Strategy

If you cannot write the job description, you are not ready to hire the agent. This template turns an AI agent into a defined role that operations can own, finance can budget, and audit can inspect.

An AI agent that is never reviewed drifts, costs more than planned, and loses its owner's trust. This guide gives operations leaders a quarterly performance review for Digital FTEs: metrics, evidence, autonomy decisions, and the actions that follow.

Coding agents are arriving developer by developer, ahead of any plan. This guide gives engineering leaders the strategy: what the agents change, what must change around them, how to govern and measure, and how to roll out without trading quality for speed.

Acceptance rates and lines generated measure usage and reward volume. This guide gives engineering leaders an outcome-based measurement model for AI coding tools and agents, the baselines to capture, and the traps that produce flattering but false numbers.

Lock-in to a model vendor rarely arrives as a single decision. It accumulates through SDK calls, tuned prompts, proprietary features, stored embeddings, and contracts. This guide names each form, its cost, and the design that avoids it.

Open-weight models let banks, insurers, healthcare providers, and public bodies run AI where their data already lives. This guide sets out when they fit, what they demand operationally, how to evaluate them, and the licensing and governance questions to settle first.

AI projects fail on statements of work written for conventional software. This template scopes the work around specifications, evaluation evidence, and autonomy levels, so both sides know what done means before anything is built.

An agent in production needs the same thing any service needs at three in the morning: a runbook. This template covers health checks, the failure modes specific to agents, the kill switch, rollback, escalation, and safe change procedures, written for the person on call.

The job description says what the role is for; the specification says exactly how the agent must behave. This template is the engineering contract FISTA writes before building any agent, section by section, with what makes each testable.

Every AI project should have an evaluation plan before it has a prompt. This template defines what correct means, how the golden dataset is built, how outputs are scored, the thresholds that gate release, and the production loop that keeps quality measured.

AI incidents have causes conventional postmortems miss: a specification gap, injected content, a model change, a permission too broad. This template structures the investigation so the fix lands in the right place and the case joins the regression set.

AI programs stall on ownership: engineering builds, nobody defines correct, security finds out at audit. This RACI template assigns responsibility for every artifact and decision across process owners, engineering, platform, security, data, finance, and governance.

Vendor selection for AI fails when scoring rewards the best demo. This scorecard weights evidence, security, terms, and delivery method over presentation, sets gates that no score can offset, and gives a structure that survives a procurement review.

Model cards were designed for published models. Enterprises need one for every deployed AI system: what it is for, what it must not do, how it was evaluated, what controls bound it, and who owns it. This template adapts the practice for agents and applications.

Prompts are production code that most teams write in a text box and never review. This style guide template gives engineering teams the structure, portability rules, safety conventions, and review process that keep prompts maintainable across models and years.

AI projects that start with a charter finish with evidence; those that start with a demo finish with a debate. This template defines the problem, the baseline, the evidence that will count as success, the owner, the controls, and the conditions for stopping.

An agent that read a field correctly for six months breaks the day a producer changes its meaning. Data contracts make the inputs AI depends on explicit, tested, and versioned, so upstream changes become scheduled events rather than silent regressions.

Agents that query raw schemas produce confident, inconsistent numbers. A semantic layer of certified metrics and dimensions is what makes data agents trustworthy, governable, and cheap to extend. This guide explains why, and how to build one domain at a time.

Private equity firms need AI to create measurable value across portfolio companies within the holding period. This guide covers the value creation levers, a repeatable playbook applied across holdings, diligence on readiness, shared platforms and partners, proportionate governance, and the metrics buyers verify at exit.

A startup's AI strategy begins with whether AI is the product or a feature, because that decides what must be defensible. This guide covers shipping fast with evaluation from day one, controlling model cost as usage grows, keeping providers interchangeable, building data and workflow moats, and proving traction with verifiable metrics.

An AI governance board sets policy, approves high-risk systems, reviews incidents, and holds owners accountable. This guide covers its charter and decisions, membership, relationship to existing committees, operating cadence, the evidence it should demand, and how to set one up without slowing delivery.

An AI operating model defines how an organization builds, buys, runs, and governs AI at scale. This guide covers the platform, product, and governance structure, decision rights, funding that covers run cost, delivery practices with gates, and how to move from pilot-by-pilot improvisation to a repeatable system.

An enterprise AI policy sets specific, owned, enforceable rules for how people and systems use AI. This template covers scope, acceptable use, data rules, risk tiers and approvals, human oversight, transparency, procurement, security, incidents, and enforcement, with drafting guidance for each section.

AI portfolio management treats initiatives as investments: one inventory, consistent scoring, staged funding tied to evidence, sequencing that builds shared capability, and reviews that expand, pause, or stop on results. This guide covers the process, the metrics, and the kill criteria that prevent pilot sprawl.