McKinsey QuantumBlack24 Aug 2026Agent cost
The real cost of running an AI agent is the people watching it
McKinsey's practitioner guide to agent economics finds token costs are typically just 20 to 25% of an AI agent's variable running costs, while human oversight accounts for 70 to 75%. A single-agent banking service workflow costs $20,000 to $30,000 to run; a conversational onboarding agent for 2,500 customers a year costs $10,000 to $15,000.
Why it matters
Process design, not model procurement, is the lever on agent cost. What matters is what proportion of runs get reviewed and by whom: McKinsey's own onboarding example puts 10 to 20% of runs in front of a risk or functional expert.
McKinsey Industrials26 Aug 2026Agent cost
AI's return comes from decisions, not headcount
McKinsey argues that since SG&A typically runs 5 to 12% of costs, labour savings alone can't explain a 20% EBITDA improvement from AI, so the value must come from faster decisions and better use of existing assets. Companies that redesign end-to-end workflows around AI report roughly 20% EBITDA uplift and three dollars of profit for every dollar invested.
Why it matters
This counters the idea that AI value is purely a headcount question: it points investment toward production scheduling, demand sensing and capital allocation instead. McKinsey's estimate that delaying a $50 million initiative by a year can turn it into a $75-100 million problem is a useful way to price delay.
McKinsey Quarterly28 Aug 2026Playbook
Twenty companies that made real money from AI, and what they had in common
McKinsey studied 20 large companies that created significant value from AI: EBITDA improved 20% on average, with $3 of incremental EBITDA returned per $1 invested. Two-thirds focused on three business domains or fewer; Freeport-McMoRan found 60% of its AI system reusable across plants, and DBS cut model deployment time from 15-18 months to 2-3.
Why it matters
Focus beats breadth: two-thirds of the winners worked on three domains or fewer, the opposite of how most AI use-case portfolios are built. Freeport's 60/40 reuse split is a practical planning number for sizing a central AI team against local ones.
Gartner26 Aug 2026Agent cost
Service leaders are raising AI spend 38% inside a budget growing 2%
Gartner surveyed 199 service and support leaders and found AI spending up 38% while the function's overall budget grew just 2%: the money is being redirected, not added. Leaders are moving spend away from labour and overhead and toward technology.
Why it matters
This is what an AI business case looks like with no new money behind it. Any organisation self-funding AI from a flat budget should weigh this against the finding that human oversight is most of an agent's running cost: it may be cutting the labour its agents will need back as reviewers.
Gartner26 Aug 2026Supply chain
The real test for an inventory technology is how much counting it removes
Gartner argues that barcodes, RFID and smart cabinets improve individual counting tasks while leaving the underlying operating model unchanged, whereas cameras combined with demand prediction and automatic replenishment remove the counting task altogether. Written for healthcare supply chains, but the logic holds across sectors.
Why it matters
This is a direct test for retail stock accuracy too: does a proposed technology make counting faster, or does it remove the need to count at all? Most proposals on the market do the first while being sold as the second.
MIT Sloan Management Review26 Aug 2026Playbook
The AI platform isn't finished, so invest in what outlasts it
Kevin Boudreau argues that generative AI adoption is already large, around 2.4 billion monthly users, but the technical and institutional architecture around it remains unsettled, unlike electricity, his benchmark for a mature general-purpose technology. His argument is academic rather than based on new data.
Why it matters
A steadying frame for any board facing AI-FOMO pitches: own the assets, such as proprietary data and customer relationships, that become more valuable as AI becomes abundant, rather than trying to own the AI itself. It's a fair test for any capital request that assumes today's model pricing and capability will hold for the life of the business case.
