Whitepaper16 MIN READ
Measure the work that improves
Connect agent activity to accepted business outcomes, establish a credible comparison, and distinguish useful capacity from realized financial value before deciding whether to scale.
Category
Impact measurement
Baselines, useful metrics and value models that show whether agentic systems create lasting business value.
Agentic systems generate activity easily and value unevenly. Without a baseline and an honest accounting of correction effort, failure exposure and adoption friction, the numbers reported upward tend to describe usage rather than outcomes. This category covers baseline assessment, productivity and workflow-outcome measurement, quality-adjusted return, human-review and failure-cost modelling, total cost of ownership, adoption measurement, and dashboard design that survives contact with a sceptical finance function.
Published in this category
Architecture & engineering6 MIN READ
Where smaller models can strengthen agent architecture
Smaller models are not merely budget substitutes. Used behind explicit routing and escalation rules, they can improve latency, locality and architectural control.
Strategy & transformation6 MIN READ
Redesigning roles around human–agent collaboration
Do not divide work into “human” and “AI” columns. Redesign tasks, decisions, authority, handoffs and learning so each role remains operable and accountable.
Impact measurement6 MIN READ
How to estimate the cost of an agent failure
Model concrete failure scenarios across detection, containment, correction, disruption and consequence, then use ranges to choose controls.
Impact measurement6 MIN READ
The hidden total cost of operating an AI agent
Model usage is one line in a wider cost stack that includes integration, evaluation, control, human work, incidents, change and exit.
Impact measurement6 MIN READ
How to write an AI success story that survives scrutiny
A credible AI success story separates what was delivered from what changed, defines the baseline, shows the contribution chain and preserves uncertainty and permission.
Evaluation & assurance6 MIN READ
What product leaders should demand from an agent evaluation
Product leaders need evaluation evidence that connects workflow outcomes, user experience and policy behaviour to a release choice—not a leaderboard score.
Impact measurement6 MIN READ
What should appear on an AI impact dashboard?
Give decision owners a balanced view of outcomes, quality, human work, cost, risk and change—with every measure tied to an action.
Impact measurement6 MIN READ
What conference participation proves—and what it does not
Attendance, speaking and sponsorship are evidence of activity; useful conference claims require separate measures of learning, relationships, follow-through and resulting decisions.
Impact measurement6 MIN READ
Measure cost per quality-adjusted completion, not cost per token
Connect spending to accepted workflow outcomes by counting retries, review, correction and failures alongside model and platform charges.
Strategy & transformation6 MIN READ
Build, buy or partner for an AI agent? Ask these questions first
Choose a sourcing path by separating the workflow advantage you must own from the capabilities you can responsibly obtain elsewhere.
Impact measurement6 MIN READ
Why AI productivity claims need a baseline
Time saved is not an outcome until the original work, quality, case mix and destination of released capacity are measured on comparable terms.
Strategy & transformation6 MIN READ
How to prioritise an enterprise portfolio of agent opportunities
Rank agent opportunities by decision value, workflow evidence, feasibility, consequence and reuse—not by enthusiasm, executive volume or demo polish.
Strategy & transformation6 MIN READ
Adoption is part of agent reliability
An agent is not reliable if people avoid it, over-trust it or route around its controls. Adoption evidence belongs beside technical performance evidence.
Know what your agents will do before you let them do it.
Bring one agent workflow and the decision in front of you. Gaurav Tiwari replies personally within 1 working day.