Practical AI Automation Strategy: My 90-Day Playbook

I learned the hard way that “we’re doing AI” can mean anything from a flashy demo to a real, measurable business change. A few years back, I watched a team celebrate a chatbot launch… while support tickets quietly kept piling up like unwashed coffee mugs in the office kitchen. That mismatch—between excitement and outcomes—is why I now start every practical automation AI strategy guide with one stubborn question: what will be measurably different in 90 days?

1) Develop clear AI strategy (before tools steal the show)

Before I touch any AI tool, I force myself to write a clear AI automation strategy. Tools are loud and exciting, but strategy is what keeps me focused on outcomes, not demos.

My “napkin test” (6 sentences or it’s not real)

I use a simple rule: if I can’t explain the plan in six sentences to a non-technical leader, then I don’t actually have a strategy. My napkin version usually covers: the business problem, who it helps, what data it needs, how we measure success, the main risks, and the first use case we will ship.

Translate AI ambition into measurable business outcomes

I don’t write goals like “use AI more.” I translate ambition into metrics leaders already track:

• Revenue acceleration (faster lead response, better conversion, higher retention)
• Cost-to-serve reduction (fewer manual steps, shorter handling time, fewer rework loops)
• Risk mitigation (policy checks, audit trails, safer approvals)
• CX/EX gains (better customer experience and employee experience)

The five pillars for AI automation 2026

From the Practical Automation AI Strategy Guide, I organize the work into five pillars so nothing important gets skipped:

1. Governance and risk management (policy, privacy, model usage rules)
2. Data and platform readiness (clean data, access, integration, reliability)
3. High-ROI use case prioritization (value, feasibility, time-to-impact)
4. Operating model and skills (owners, training, change management)
5. Scale-through-delivery (MLOps + security, monitoring, versioning)

Wild-card analogy: treat AI like a new hire

AI should have a job description, access rules, a probation period, and a performance review.

This framing helps me set boundaries: what the AI can do, what it must never do, and how we check quality over time.

Centralized expertise, distributed execution (when it fits)

I centralize the hard parts (governance, security, shared platforms, reusable components) and distribute execution to business teams who own the process. If a workflow is high-risk or highly regulated, I keep more control centralized.

2) Business needs assessment: picking high-ROI use cases (and resisting shiny objects)

Before I automate anything, I do a business needs assessment. The goal is simple: pick high-ROI AI automation use cases that solve real pain, not “interesting” demos. I’ve learned that the fastest way to waste 90 days is to chase shiny objects.

My simple use case scorecard

I rank every idea with a short scorecard so decisions feel fair and repeatable. I score each item 1–5 and total it.

• Impact: Will this move revenue, cost, speed, or customer experience?
• Feasibility: Can we build it with our tools and skills?
• Data readiness: Do we have clean inputs and labeled examples?
• Risk: What’s the compliance, security, or brand downside?
• Time-to-first-value: Can we show value in weeks, not quarters?

Look for “quick win enablers”

The best early wins are workflows with clear inputs and outputs and a painful backlog. I look for repeatable work where people already follow a pattern, like:

• Invoice intake and coding
• Renewal reminders and follow-ups
• Support ticket triage and routing

These are great because you can measure improvement fast, and the automation can plug into existing systems.

Production-intent pilots (not science projects)

I only approve pilots that are built to graduate. That means the pilot plan includes evaluation, monitoring, and change management from day one. If we can’t explain how we’ll test quality, watch drift, and train users, it’s not a pilot—it’s a demo.

My small confession: I once green-lit a “cool” use case. It became a dashboard nobody opened. Never again.

Set unit economics early

I add targets up front so ROI isn’t guesswork. I write them down in plain numbers: cost per case, minutes saved, error rates, and rework volume. If we can’t measure it, we can’t manage it.

3) Data readiness assessment: the 2-week audit I now refuse to skip

Before I pick a model, a vendor, or even a workflow tool, I run a data readiness assessment. It’s the least exciting part of my Practical AI Automation Strategy, and it’s the part that saves me the most pain later. I timebox it to 2 weeks per target process because endless audits are their own kind of procrastination.

Step 1: Map the source-of-truth (yes, it’s annoying; yes, it matters)

I start by mapping every system that touches the process and naming the source-of-truth for each key field. If “customer status” lives in three places, I force a decision on which one wins. This is where most automation projects quietly fail: the model isn’t wrong, the data is.

Step 2: Run schema + quality checks

Next, I do quick, practical checks on structure and quality. I’m not trying to build a perfect warehouse—just to see if the data can support automation.

• Schema checks: required fields present, consistent types, stable IDs, join keys.
• Quality checks: missing values, duplicates, outliers, stale records, conflicting labels.
• Sampling: I manually review a small batch to spot “looks fine on paper” issues.

Step 3: List access gaps and compliance constraints

Before any model selection, I document who can access what, how data moves, and what rules apply (PII, retention, audit logs, regional limits). If Legal or Security will say “no,” I want that answer now, not after a demo.

Mini-tangent: the shared drive of doom is not a data platform, even if it has folders named FINAL_v7.

My outputs (non-negotiable)

At the end of two weeks, I produce two artifacts:

1. Data readiness audit doc (systems map, source-of-truth decisions, checks run, risks).
2. Remediation backlog with owners, dates, and effort.

Backlog item	Owner	Due	Effort
Fix duplicate customer IDs	Ops	Feb 5	2 days
Add consent flag to CRM export	IT	Feb 9	1 day

4) AI governance framework: safe speed, not bureaucratic speed bumps

In my 90-day playbook, I treat AI governance like guardrails on a fast road: it keeps us moving, but it stops costly crashes. The goal is safe speed, not slow approvals and endless meetings. I set a lightweight framework that answers three questions: who approves what, how risks are logged, and what “good enough” looks like for launch.

Lightweight approvals + a simple risk log

I keep approvals small and clear: the product owner approves the workflow, security approves data access, and a domain lead approves the final output quality. Every automation gets a short risk entry (one page or less) so we can track decisions without creating paperwork.

Governance risk management with tiers

I use three risk tiers and match them to review depth:

• Low: internal summaries, formatting, routing. Quick peer review.
• Medium: customer-facing drafts, pricing guidance, policy references. Add domain review + test cases.
• High: regulated content, financial decisions, access to sensitive data. Security review, red-team prompts, and sign-off.

Generative AI accuracy safety: retrieval guards + validators

To keep outputs from drifting, I add retrieval guards (only approved sources, limited context windows) and response validators (must cite sources, must follow a template, must refuse if data is missing). I often enforce a simple rule:

“If it can’t cite an approved source, it can’t claim it as fact.”

Security-by-design AI aligned to Zero Trust

I harden endpoints, sanitize inputs, and restrict tools and data scopes per worker. Each agent gets the minimum permissions needed, and every call is authenticated and logged—classic Zero Trust applied to AI automation.

Prompt injection prevention as a checklist

I make prompt injection prevention routine, not reactive:

1. Strip or quarantine untrusted instructions from user content.
2. Lock system prompts and tool policies.
3. Allowlist tools, domains, and actions.
4. Test with known injection strings before release.

5) Choosing models architecture + MLOps security deployment (the unglamorous part I’ve grown to love)

In my 90-day playbook, this is where the “fun demo” turns into a real system. I start with the simplest model architecture that meets my needs for latency, cost, and accuracy. If a small model, a prompt template, or even a rules step can hit the target, I use that first. Then I evolve the design only when the data proves I need more.

Choosing the right foundation setup: rules vs RAG vs fine-tuning

Most business automation work is not about inventing new knowledge—it’s about using your knowledge safely. That’s why I often land on “RAG + guardrails” as the default.

• Rules: best for strict policies, routing, and “never do X” constraints.
• RAG (retrieval-augmented generation): best when answers must come from current docs, tickets, or SOPs.
• Fine-tuning: best when you need consistent style or classification patterns at scale, and your data is stable.

If the workflow depends on fresh internal info, I choose RAG. If it depends on stable patterns, I consider fine-tuning. If it’s a hard requirement, I enforce it with rules.

Observability and cost control (unit economics)

I track performance like a product, not a science project. That means monitoring drift, common failure modes, and cost per workflow run. I keep a simple dashboard: success rate, escalation rate, average tokens, and average runtime.

Metric	Why it matters
Cost per run	Stops “silent” spend growth
Top failure reasons	Shows what to fix first
Drift signals	Catches changes in inputs over time

MLOps security deployment: build it in from day one

From the first pilot, I bake in evaluation suites, monitoring, and release gates (no deploy without passing tests). I also run weekly evals instead of quarterly “big bang” reviews.

Weekly evaluation runs beat quarterly surprises.

This is the unglamorous part—but it’s what keeps AI accurate, safe, and ready for real users.

6) Measuring AI ROI (and publishing an AI P&L like I mean it)

If I can’t measure it, I don’t ship it. In my Practical AI Automation Strategy, ROI starts with a clean baseline. Before I turn on any AI automation, I capture the “pre” numbers: cycle time, backlog size, SLA adherence, error rate, and cost per ticket/task. Then I compare against “post” results and adjust for change costs like training time, process updates, and QA effort. Otherwise, the ROI is fake.

Baseline first, then compare post (with change costs)

I treat every workflow like a mini experiment. If the process changed, I log the cost of that change. That includes manager time, documentation, and the extra reviews we do in week one.

Use counterfactuals when I can

When possible, I use a counterfactual so I’m not fooling myself. That can be:

• Control groups (one team uses AI, one doesn’t)
• Holdout queues (a slice of work stays manual)
• Matched time windows (same days of week, similar volume)

If none of that is possible, I at least annotate what changed (seasonality, staffing, policy shifts).

Publish an AI P&L by function (not a mystery blob)

I roll up benefits and costs by function so leaders can see where value is real:

Function	Benefits (examples)	Costs (examples)
Support	Hours saved, SLA improvement	Tooling, QA reviews
Sales Ops	Faster lead routing, fewer errors	CRM changes, prompts
Finance	Close time reduced	Controls, audit checks

Avoid vanity metrics

I don’t care about tokens used if backlog and SLA adherence don’t move.

I track outcomes, not AI activity. Tokens, model calls, and “messages processed” are only useful if they connect to business results.

Make ROI visible in a 3-minute monthly one-pager

1. Top 3 wins (with numbers)
2. Top 3 costs/risks
3. Net ROI by function
4. What changed since last month

7) Step-by-step automation roadmap: my 90-day cadence (with a few human wobbles)

When I build an AI automation strategy, I don’t start with tools. I start with a 90-day rhythm that keeps me honest, keeps risk low, and still ships real work. I also plan for a few human wobbles—because adoption is never a straight line.

Day 0–14: assess and align

In the first two weeks, I align on the “why” and the boundaries. I confirm the business goal, map the workflow, and run a quick data readiness audit: where the data lives, who owns it, how clean it is, and what can (and cannot) be used. Then I set governance guardrails—privacy rules, approval steps, and a simple risk tier so we don’t debate every use case from scratch. This is also where I pick success metrics that a non-technical leader can repeat.

Day 15–45: prove value fast with production-intent pilots

Next, I run pilots that are built like they will go live. That means evaluation and monitoring are included from day one. I test quality, cost, speed, and failure modes. I track drift signals (like rising manual edits or more exceptions) and I document what “good” looks like. If a pilot can’t be measured, I treat it as a demo—not automation.

Day 46–75: ship and scale

This is the hardening phase. I tighten integrations, reduce manual handoffs, and expand coverage to nearby steps in the workflow. I also formalize runbooks: how to handle outages, how to roll back, and who gets paged. This is where my wobbles show up—stakeholders change their minds, edge cases appear, and I learn to ship smaller releases more often.

Day 76–90: expand capability and formalize the AI Center of Excellence

By the last stretch, I set up an AI Center of Excellence that enables teams instead of policing them. It provides templates, shared evaluations, approved connectors, and office hours. Finally, I create a change management runbook and train managers to spot workflow drift early—so the automation stays useful after the launch excitement fades.

TL;DR: If I had to boil my AI business transformation playbook down: pick 1–2 high-ROI workflows, run a 2-week data readiness audit, build production-intent pilots with evaluation + security from day one, measure ROI with baselines (not vanity metrics), and scale via an AI governance framework plus MLOps security deployment.