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The Strategy Spectacle

Your company just unveiled its AI strategy.

Six months in development. Three consulting firms involved. 200 slides. Beautiful frameworks. Market analysis. Capability assessments. Roadmaps extending three years.

Executive team is impressed. Board approves. Budget allocated.

Twelve months later: Models deployed: Zero. Business value: Zero. Team morale: Low.

You have a strategy. You do not have AI.

This is AI strategy theater. It looks like progress. It feels like leadership. It produces exactly nothing.

A 2024 MIT study found that companies spending 6+ months on AI strategy had 58% lower deployment rates than companies who spent 2-4 weeks planning and started building.

Strategy is necessary. Strategy theater is fatal.

What AI Strategy Theater Looks Like

Let us identify the symptoms.

Symptom 1: The Comprehensive Framework

Your strategy deck includes: AI vision and mission. Capability maturity model. Technology stack recommendations. Data governance frameworks. Operating model transformation. Change management approach. Risk and compliance considerations.

It covers everything. It deploys nothing.

Why it fails: Comprehensive strategies create analysis paralysis. Everything is interconnected. You cannot start anywhere without addressing everything.

What works: Simple plans that answer three questions: What business problem? What model? Who builds it?

Symptom 2: The Three-Year Roadmap

Your strategy projects AI deployment across 36 months. Phase 1: Foundation (months 1-12). Phase 2: Acceleration (months 13-24). Phase 3: Scale (months 25-36).

By month 36, the roadmap promises: 15 use cases deployed. $50M in value. AI-driven organization.

Reality: By month 12, nothing is deployed. By month 18, the roadmap is abandoned. By month 24, the program is restructured.

Why it fails: AI moves too fast for three-year plans. Technology changes. Business priorities shift. Teams turn over.

What works: 90-day cycles. Deploy one model. Learn. Adjust. Repeat.

Symptom 3: The Capability Maturity Assessment

Your strategy includes a detailed assessment: Current state: AI maturity level 1.5 out of 5. Gap analysis: Data, skills, infrastructure, governance, culture. Path to level 4: 24-month journey with defined milestones.

Then you wait. You build capabilities. You check boxes on the maturity model.

Two years later: Maturity level improved to 3.2. Models deployed: Still zero.

Why it fails: Maturity models measure inputs, not outcomes. You can score high on maturity and deliver zero business value.

What works: Measure models deployed and value delivered. Not capability scores.

Symptom 4: The Technology Selection Process

Your strategy spends 40 slides evaluating technology options. Cloud providers. AI platforms. MLOps tools. Data warehouses. Detailed scorecards. Comparison matrices.

Selection process: 6 months. Approval process: 3 months. Procurement: 4 months. Implementation: 8 months.

21 months later: Technology is deployed. First model development begins.

Why it fails: Technology selection before use case validation puts the cart before the horse.

What works: Build first model with whatever tools are available. Then select technology based on actual needs.

Symptom 5: The Organizational Transformation

Your strategy includes org design: New roles (Chief AI Officer, AI Center of Excellence, AI Governance Board). New processes (model approval, ethics review, deployment gates). New training programs (executive AI education, citizen data scientist program).

You hire people. Build processes. Create governance.

Organization looks great on paper. No models in production.

Why it fails: Organization follows deployment, not precedes it. You cannot design the optimal org until you know what AI actually looks like in your company.

What works: Start small. Build models. Let organization emerge from doing.

The Real Cost of Strategy Theater

Strategy theater is not just wasted time. It has real costs.

Cost 1: Opportunity Cost

While you strategize, competitors deploy. They learn. They capture value. They build momentum.

Every month you spend in strategy theater, they ship 2-3 models. By the time you finish strategizing, they are 18 months ahead.

That gap is nearly impossible to close.

Cost 2: Team Atrophy

Your best people leave. They joined to build AI, not attend strategy meetings.

Data scientists want to build models. Engineers want to deploy systems. Product people want to drive value.

Six months of strategy sessions with no deployments? They go to companies where they can actually build.

Cost 3: Executive Skepticism

After 12 months of strategy with zero results, executives lose faith. They cut budgets. They reallocate resources. They declare AI a failed experiment.

Not because AI does not work. Because your approach does not work.

Cost 4: Organizational Antibodies

The longer you strategize without deploying, the more resistance builds. Skeptics multiply. "We've been talking about AI for 18 months and nothing has changed."

By the time you try to deploy, organizational antibodies have formed. People resist because they have learned that "AI initiative" means meetings, not results.

What Actual AI Strategy Looks Like

Real AI strategy is simple. Almost suspiciously simple.

Component 1: Use Case Identification (Week 1)

Not: Comprehensive inventory of every possible AI use case. Scoring models. Prioritization matrices.

Instead: Answer these questions:

Which business problem costs us the most? Where is manual effort highest? Which process has worst outcomes? Where do errors impact business most?

Pick top 3. That is your use case list.

Timeline: 1 week, not 3 months.

Component 2: Feasibility Assessment (Week 2)

Not: Detailed technical architecture. Technology evaluation. Build vs buy analysis.

Instead: Answer these questions:

Do we have data for this use case? Can we get it in 2 weeks? Is there enough (hundreds of examples minimum)? Could a model plausibly help?

If yes: Proceed. If no: Next use case.

Timeline: 1 week, not 6 weeks.

Component 3: Pilot Plan (Week 3)

Not: Comprehensive deployment plan. Change management strategy. Training program. Governance framework.

Instead: Answer these questions:

Who will build the model? (In-house or partner like ITSoli?) What is the success metric? (Specific, measurable target.) Who are the pilot users? (10-20 people max.) When will we deploy? (90 days maximum.)

Write this down on 2 pages. That is your plan.

Timeline: 1 week, not 4 weeks.

Component 4: Build and Deploy (Weeks 4-12)

Not: More strategy. More planning. More approvals.

Instead: Execute. Build model. Test. Deploy to pilot users. Measure results.

Timeline: 8-10 weeks, not 12 months.

Component 5: Learn and Scale (Weeks 13-16)

Not: Back to strategy. Revise roadmap. Update frameworks.

Instead: Ask: Did it work? What did we learn? What is next?

If it worked: Scale or build next model. If it failed: Learn why and try different approach.

Timeline: 4 weeks, then repeat cycle.

Total strategy to deployment: 16 weeks. Not 24 months.

Case Study: Strategy vs Execution

Two companies started AI initiatives in January 2024.

Company A: Strategy-First (Traditional Approach)

Approach: Hired big consulting firm. Conducted 6-month strategy development. Produced 180-slide deck covering vision, capabilities, roadmap, governance, technology, organization.

Strategy approved September 2024. Implementation began October 2024.

By January 2025 (12 months later): Infrastructure selected and being procured. AI team hiring in progress (3 of 8 positions filled). First use case in development (not deployed). Models in production: Zero. Business value: Zero.

Investment: $2.1M (consulting plus initial hiring). ROI: Undefined.

Company B: Execution-First (ITSoli Approach)

Approach: Partnered with ITSoli. Week 1: Identified top 3 use cases. Week 2: Assessed feasibility. Week 3: Kicked off first 90-day sprint.

By April 2024 (12 weeks later): First model deployed (claims processing automation). Result: 73% time reduction, $680K annual savings.

By July 2024 (24 weeks): Second model deployed (fraud detection). Result: $2.1M fraud prevented annually.

By October 2024 (36 weeks): Third model deployed (customer churn). Result: 22% reduction in churn, $3.4M annual value.

By January 2025 (12 months later): Six models in production. Measured value: $8.7M annually. ROI: 1,087%.

Investment: $800K (three sprints plus ongoing partnership).

Same timeline. Company A has slides. Company B has $8.7M in value.

The Minimum Viable Strategy

You need some strategy. Just not 200 slides.

Here is the minimum viable AI strategy:

Page 1: Use Cases

Use Case 1: [Business problem]. Target: [Metric improvement]. Value: [$X annually].

Use Case 2: [Business problem]. Target: [Metric improvement]. Value: [$Y annually].

Use Case 3: [Business problem]. Target: [Metric improvement]. Value: [$Z annually].

Page 2: Execution Plan

Quarter 1: Deploy use case 1. Quarter 2: Scale if successful, deploy use case 2. Quarter 3: Deploy use case 3. Quarter 4: Scale portfolio.

Page 3: Investment

Q1 budget: $200K. Q2 budget: $300K (if Q1 succeeds). Q3-Q4 budget: TBD based on results.

Page 4: Team

Pilot: Partner with ITSoli for first 3 models. Year 2: Hire 2 in-house engineers if models prove value. Year 3: TBD based on scale.

That is it. Four pages. Not 200.

Everything else is execution.

The ITSoli No-Theater Approach

ITSoli does not write strategy decks. We deploy models.

Our Philosophy

Week 1: Strategy (1 page, not 200). Weeks 2-12: Execution. Week 13: Results and next steps.

We believe: Deployed model beats perfect strategy. Learning from real deployment beats theoretical planning. Value delivered beats frameworks created.

What We Actually Do

Day 1: Workshop with your team. Identify top use cases. Select one. Define success. Start immediately.

Weeks 1-8: Build model. We do not wait for perfect data. Perfect infrastructure. Perfect organization. We build with what you have.

Weeks 9-12: Deploy to pilot users. Measure results. Iterate based on feedback.

Week 13: Present results. If successful, plan next model. If unsuccessful, learn and adjust.

No 200-slide decks. No 6-month strategy processes. No maturity models.

Just deployed models that drive value.

Pricing

90-Day Sprint: $75K-$125K. One model from strategy to deployment. Prove value before big investments.

Three-Sprint Package: $200K-$300K. Deploy three models in 9 months. Build portfolio of value.

Annual Partnership: $600K-$1M. Continuous deployment. 4-6 models per year. No strategy theater.

All engagements focus on deployment, not planning.

The Anti-Strategy Questions

Before your next strategy session, ask:

"Can we start building instead of strategizing?" If you have identified a use case and have data, you can start building today.

"What prevents us from deploying in 90 days?" If the answer is "we need more strategy," you are doing it wrong.

"Will this strategy session lead to code being written?" If no, cancel it.

"What are we afraid of?" Usually: failure, making wrong decisions, looking unprepared. But deployed failures teach more than perfect strategies.

Stop Strategizing, Start Deploying

AI strategy is necessary. AI strategy theater is fatal.

You need enough strategy to start. Three questions: Which problem? What metric? Who builds it?

Everything beyond that is execution.

The companies winning with AI are not the ones with the best strategies. They are the ones deploying models.

Strategy is 1% of success. Execution is 99%.

Your 200-slide deck will not deploy a single model. Your 90-day sprint will deploy three.

Stop strategizing. Start deploying.

That is how AI transformation actually happens.