The management system around it has not caught up. TargetBoard governs human and agent work on the same terms, so leaders can see what AI is doing to productivity, quality, cost, and delivery.
Humans and agents now produce change side by side, but capacity models, baselines, and reviews are still built on human-only throughput.
AI raised the speed of change without raising the speed of management. Quality, cost, and planning issues still surface at quarter close rather than in-sprint.
Velocity holds while churn, ownership gaps, and code-base inflation accumulate. Spend moves monthly while governance stays annual.
Run an AI-assisted organization on. TargetBoard measures each against a team's own baseline, not a generic industry benchmark, and shows which way AI is moving them.
throughput per engineer on AI-assisted teams, measured against their own baseline
escaped defect rate as AI share rises, with churn and rework tracked per source
blended cost per delivered change, tool and agent spend included
of committed milestones landing inside the forecast window
Four steps between AI tool telemetry and a decision a leader can act on the same week.
AI-generated commits, pull requests, and agent runs are identified per activity, team, tool, and model.
Cycle time, rework, defects, and predictability against each team's own pre-adoption baseline.
Token, licence, and agent spend allocated to the work it produced.
Material shifts arrive as alerts with an owner attached, on a single definition shared by all.
A domain-expert agent watches adoption, spend, and outcome signals continuously, and raises only what is material: spend outrunning delivered change, quality slipping on AI-generated code, adoption sitting where it moves nothing.
A programme-level read on where adoption converts and where it stalls, with agent governance and a spend allocation you can defend at renewal.
Cycle time, review load, and defect trends per team, showing where AI-assisted work helps and where it quietly adds rework.
Whether faster development is reaching committed dates, and which programmes carry the risk once AI-assisted scope enters the plan.
TargetBoard reads telemetry from AI-enabled developer tools and agent platforms alongside your SCM and work-tracking systems, then attributes commits, pull requests, and automated workflows at the change level. Attribution rules are configurable per tool and per team.
Token, licence, and agent spend is allocated to the work it produced, then divided by delivered change to give a blended cost per change by team and vendor. The definition is fixed in the semantic layer, so Engineering and Finance report the same number.
Adoption dashboards report usage. TargetBoard is a management layer: it connects usage to productivity, quality, cost, and delivery against each team’s own baseline, then raises the cases where adoption is high and impact is not, with an owner attached.
Escaped defects, rework, churn, and ownership gaps are tracked per source, so AI-generated changes are visible separately from human ones. Where quality moves against adoption, the agent raises it with the teams and repositories named.
Historical data from your connected systems is used to establish baselines, so first measurement typically arrives within the first weeks rather than after a quarter of collection.
A 30-minute call, walked through your own systems: where AI is moving productivity, quality, cost, and delivery today, and where it is not yet being managed.
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