Archive · 20 Aug 2026
Enterprise AI’s Value Depends on Execution, Not Context Alone
Enterprise AI systems are being taught about many business records and rules.
This helps them understand what companies and customers need.
But knowing information is not enough if the AI cannot finish useful tasks.
The article says companies should give AI the most relevant information, not necessarily all available information.
Too much information can confuse the system and increase costs.
Useful AI might process a simple refund or help solve a billing problem.
It could also spot supply-chain problems before they affect customers.
Companies must check whether AI actions produce real business improvements.
The article describes this journey as moving from context, to execution, to outcomes.
Enterprise AI adoption is shifting from gathering context toward reliable execution and measurable outcomes.
The article argues that AI needs relevant, timely, trustworthy information rather than every possible data point.
Excessive context can create conflicting signals, raise costs, and distract AI systems from their objectives.
Examples of valuable execution include processing refunds, resolving billing disputes, and managing supply-chain disruptions.
A 2025 McKinsey survey found that only 39% of respondents reported enterprise-level EBIT impact from AI, usually below 5%.
- Who
- Enterprise AI systems and the organisations adopting them; the article was written by Anuj Bhalla.
- What
- The article argues that enterprise AI must progress from understanding context to reliably executing tasks and creating measurable business value.
- When
- The discussion refers to the current enterprise AI adoption phase and cites a 2025 McKinsey survey.
- Why
- To explain how organisations can move beyond AI adoption and achieve measurable business outcomes.
- Maturity curve
- Context-maxxing, execution-maxxing, and outcome-maxxing.
- Useful context
- Information should be timely, trustworthy, relevant, and limited to what is needed for sound decisions.
- Retail example
- AI can process eligible refunds and escalate exceptions using policy, purchase, and fraud information.
- Billing example
- AI can resolve routine disputes, route approvals, and close cases.
- Supply-chain example
- AI can identify disruptions, recommend inventory reallocations, and alert teams before delays affect customers.
- Survey finding
- In a 2025 McKinsey survey, 39% of respondents reported some enterprise-level EBIT impact from AI.
- Reported impact
- Most respondents who reported enterprise-level impact estimated it at below 5%.