Why 6 months?
Six months is the sweet spot for an AI implementation. Shorter, and you do not have time to move from prototype to production with proper integration, testing, and user adoption. Longer, and the organisational momentum dissipates, stakeholders lose interest, and the technology landscape may have shifted enough to require a restart. Six months is long enough to build something real — a working AI capability integrated into a business process, producing measurable results — and short enough to maintain executive sponsorship and team focus. It also aligns with quarterly planning cycles: a January kickoff delivers results by the mid-year review.
The month-by-month timeline
Month 1 — Discovery & Data Readiness: Map the current process end-to-end. Identify data sources, assess data quality, and define the success metrics that will determine whether the project is worth continuing. Output: a data readiness report and a prioritised use-case shortlist. Go/No-Go Gate #1: Is the data sufficient? Are stakeholders aligned on the metric?
Month 2 — Prototype Build: Build a working prototype of the AI capability using real (anonymised) data. This is not a PowerPoint demo; it is a functioning model that processes actual inputs and produces outputs that can be evaluated against the success metric. The prototype runs in a sandbox environment, not production.
Month 3 — Prototype Evaluation & Refinement: Test the prototype against a holdout dataset. Measure accuracy, latency, and edge-case performance. Refine the model based on findings. Output: an evaluation report comparing prototype performance to the success metric. Go/No-Go Gate #2: Does the prototype meet the minimum performance threshold?
Month 4 — Integration Build: Connect the AI model to the production system — CRM, ERP, DMS, or custom application. Build the API layer, authentication, error handling, and fallback logic. Deploy to a staging environment for integration testing. This is where most projects fail, because integration is harder than model-building. ALTE dedicates a full month to this phase specifically to avoid the 'model works in a notebook but not in production' problem.
Month 5 — User Acceptance Testing & Training: Release to a pilot group of actual users. Collect feedback on UX, accuracy, and workflow fit. Train users — not just on how to use the tool, but on what the AI can and cannot do, how to interpret confidence scores, and when to escalate to a human reviewer. This month is critical for adoption; users who feel the AI was imposed on them will find ways to work around it.
Month 6 — Production Go-Live & Monitoring: Deploy to full production. Enable monitoring dashboards for model performance, data drift, and business impact metrics. Establish a weekly review cadence for the first month post-launch. Output: a live AI capability producing measurable results, with a monitoring and maintenance plan in place. Go/No-Go Gate #3 (post-launch review at Month 7): Are the target metrics being met? Is adoption at expected levels?
Who needs to be involved
The minimum viable team for a six-month AI implementation includes: an Executive Sponsor who can clear organisational obstacles and approve budget; a Business Process Owner who understands the current workflow and can define what success looks like; a Data Engineer or IT Lead who controls access to data sources and production infrastructure; an AI/ML Engineer (ALTE provides this role, or we work alongside your internal team); and a Change Management Lead responsible for user training, communication, and adoption tracking. The most common failure mode in AI projects is not technical — it is organisational. Without an engaged business process owner and executive sponsor, even a technically flawless AI system will fail to deliver ROI because nobody changes their workflow to use it.
The three go/no-go gates
ALTE's six-month roadmap includes three explicit decision points where the project should be re-evaluated — and potentially stopped — based on evidence, not enthusiasm. Gate #1 (end of Month 1): Is the data sufficient to build a useful model? If the data is too sparse, too messy, or too sensitive to process under current compliance frameworks, stop here. The cost of this gate is one month of discovery effort. Gate #2 (end of Month 3): Does the prototype meet the minimum performance threshold defined at kickoff? If accuracy, latency, or edge-case handling is not good enough, do not proceed to integration. Either extend the refinement phase or kill the project. Gate #3 (Month 7, post-launch review): Are the target business metrics being met? This is the hardest gate because by now there is significant sunk cost and organisational momentum behind the project. But shipping an AI system that nobody uses is worse than not shipping one at all. If adoption or impact metrics are below threshold, diagnose the root cause — is it a technical issue, a workflow-fit issue, or a change-management issue? — and decide whether to fix, pivot, or sunset.
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