When adopting AI, one of the first things an organization must do is determine how to implement it into the existing technology ecosystem. Is it better to buy an off-the-shelf tool, borrow an existing solution and adapt it to your needs, or go all-in and build a custom tool from scratch? While each approach comes with its own unique set of advantages and challenges, determining the best strategy will depend on your specific goals, budget, timeline, and security concerns. Let’s explore the pros and cons of buying, borrowing, and building to help you identify the most effective strategy for your organization’s L&D transformation.
Weighing the Pros and Cons of the Three AI Implementation Strategies
Strategy #1: Buying a Third-Party AI Solution
There are already hundreds of AI solutions on the market, with more being added on a regular basis. These products can perform several important L&D functions such as generating images, creating audio, and analyzing large amounts of data.
Pros:
- Cost-Effective: Purchasing a third-party AI solution often provides a more affordable entry point. These tools are typically designed for broad use cases, allowing you to skip the cost of development.
- Ease of Integration: Many third-party AI solutions are built to integrate seamlessly with your existing learning management system (LMS). This reduces the time and technical expertise needed to implement them.
- Support and Maintenance: Third-party solutions often include vendor support such as regular updates, troubleshooting, and assistance, which can be critical for organizations with limited internal AI expertise.
Cons:
- Less Customization: Since these tools are designed for general use, they may not be flexible enough to meet all of your organization’s specific L&D needs.
- Ongoing Cost: Subscription fees, licensing costs, and maintenance expenditures can add up over time, making it potentially more expensive in the long run.
- Data Security: Sharing data with third-party vendors raises concerns about how the solution uses and stores sensitive information.
Strategy #2: Borrowing AI Tools and Expertise
The borrow strategy involves leveraging external expertise and resources to modify an existing AI tool and customize it to your needs. This typically involves partnerships with AI vendors, consultants, or other outsourced talent.
Pros:
- Lower Initial Cost: Existing AI technologies are sometimes free to use and customize, making them a cost-efficient option.
- Customizable: AI platforms give you the flexibility to modify algorithms and features to meet your organization’s specific needs.
- Community Support: With current AI tools, there is often a community of developers who improve the software, provide updates, and help to troubleshoot problems.
Cons:
- Integration Complexity: Some AI technologies may not implement seamlessly with your existing technology.
- Technical Expertise Needed: Modifying and maintaining some AI technologies requires technical proficiency. If your organization lacks the necessary expertise, you may need to hire developers, which can increase long-term costs.
- Data Security Concerns: Data privacy can be a risk if the tool lacks effective protective measures. Your IT team will need to ensure the platform is secure and aligns with your data privacy regulations.
Strategy #3: Building a Custom AI Solution
Building a proprietary AI solution from scratch allows you to tailor the system entirely to your organization’s needs.
Pros:
- Full Customization: You control every aspect, from data inputs and learning algorithms to how it integrates with your other systems.
- Enhanced Security: Since the solution is developed internally, you have complete control over data security, ensuring that sensitive data remains protected.
- Long-Term Cost Savings: While the upfront cost is higher, you won’t have to pay ongoing licensing or subscription fees to a third-party vendor, which can save you money in the long run.
Cons:
- High Initial Investment: Developing an AI tool from scratch is expensive and time-consuming. You’ll need to budget for development, testing, and implementation, which may require you to hire specialized staff or consultants.
- Longer Time to Implement: Custom solutions require significant time for development, testing, and refinement. If your organization needs to implement AI quickly, this option might not be feasible.
- Maintenance and Updates: Once the AI tool is built, your organization will be responsible for maintaining and updating it. This requires a team of developers and IT professionals, which can add to long-term costs.
How to Determine Which AI Strategy Fits Your Organization
Identify the Problem & Determine How AI Can Solve It
When researching your AI options, you should always start by identifying the problem you are trying to solve. One of the ways we use AI at GP Strategies is to assist with requests for proposals (RFPs) for potential client projects. The problem with RFPs is that they can be incredibly labor-intensive. A typical RFP contains 300–400 questions, which can take several days to answer.
Once you have identified your problem, think about how AI can offer a solution. While AI can do many things, it is particularly useful for automating tasks, generating content, and analyzing large datasets. For the RFP problem, we wanted AI to do all three of these things—analyzing the information from past RFPs, using that information to generate answers, and automating the initial responses. Once that was achieved, we would be able to customize our answers to the questions for a more effective response for the customer.
Use Project Parameters to Decide Whether to Buy, Borrow, or Build Your Solution
Once you have determined your problem and how AI can help you solve it, you can use other considerations such as budget, security concerns, and the technical expertise within your organization to help steer you toward the best implementation strategy. Since our RFP solution would be working entirely with proprietary information, and we have a great deal of technical experts on staff, we determined that the best solution was to build a solution ourselves, hosted in our secure environment.
The finished solution has shortened the cycle considerably and helps us provide authentic answers based on how we have responded to questions in the past. This frees us up to spend more time on the client’s needs.
Aligning Your Strategy with Your Organizational Needs
Ultimately, the best AI implementation strategy for your organization will depend on your unique combination of budget, timeline, technical expertise, security needs, and learning goals. By evaluating these factors, you can choose the path that maximizes both immediate impact and long-term value for your L&D function. Whether you decide to buy, borrow, or build, the key is aligning the solution with your organization’s overall vision for learning and development.
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