autonomous business does not mean replacing people with “lights out” systems. It means people doing different things, rethinking the purpose of their roles, and reallocating time toward higher-value work.
This is the central point of AI is not SAP: AI transformation is not mainly about tool deployment. It is about whether the organization’s decision system can adapt.
Most organizations will initially measure AI through familiar indicators: usage rates, productivity, cost reduction, human-in-the-loop reduction, output quality, and error rates. These are useful, but incomplete.
They tell us what the technology is doing. They do not tell us what is happening to the organization.
Our framework focuses on three dimensions:
Adoption asks whether people are using AI.
Acceleration asks whether AI is actually increasing the speed and quality of collective decision-making.
Isolation asks whether AI is unintentionally weakening the informal human networks through which trust, judgment, advice, and context normally flow.
The third dimension is the most easily missed. As AI becomes a source of answers, analysis, validation, and recommendations, people may rely less on colleagues for sensemaking. That can be efficient in the short term, but risky if it erodes the advice networks, trust relationships, and cross-boundary conversations that help organizations make good decisions under uncertainty.
In other words: AI can either strengthen or weaken organizational networks. The difference depends on whether leaders are measuring and intentionally designing the human network layer as AI adoption scales.
This is where Organizational Network Analysis becomes essential.
Before AI adoption accelerates, organizations should understand how decisions, influence, expertise, trust, and collaboration currently travel. Who do people turn to for advice? Where are the central connectors? Which groups are isolated? Where are the bottlenecks? Which relationships carry the contextual judgment that formal structures rarely capture?
Once AI enters the system, those patterns will change.
A company may see rising AI usage and assume the transformation is working. But beneath the surface, junior employees may be asking fewer colleagues for help, cross-functional advice ties may weaken, key experts may be bypassed or overloaded, and decision quality may become more uneven. Standard AI metrics will not reveal this. ONA can.
The point is not to resist AI. The point is to make sure AI adoption increases organizational capability rather than simply shifting work into invisible, fragmented, or isolated patterns.
Gartner is right to recommend careful sequencing: start with focused areas of autonomy, build trust, avoid trying to make the entire enterprise autonomous at once, and put governance in place.
But governance alone is not enough.
Autonomous business requires a deeper organizational capability: the ability to sense how AI is changing decision flow, collaboration, trust, and informal influence — and then intervene responsibly.
That requires three things working together:
Organizational Network Analysis to map how work and decisions actually happen.
Internal communication strategy to activate understanding, confidence, and shared direction.
Organizational development to build the leadership conditions for autonomy: decision authority at the edge, psychological safety, learning loops, and embedded governance.
This is the layer where strategy either compounds or dissipates.
The most important work for AI transformation may need to happen before adoption scales too far.
Organizations that map their informal decision, advice, trust, and collaboration networks now will have a baseline they can use to understand how AI is reshaping the organization. Those that wait may never be able to reconstruct what changed.
Gartner confirms the strategic diagnosis: AI autonomy requires a different organizational model. The next step is to build the diagnostic and intervention layer that makes that model work in practice.
That layer is organizational network health.
And for companies serious about becoming AI organizations, this is where the practical work begins.
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Mike Klein is Principal of Changing The Terms and Editor-in-Chief of Strategic Magazine. Andras Vicsek is founder of Orgmapper. Peter Kalmar is Founding and Managing Partner of Flow International. Together, they offer a diagnostic and intervention framework for organizations navigating AI adoption. The original article, “AI is not SAP: Here’s Why it Needs a Different Playbook,” is available at https://strategic.global/AI-is-not-SAP-Heres-Why-it-Needs-a-Different-Playbook/
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