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The False Binary

Your company needs AI capabilities.

The strategic question arises: "Should we build or buy?"

Executives debate. Build means: Hire team. Develop models. Own IP. Customize for our needs.

Buy means: Purchase platform. Faster deployment. Proven technology. Lower risk.

Debates rage for months. Build camp vs buy camp. Eventually one side wins.

Twelve months later: Build approach is 18 months behind schedule. Buy approach delivered software but no business value.

Both sides were wrong. Because the question was wrong.

Build vs buy is a false binary. There is a third option you are ignoring: Partner.

A 2024 McKinsey study found that companies using partner models achieved production AI 3.2x faster than build approaches and 4.1x better ROI than buy approaches.

The best answer is not build or buy. It is partner first, then decide.

Why Build Fails

Let us examine why building AI in-house rarely works for most companies.

Build Problem 1: Hiring Delay

Plan: Hire 6-person AI team. Start building in 3 months.

Reality: Senior AI talent takes 6-9 months to hire. Competitive market. High salaries. Multiple offers. Long negotiations.

By month 9, you have 3 of 6 positions filled. They are ramping (3-6 months). First model work begins month 12-15.

By then, business requirements have changed. Market has shifted. Competitors have deployed 8 models.

Build Problem 2: Learning Curve

Your team is smart. But they need to learn: Your business domain. Your data landscape. Your organizational dynamics. What actually drives value in your context.

This learning takes 6-12 months. Mistakes happen. Dead ends explored. Wrong use cases attempted.

Meanwhile, consultants who have built 50+ models across 20 industries bring pattern recognition. They see what works. They avoid known pitfalls.

Build Problem 3: Reinventing Wheels

Your team builds: Data pipelines (somebody solved this already). Model registry (open-source solutions exist). Deployment automation (not differentiated). Monitoring dashboards (common problem).

They spend 60% of time on infrastructure nobody cares about. 40% on models that drive value.

Build Problem 4: Full Cost Ownership

When you build, you own: Recruiting costs ($150K per year). Management overhead (40% of tech lead's time). Infrastructure costs ($200K+ annually). Turnover replacement (30% annual churn). Underutilization (40-50% of time not on active projects).

True cost of 6-person team is not $1.2M salaries. It is $2.1M-$2.4M total.

Build Problem 5: Permanence

You hire permanent team. Business pivots. AI priorities change. You are stuck paying for capacity you do not need.

Cannot scale down without layoffs. Cannot scale up without 6-month hiring cycles.

Fixed capacity in dynamic environment is liability.

Why Buy Fails

Now let us examine why buying platforms usually disappoints.

Buy Problem 1: Feature vs Need Mismatch

Platform has 50 features. You need 3.

You pay for 50. Use 3. Waste money on 47.

Sales pitch: "You will eventually use other features." Reality: You will not.

Buy Problem 2: Integration Complexity

Platform works great in demo. Clean data. Standard formats. Test environment.

Your reality: 15 legacy systems. Inconsistent data. Custom integrations needed. 12-month integration project.

Platform cost: $1.8M. Integration cost: $2.4M. Total: $4.2M.

You bought software. You got integration project.

Buy Problem 3: Vendor Lock-In

Once committed to platform, you are locked in.

Vendor raises prices? You pay. Vendor deprioritizes features you need? You wait. Vendor gets acquired and product roadmap changes? You adapt.

You are stuck. Migration is too expensive.

Buy Problem 4: Generic Solutions

Platform is built for everyone. It is not built for you.

Pre-built models are generic. They do not understand your business nuances. Your industry specifics. Your competitive context.

You need customization. But platform is not flexible. Or customization costs additional millions.

Buy Problem 5: Capability Illusion

You bought AI platform. Executives think: "We have AI now."

Reality: You have software. Not capability.

Software does not identify use cases. Does not prioritize value. Does not integrate with workflows. Does not train users. Does not ensure adoption.

You bought tool. You did not buy transformation.

The Partner Model: Build and Buy Benefits Without Drawbacks

There is a third option: Partner with firms that provide AI capability as a service.

How Partner Model Works

You partner with firm like ITSoli. They provide: Senior AI consultants (on demand, not permanent). Model development (deliverables, not FTEs). Proven methodologies (learned from 100+ projects). Flexible engagement (scale up/down as needed).

You pay for outcomes, not headcount. Models deployed, not hours worked.

Partner Model Advantages

Speed: No hiring delay. Start building in days, not months. First model in 10-12 weeks.

Experience: Senior consultants with 50+ models built. Pattern recognition. Avoid pitfalls. Fast learning.

Flexibility: Scale engagement based on needs. Busy quarter? Add capacity. Slow quarter? Scale down.

Cost: 50-70% cheaper than equivalent in-house team. Pay for deployment, not bench time.

Knowledge Transfer: Partner trains your team. Builds internal capability. Makes themselves unnecessary eventually.

When Partner Model Works Best

Partner model is ideal for: First 3-10 AI models (learning phase). Variable AI workloads (not continuous). Specialized skills you lack (e.g., computer vision, NLP). Testing AI value before big investment.

Partner model enables you to: Prove AI works. Build initial portfolio. Train internal team. Then decide: Continue partnering, or transition to in-house build, or add buy for scale.

The Staged Approach: Partner → Hybrid → Build

Smart companies do not see this as binary. They stage their approach.

Stage 1: Pure Partner (Year 1)

First 3-5 models: Partner with ITSoli. Prove value. Learn what works. Build proof points.

Investment: $300K-$600K. Result: 3-5 production models. $2M-$8M value. Proven ROI.

Stage 2: Hybrid (Year 2)

Next 5-10 models: Partner handles new development. Hire 1-2 engineers for maintenance. Partner trains internal team.

Investment: $800K-$1.2M. Result: 8-13 production models total. Internal capability growing.

Stage 3: Mostly Build (Year 3+)

Core models: In-house team (now 4-6 people). Specialized models: Still partner (computer vision, advanced NLP). Surge capacity: Partner for crunch times.

Investment: $1.5M-$2M. Result: 15-20+ models. Internal capability mature.

This staged approach: Minimizes risk. Builds capability incrementally. Avoids over-investment before proving value.

Case Study: Three Companies, Three Approaches

Three manufacturing companies started AI initiatives in January 2024.

Company A: Build Approach

Strategy: Hire 8-person AI team. Build everything in-house.

Timeline: January-September 2024: Hiring (6 positions filled by September). October 2024-March 2025: Ramp and learning. April 2025: First model development begins.

By January 2025 (12 months): Models deployed: 0. Team: 6 people (2 positions still open). Investment: $1.4M (salaries, recruiting, infrastructure).

Company B: Buy Approach

Strategy: Purchase enterprise AI platform ($2.1M). Hire implementation consultants ($800K).

Timeline: January-June 2024: Platform selection and procurement. July-December 2024: Implementation and integration. January 2025: Platform deployed, training begins.

By January 2025 (12 months): Models deployed: 0 (platform is ready, use cases not developed). Investment: $2.9M. Using 15% of platform features.

Company C: Partner Approach (ITSoli)

Strategy: Partner with ITSoli. Deploy first model immediately.

Timeline: January 2024: Use case workshop (week 1). First model development (weeks 2-11). February-March 2024: Deployed model 1 (predictive maintenance). April-June 2024: Deployed model 2 (quality prediction). July-September 2024: Deployed model 3 (demand forecast). October-December 2024: Scaled models, deployed model 4.

By January 2025 (12 months): Models deployed: 4. Measured value: $6.8M annually. Investment: $580K. ROI: 1,172%.

Same timeline. Company C has 4 models driving $6.8M value. Companies A and B have 0 models and $4.3M spent.

The Real Build vs Buy Question

The real question is not "build or buy?"

The real questions are:

"How do we prove AI drives value fastest?" (Answer: Partner)

"How do we build AI capability most efficiently?" (Answer: Partner, then selectively build)

"How do we avoid over-investing before proving value?" (Answer: Partner with variable costs)

"How do we access best expertise and methodologies?" (Answer: Partner with experienced firms)

Build and buy both have roles. But neither is the right starting point for most companies.

Partner first. Prove value. Build capability. Then strategically add build (for core) and buy (for infrastructure) as needed.

The ITSoli Partner Model

ITSoli provides AI capability as a service.

What You Get

On-Demand Expertise: Senior AI consultants. Available when needed. Not permanent overhead.

Model Development: We build production-ready models. Deliverables, not FTE hours.

Proven Playbooks: 100+ models built. We know what works. We avoid known failures.

Flexible Capacity: Scale up for active projects. Scale down between projects. Variable cost structure.

Knowledge Transfer: We document everything. Train your team. Build capability while delivering models.

Engagement Models

90-Day Sprints: $75K-$125K per model. Ideal for first 3-5 models.

Annual Retainer: $600K-$1M. Ongoing partnership. 4-6 models per year. Flexible capacity.

Hybrid Support: $300K-$500K. We partner with your internal team (1-2 people). Accelerate their work.

All models pay for outcomes, not time.

Why Companies Choose Partner Model

Speed: Deploy in weeks, not years. Cost: 50-70% cheaper than build. Quality: Senior expertise from day 1. Flexibility: Scale engagement based on needs. Risk: Prove value before big investment.

The Build vs Buy Conversation

Next time someone says "should we build or buy?" respond:

"Neither. Not yet. Let us partner with ITSoli for our first 3-5 models. Prove AI drives value. Learn what we actually need. Then decide what to build, what to buy, and what to continue partnering on.

Starting with build or buy is premature optimization. We do not yet know what AI looks like for our company. Let us learn through deployment. Then make informed decisions."

This reframes the debate from ideology to pragmatism.

Stop Debating, Start Deploying

Build vs buy debates waste time.

While you debate, competitors deploy.

The right answer for most companies: Partner first. Prove value. Build capability incrementally. Then strategically add build and buy.

You would not build your own email system. You would not buy an email platform before knowing how many users you have.

Same logic applies to AI. Do not build or buy prematurely.

Partner. Deploy. Learn. Then decide.

That is how AI transformation actually works.