How we cut through paralysis by analysis to identify the best focus for your AI efforts
Bad decisions are expensive.
Indecision costs more.
By 2030, any organization that remains competitive will be AI-enabled end-to-end. A central nervous system, made up of increasingly specific subsystems, will synchronize data and coordinate efficiencies across every department and function. This total organizational intelligence, built on an agile, modular architecture, will confer unimaginable advantages.
This 5-year AI vision informs everything we have to say about the buy vs build dilemma.
The optimal path might be pure buy. It might be pure build. It might be buy and customize. It might be buy then build later when limitations become too costly. It might be build with commercial components. It might be multiple commercial tools orchestrated together. It might be something else entirely.
Are you capable at arriving at the optimal choice for your specific context?
For example, a large language model like Claude or ChatGPT might get you partway there. As long as you understand the model’s limitations and risks, and operate within those, you can get some efficiency gains. Once you outgrow the model’s capabilities, it might be time to look at a custom solution.
The above sequence may be perfect for one company yet terrible for another.
Commercial AI products are built for their target markets, not your specific organization. Some target extremely narrow niches: e.g., AI tools built specifically for interventional cardiology or freight rail scheduling. Others target broader markets. But none are adapted to your unique combination of processes, data, regulations, and constraints.
The specialization level doesn't predict success. A highly specialized tool can fail if it solves a slightly different version of your problem. A general-purpose tool can succeed if your needs align with its capabilities.
Both commercial and custom solutions can be simple or complex to implement. A highly specialized commercial tool might deploy in days, while a "simple" custom solution could take months due to data preparation requirements. The complexity depends on how well the solution matches your existing technical and operational environment.
Commercial solutions aren't inherently faster to deploy. Some require extensive configuration, integration, and testing. Some custom solutions can be built quickly using modern development approaches and existing organizational capabilities.
Sometimes commercial solutions adapt to your workflows. Sometimes they require you to change. Sometimes custom solutions require the same process changes because your existing processes are inefficient or inconsistent.
The direction of adaptation depends on the specific product and your specific processes, not on whether the solution is commercial or custom.
Many AI initiatives fail on non-technical obstacles. Missing policies, inconsistent processes, fragmented data governance, company culture, and human habits often trip initiatives up.
These dependencies often determine project success more than the technical solution itself. Organizations frequently discover them too late in the process, after they've already committed to an approach that assumes the dependencies don't exist.
Commercial solutions create ongoing dependencies that organizations rarely calculate. Custom solutions have different cost structures. The financial profile depends on the specific vendor, implementation approach, and organizational context.
Total cost of ownership calculations require understanding license fees, customization costs, integration expenses, training requirements, ongoing support, and switching costs. These variables affect commercial and custom solutions differently in each situation.
Some commercial vendors provide excellent support. Others provide minimal support or disappear entirely. Some custom solutions have robust ongoing support relationships. Others leave organizations stranded when key developers leave.
The quality of ongoing support depends on the specific vendor or development partner, not the approach category.
While organizations debate endlessly, competitors move forward. The opportunity cost of indecision often exceeds the cost of making the "wrong" decision.
Extended evaluation processes consume internal resources while delaying value creation. Perfect decisions are impossible. Organizations that move quickly with good decisions outperform those that wait for perfect ones.
Organizations rarely have the knowledge needed to make optimal build vs buy decisions. They can't accurately assess vendor stability, integration requirements, hidden costs, or implementation timelines. They don't know which variables matter most for their specific situation.
This creates an arbitrage opportunity. Expert guidance that costs $50,000 can prevent $500,000 in mistakes. The return on investment for decision-making expertise often exceeds 10:1.
Expert analysis may reveal that a hybrid approach delivers better results at lower cost than “build” or “buy.” Organizations can purchase commercial components while building custom integration layers. They can develop core capabilities internally while licensing supporting technologies.
More importantly, expert evaluation can identify non-technical dependencies early and design solutions that eliminate them rather than requiring months of organizational changes.
Optimal build vs buy decisions require systematic evaluation frameworks that weigh all relevant variables. Organizations need methodologies that balance technical feasibility, cost implications, strategic alignment, and competitive impact.
Talbot West’s APEX framework evaluates every AI initiative across five dimensions:
These criteria apply whether you're evaluating commercial solutions or custom development. A commercial solution might score high on technical feasibility and cost but low on strategic alignment. A custom solution might score high on strategic alignment and biggest impact but require careful evaluation of cost and complexity.
The most successful organizations optimize for long-term competitive advantage. They evaluate how each choice positions them for future capabilities, not just immediate needs.
The build vs buy decision appears binary, but a hybrid approach often scores highest on APEX. Remember, build vs buy is an optimization problem, not a binary choice. Success requires evaluating all possible approaches and selecting the one that delivers optimal results for your circumstances.
Organizations that can't make build vs buy decisions lack decision-making frameworks. They need systematic approaches that weigh all relevant variables and optimize for long-term success.
The cost of decision-making expertise is minimal compared to the cost of wrong decisions. Organizations that invest in clarity avoid expensive mistakes and competitive disadvantage. They move faster, execute better, and achieve superior results.
The choice is between making optimal decisions and living with suboptimal outcomes. In a world where AI capabilities determine competitive advantage, getting these decisions right isn't optional—it's survival.
Ready to cut through the complexity? Contact Talbot West to explore how our APEX framework can help you make optimal AI implementation decisions. We'll help you avoid expensive mistakes while positioning for long-term competitive advantage.
We’ll help you see both the big picture and the tactical steps that deliver value today while establishing a competitive position for tomorrow.
Talbot West brings Fortune-500-level consulting and business process discovery to the mid-market. We then implement cutting-edge AI solutions for our clients.
