Principles
Value at the heart of every decision
AI is adopted not for technology, fashion, or pilot count. Every decision — product launch, initiative selection, stage transition, scaling, or stop — must link to expected or confirmed business value.
Practical meaning: if an initiative or AI product has no clear value hypothesis, it should not receive resources.
Anti-pattern: "Let's deploy an AI agent because everyone is doing agents."
Right approach: start with the business problem, the outcome owner, and a testable effect hypothesis.
Broken process + AI = broken process
AI does not fix a process that has no clear rules, standards, or accountability. If a report is assembled from different data every time, an automated reporting skill will not make it reliable. If recruiting has no standards for evaluating candidates, an HR agent will not make scoring fair or reproducible. If nobody knows where documents live or who owns their freshness, RAG will not turn chaos into knowledge.
Practical meaning: before launching an AI product, check not only the model and interface but also the process itself: input data, decision rules, owners, quality standards, exceptions, result control, and accountability for errors.
Anti-pattern: a team automates a report that different employees assemble from different sources and with different logic, then wonders why AI quickly produces unrepresentative conclusions.
Right approach: first describe and stabilize the process to a minimally sufficient level, then add AI as an amplifier for search, generation, analysis, or automation.
AI products address task classes, not one-off requests
The company should aim not to build a unique solution for every separate idea, but to develop reusable AI products that address recurring classes of business pain.
An AI product must answer not only "what task are we solving now?" but also "what class of similar tasks can this product cover next?"
Practical meaning: the same product stack can cover dozens of use cases: knowledge search, document generation, inquiry analysis, request processing, forecasting, and automation of routine operations.
Anti-pattern: each department gets its own bot, knowledge base, integration, and support logic.
Right approach: identify recurring task patterns and turn them into shared AI products that initiatives from different departments can be routed to.
One business funnel, different delivery tracks
All AI products and AI initiatives should follow one management logic: idea, evaluation, routing, implementation, adoption, impact expectation, and closure. That lets the company see the full portfolio, compare initiatives, manage priorities, and decide by shared rules.
Technical implementation should not be the same for every type of AI product. RAG, ML models, AI agents, process automation, language assistants, and developer tools need different delivery tracks: different requirements for data, testing, integrations, quality, security, support, and readiness criteria.
Practical meaning: the business funnel answers "where is the initiative from a management and value perspective?" The delivery track answers "how do we technically bring this solution to result?"
Anti-pattern: every AI initiative is forced into one universal project template — same stages, same artifacts, same readiness criteria — whether the work is RAG, an ML model, an agent, or automation.
Right approach: keep one business funnel for portfolio management, but use different delivery tracks to implement different classes of AI products.
Federated responsibility model
Effective AI adoption should be neither fully centralized nor fully decentralized.
With full centralization, every initiative goes only through the AI function. That creates control but quickly leads to queues, bottlenecks, and loss of business context.
With full decentralization, teams adopt AI on their own. That brings speed but leads to duplicated products, shadow IT, extra load on infrastructure and budgets, security risks, and no path to scale.
The federated model combines both approaches:
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The AI function owns the rules of the game, methodology, infrastructure, product stack, control points, training, and adoption support.
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The business owns idea generation, domain expertise, task definition, pilot participation, and confirmation of business impact.
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IT, architecture, security, compliance, and data are not external approvers but full participants in the operating model.
Practical meaning: the AI function should not do everything itself. It should build a system in which the business can adopt AI safely and deliberately.
Anti-pattern: the AI office collects ideas, runs pilots, convinces the business to use the result, and tries to prove impact — all on its own.
Right approach: the business owns the problem and impact; the AI function owns the adoption model, products, and scaling rules.
Adjacent functions are part of the operating model
Architecture, information security, compliance, IT, data, and operations should not join only at the end, when the solution is almost ready. They directly affect whether AI can be adopted and must be embedded in the process from early stages.
Practical meaning: an initiative should pass upfront checks on data, access, architecture, security, integrations, and support.
Anti-pattern: the pilot was shown successfully to the business, but then it turned out it could not be deployed in production because of security constraints, missing data ownership, or inability to support it.
Right approach: adjacent functions participate in control points and help reduce risk early rather than slow adoption down.
Control proportional to risk
Not every AI initiative needs heavy approval. Review depth and control points should depend on the level of risk and novelty, not apply equally to everything.
- Low risk, standard scenario — fast path (self-service), minimal approvals.
- Medium risk — trust-but-verify mode: limited pilot with human-in-the-loop checks and impact control.
- High risk or significant impact — strategic review with security, compliance, architecture, and data.
- Unacceptable risk — the initiative does not launch.
Practical meaning: the same control layer should not slow down both a small departmental experiment and a system that affects customers or regulatory reporting.
Anti-pattern: every idea — from an internal knowledge-base chatbot to credit scoring — goes through the same heavy committee and waits months for approval.
Right approach: classify initiatives upfront by risk and novelty, then route them through different control paths, from self-service to strategic review.
Training and AI promotion are part of adoption, not an afterthought
An AI solution is adopted not when it is technically live, but when users understand how to apply it at work, use it regularly, and give feedback for product evolution.
Practical meaning: training, communications, AI champions, support for first use cases, and feedback collection should be built into the operating model.
Anti-pattern: the team shipped an assistant, posted a link in chat, and considered adoption complete.
Right approach: plan training, first application scenarios, business owners, and usage metrics in advance.
Impact is confirmed, not declared
Expected value for an initiative must turn into confirmed impact or a clear management decision: scale, refine, stop, or move to another format.
Practical meaning: after deployment, an initiative should not simply disappear from the portfolio. You need to know whether the solution is used, whether it delivered impact, who confirmed the result, and whether it can be scaled.
Anti-pattern: "We deployed an AI assistant" is treated as success on its own.
Right approach: success is not launch — it is regular use and confirmed benefit.