How SMB Consultants Win by Avoiding the Expensive Theater Everyone Else Calls Strategy

Audience: Leaders and strategy consultants serving small and medium businesses.

Your challenge: Most "AI strategies" are expensive theater, tool chases, and pilot parades that burn budget, stall trust, and fail to scale. Your job is to diagnose the real obstacle, choose a clear policy, line up coherent moves, and then show money in the bank.

The foundation: Richard Rumelt's strategy kernel remains the test. Good AI strategy honors that kernel: it targets a real obstacle, sets a guiding policy, and lines up coherent actions that pay off. If your AI strategy doesn't name the obstacle, pick a policy, and align coherent actions, you don't have a strategy; you have a wishlist.

The 10 Principles of Good AI Strategy

1) Start with a real diagnosis, not a wish

The problem: Most strategies are just goals and tech roadmaps that don't explain what's blocking outcomes. Effort splinters and value never compounds.

The solution: Write one plain sentence that names the bottleneck and its cause, then set a guiding policy and 2-3 actions that all relieve that bottleneck.

• Bad example: "Adopt AI across the business in 2025."

• Good example: "Sales stall because proposals take 9 days and pricing matrices exist  in three systems, we will use an AI drafting assistant and pricing guardrails to cut cycle time 40 to 70 percent."

Client risk: If you cannot say the bottleneck crisply, you're selling theater.

2) Choose tools on purpose, not in bulk

The problem: Tool sprawl adds hidden integration, training, and governance costs with no line of sight to value.

The solution: One tool, one owner, one KPI, one workflow change. Good AI strategy ties each tool to a measurable outcome and a named owner.

• Bad example: "We are rolling out Copilot, a vector database, and an agent platform."

• Good example: "Adopt one CRM-tied sales copilot, owned by Sales Ops, KPIs are win rate and cycle time, embed into proposal creation and handoffs."

Client risk: Tech-first talk gets you labeled a vendor, not a strategist.

3) Go after solvable problems first

The problem: The top problem often needs perfect accuracy, deep integrations, or custom ML that isn't feasible now.

The solution: Pick the number one solvable problem using the 4D AI-Fit screen (detailed below).

• Bad example: "Fix churn with predictive AI."

• Good example: "Reduce ticket backlog 30 percent with AI triage and macros, then revisit churn after we stabilize support data."

Client risk: Over-promising on the hardest problem is how relationships die.

4) Pilot with production intent

The problem: Demos wow in meetings, then die in the wild because there's no production intent, no owner, no gates, no security sign-off.

The solution: Every pilot gets a 90-day plan, a success metric, a defined data scope, an integration path, a security review, and a go or no-go gate.

• Bad example: "Cool demo of an email bot, we'll see where it goes."

• Good example: "If first response time drops 25 percent in 60 days with a stable CSAT (customer satisfaction score), integrate the bot into Tier 1 support and retire the old macro pack."

Client risk: Zombie pilots drain credibility.

5) Earn the infrastructure

Why it fails: Overbuilding foundations chews budget and time, especially for SMBs that need returns this quarter.

Do this instead: Prove value with SaaS and APIs, invest in data quality, identity, and logging only when actual bottlenecks appear.

• Bad example: "Build a data lakehouse, then find use cases."

• Good example: "Use CRM and help desk APIs to ship one win, then add monitoring and DLP once adoption creates real load."

Client risk: Infra-first looks sophisticated, delivers nothing near term.

6) Govern with guardrails people use

Why it fails: A 40-page policy no one reads slows delivery; no policy invites incidents.

Do this instead: Ship a simple, enforceable guardrail with approved tools and data classes, human review checkpoints, disclosure rules, incident escalation, minimal logging and retention.

• Bad example: "We will publish an AI charter, details later."

• Good example: "Two-page guardrail, no client PII in public LLMs, proposals require human review/approval, prompts and outputs logged on restricted projects, named incident owner."

Client risk: One governance miss can end the client, full stop.

7) Go beyond words, let AI act

Why it fails: If AI only writes words, you miss the value. Modern AI can "combine complex analyses" and work as a "researcher, interpreter, thought partner, simulator, and communicator" across systems.

Do this instead: Design AI to advise and act, suggest decisions, execute tasks via APIs, update CRM or ERP, with guardrails.

• Bad example: "AI for copywriting."

• Good example: "When a freezer IoT sensor flags a temp issue, AI opens a maintenance ticket, messages the shift lead, and updates the asset record."

Client risk: Selling word magic undersells impact; competitors will win with automation.

8) Co-own with the business, not just IT

Why it fails: Value lives in workflows and incentives, not in code. If IT owns it alone, the business shrugs.

Do this instead: Co-own with line leaders, update SOPs, set incentives tied to adoption and outcomes.

• Bad example: "IT will deploy and measure success."

• Good example: "Sales Ops co-owns the lead qualifier, Sales Rep  compensation includes AI-assisted touches and quality thresholds."

Client risk: "IT project" equals low adoption, fast stall.

9) Run an AI portfolio, not a one-off

Why it fails: Models drift, processes change, people turn over.

Do this instead: Run an AI portfolio with stage gates and quarterly reviews, discover, prove, industrialize, scale, retire. 

• Bad example: "Launch a flagship and celebrate."

• Good example: "Operate a small portfolio, scale two winners each quarter, kill one laggard with data, refresh the backlog."

Client risk: Static programs quietly decay, then suddenly fail.

10) Show the money, every month

Why it fails: Leaders buy outcomes, teams adopt what helps today.

Do this instead: Instrument everything, publish a simple scorecard monthly.

• Bad example: "Leverage synergy for transformation," no numbers.

• Good example: "Scorecard shows proposal cycle time, meetings booked, ticket handle time, cost per ticket, average order value, repeat rate, plus adoption and data quality, published on the first business day each month."

Client risk: If you cannot quantify it, you cannot defend it when budgets tighten.

Your 4D AI-Fit Screen

Score each use case from 1 to 5. Prioritize those with high Business and Integration scores, with Technical and Adoption risk you can live with.

1. Business Fit: Does it drive revenue up, cost down, or risk down?

2. Technical Fit: Can today's GenAI, or GenAI plus RPA or analytics, solve it with light tuning?

3. Integration Feasibility: Can it slot into CRM, ERP, help desk, LMS, or case systems without heavy rewrites?

4. Adoption Feasibility: Will users trust it? Is "good enough with oversight" acceptable?

Quick examples:

• Ticket triage: 5, 4, 4, 4 → Yes

• High-stakes adjudication: 5, 2, 3, 1 → No

• Proposal drafting: 4, 5, 5, 4 → Yes

Red Flags: Kill or Rework If Any Are True

• No single sentence diagnosis

• No owner per use case

• No production plan with integration and acceptance criteria

• No security or data policy

• No metric and baseline

• No incentives or SOP changes

• No decision gate with dates

• No adoption or training plan

• No rollback plan

• No scorecard tied to outcomes

The Bottom Line

Rumelt's kernel is still the test. A good AI strategy starts with a real diagnosis, not a wish. It chooses tools on purpose, tackles solvable problems first, and shows money in the bank every month. Keep it simple, keep it real, keep it measurable.