When to Buy and When to Build AI: A Comprehensive Guide
Deciding whether to buy off-the-shelf AI products or to build custom solutions in-house is one of the most critical and complex decisions organizations face today. It’s not a one-size-fits-all scenario, and the choice heavily depends on various factors unique to each organization. This guide aims to shed light on these considerations, helping you make an informed decision that best suits your business needs.
Is AI Strategic for Your Business?
At the heart of the decision-making process lies a fundamental question: Is AI development a strategic initiative for your organization? In simpler terms, will AI solutions provide a competitive advantage that you actively seek to protect and enhance over time?
If AI serves merely as a tool for incremental improvements easily replicable by your competitors, then off-the-shelf solutions or custom builds by external consultants are your best bets. Building in-house for a minor competitive edge is often an inefficient use of resources.
Conversely, if AI solutions can offer a significant and hard-to-replicate competitive advantage, it might be worth building in-house. Ask yourself: do we have unique access to data, superior capabilities, or any other factors that make it difficult for competitors to replicate? If you can answer affirmatively, then dedicating resources to build in-house AI solutions could be a strategic investment.
Research the Market
Don’t underestimate the value of thorough market research. There are numerous off-the-shelf AI solutions designed to address a wide array of business needs. Too often, organizations skip this step and end up investing significant time and money into solutions that already exist.
Example: I once encountered an organization that embarked on an in-house AI project, only to discover they were replicating what an established vendor with over 14,000 clients was already offering. Eventually, they had to abandon their project after sinking substantial resources into it, demonstrating the importance of diligent market research.
In For a Penny in For a Pound (of AI)
A critical rule of thumb is: “When an organization does something it doesn’t do regularly, it will execute it poorly.” This principle is particularly pertinent to AI projects. If AI development isn’t part of your organization’s core activities, chances are the project will face significant hurdles.
If your organization doesn’t frequently undertake AI projects, consider outsourcing. Building AI capabilities involves more than just technical skills; it demands a culture and process alignment that infrequent projects rarely achieve. This isn’t to discourage you but to emphasize the deliberate effort needed for successful AI implementation.
Size Matters
AI projects generally require a larger initial investment than traditional IT projects, largely because they necessitate a specialized skill set, including engineers, machine learning developers, data scientists, and product managers.
Your organization needs to be of a certain size and capacity to absorb these costs, as AI projects often follow a trial-and-error methodology and don’t guarantee immediate returns. Ensure you are prepared to support a team of at least 4-5 members for an extended period without expecting immediate revenue or savings.
Get Your Data Straight
Quality data is the backbone of any successful AI project. Before diving into AI development, prioritize establishing a robust data operation. High-quality, low-cost data processes provide a significant competitive edge.
Just like supermarket chains with efficient purchasing operations can offer lower consumer prices, businesses with superior data operations can develop better AI solutions, even if their AI capabilities aren’t the best. Example: Imagine a company with impeccable data collection and management processes. This company would outshine competitors with even superior AI capabilities simply by virtue of having better, cheaper data.
Building AI Is Getting Easier
The barriers to starting AI projects are lowering. Techniques like Transfer Learning and AutoML have made AI development more accessible and less resource-intensive. Organizations that once needed PhDs in data science and thousands of coding hours can now achieve similar milestones more easily.
As AI technology continues to evolve, building in-house AI solutions will become increasingly viable for more businesses. The ease of entry should encourage businesses to explore AI development in-house, provided they consider the outlined factors.
In conclusion, the decision to buy or build AI depends on your strategic vision, market research, organizational capabilities, data quality, and available resources. Reflect on these aspects to make the best choice for your business.
Ready to make a decision? Let’s hear your thoughts! Are you leaning towards buying or building your next AI project?