Three Paths to AI Implementation: Buy, Boost or Build Your Way to Competitive Advantage
- Nancy Wang
- Sep 29
- 6 min read
Updated: 4 days ago
The AI gold rush is on, and companies are making big bets on technologies they barely understand, often without enough consideration of their optimal path.
This trend has caught the attention of researchers at MIT and elsewhere, who’ve been looking at management factors related to successful AI rollouts. The thoughtful work provides a practical framework for three paths to AI implementation: Buy off-the shelf, boost with proprietary data and build custom solutions.
Nick van der Meulen and Barbara Wixom from the MIT Center for Information Systems Research crystallize what we're seeing as consultants in the field: companies seeking to deploy AI should start with a sober readiness assessment and then commit to incrementally developing capabilities.
Their insights match our experience at Agentic Foundry of watching organizations across industries make critical mistakes simply by approaching AI with a misaligned approach.
This smart, tripartite framework provides clarity around operational self-assessment. Most companies overestimate their building capabilities and underestimate the potential for boosting existing solutions with proprietary data, for example.
To help busy leaders, we offer a reflection on the Buy, Boost or Build model informed by our work with clients. Navigating these choices is how you determine whether AI becomes your competitive moat or your expensive mistake.
The 3 Paths: A Strategic Breakdown
Buy: Speed Over Substance
What it means: Purchasing off-the-shelf AI solutions from vendors who handle everything from development to maintenance.
When it works: You need to demonstrate AI capabilities quickly, have limited technical resources, or are testing specific use cases before committing to larger investments.
The hidden costs: While the initial price tag looks attractive, vendor dependency creates long-term strategic vulnerability. When vendors discontinue versions, push major updates, or pivot their offerings, your entire AI strategy can become obsolete overnight.
Real-world example: A mid-sized logistics company adopted a vendor's route optimization AI. Within 18 months, the vendor was acquired, the product roadmap shifted, and integration costs ballooned by 300%. The "fast path" became the expensive option.
Best practices for buyers: The key here is identifying and building strategic partnerships. This means negotiating for meaningful input on product roadmaps and feature development rather than simply accepting what's offered.
Even when outsourcing, organizations must build internal AI literacy to make informed decisions and avoid becoming completely dependent. Smart buyers plan for vendor transition scenarios from day one, establishing clear data portability requirements and maintaining enough internal knowledge to switch if necessary.
These practices turn vendor dependency from a strategic vulnerability into a managed risk.
2. Boost: The Goldilocks Effect
What it means: Taking vendor solutions and enhancing them with your proprietary data through fine-tuning or retrieval-augmented generation (RAG).
When it works: You have valuable proprietary data, need customization beyond off-the-shelf capabilities, and can invest in robust data governance frameworks.
The strategic advantage: This approach delivers significantly better results than generic solutions while avoiding the massive upfront costs of building from scratch. It's where most successful AI implementations land. It is the path we at Agentic Foundry see a lot of clients select with guidance from our initial work with them.
The requirements: Strong data governance isn't optional here—it's existential. Poor data quality will amplify rather than solve your problems. You also need tolerance for higher operational costs as prompt lengths and usage fees increase with customization.
Best practices for boosters: Success with this approach demands a data-first mindset, beginning with comprehensive audits of data quality, accessibility, and governance maturity before any implementation begins.
Starting with retrieval-augmented generation (RAG) rather than fine-tuning reduces risk while enabling faster implementation and easier iteration based on real-world results. Organizations must build robust validation processes specifically designed for custom solutions, as standard testing frameworks won't capture the nuances of your enhanced models.
Critical to long-term success is planning for scale from the outset, as usage costs will grow significantly with adoption — budget planning should account for this inevitable expansion rather than treating it as an unexpected expense.
3. Build: Maximum Control, Maximum Risk
What it means: Developing custom AI solutions from the ground up, taking full responsibility for development, deployment, and maintenance.
When it makes sense: You have truly unique use cases, significant technical capabilities, and can justify the massive upfront investment for long-term competitive differentiation.
The reality check: Most companies choosing this path probably should reconsider. Building AI isn't just about hiring data scientists—it requires advanced infrastructure, specialized talent, and substantial ongoing investment.
Strategic considerations: Building custom AI solutions presents a complex value proposition that requires careful long-term thinking. While organizations can expect lower usage costs over time, this benefit comes only after massive upfront computational investments that can strain budgets for years before showing returns.
The trade-off is complete control over intellectual property and the potential for genuine competitive differentiation that can't be replicated by competitors using vendor solutions.
However, success demands advanced data monetization capabilities to justify the enormous costs involved, making this path viable only for organizations that can demonstrate clear pathways from AI investment to revenue generation.
Before choosing to build, ask:
Do we have use cases that genuinely can't be solved with vendor solutions?
Can we attract and retain top-tier AI talent?
Is our data infrastructure mature enough to support custom model development?
Can we commit resources for 3-5 year development cycles?
The answers to these questions will reveal whether you have the foundation for custom AI development or whether alternative paths offer better risk-adjusted returns.
A Three-Part Decision Framework
The choice between buy, boost, and build isn't arbitrary — it should be driven by a systematic evaluation of your organization's current state and strategic objectives.
Start with Organizational Readiness
Choosing the right AI path requires honesty about your organization's capabilities and strategic position. Technical capabilities form the foundation of any AI initiative, encompassing your current AI/ML expertise, data infrastructure maturity and development capabilities.
Organizations often overestimate their technical readiness, particularly their ability to handle integration complexity and maintain AI solutions over time.
Strategic positioning considerations are equally critical, involving your competitive differentiation requirements, time-to-market pressures, budget constraints and tolerance for vendor dependency risk. These factors must be weighed against realistic ROI expectations that account for both direct costs and opportunity costs of different approaches.
Your data assets represent the most undervalued component of AI readiness assessment. The volume and quality of proprietary data, combined with data governance maturity and regulatory considerations, directly determine which paths are viable for your organization.
The monetization potential of data-driven insights often becomes the deciding factor between boost and build strategies.
At Agentic Foundry, we've developed the FORGE framework to help organizations navigate this assessment. FORGE evaluates where AI can create value, identifies potential agentic solutions that align with strategic objectives and provides clear guidance on selecting the optimal path — buy, boost, or build. We’ll post future blog articles about FORGE soon.
This structured approach prevents the costly mistakes that come from rushing into AI implementation without proper foundational assessment.
3 Critical Success Factors
Regardless of which path you choose, three elements are essential for AI success:
1. Visible AI Governance
This isn't just about ethics committees. You need clear decision-making processes, stakeholder engagement frameworks, and scalability guidelines. Too many AI projects fail because organizations treat them as IT initiatives rather than strategic business transformations.
2. Strategic Relationships
Even if you're building internally, you'll likely need vendor partnerships for infrastructure, data, or specialized capabilities. The most successful AI implementations treat these as true partnerships with mutual value creation rather than traditional supplier relationships.
3. Continuous Adaptation
AI is evolving too rapidly for set-and-forget strategies. Build testing and learning loops into your implementation that allow for continuous optimization based on real-world results and changing technology landscapes.
Making Your Decision
Multiple industry studies (Accenture, BCG, MIT, e.g.) have shown that companies that choose their AI path strategically rather than opportunistically are 3x more likely to achieve measurable business value within 18 months.
We’re seeing the same thing on the street. The organizations winning with AI aren't necessarily those with the biggest budgets — they're the ones with the clearest strategy aligned to their organizational reality.
The buy-boost-build framework provides clarity, but the decision ultimately comes down to honest organizational self-assessment. Most companies overestimate their building capabilities and underestimate the strategic value of boosting existing solutions with proprietary data.
Start with pilot programs that allow you to test your chosen approach with limited risk. Use these pilots to validate not just technical capabilities, but organizational readiness for AI transformation.
As leaders confront the challenge of turning AI into measurable business value, choosing the right path can determine whether you're driving transformation or being driven by it.
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