The one most important factor that an organization must consider when evaluating build vs buy for generative AI solutions is what state they are in currently and what state they want to achieve and in how much time. Buying gives you quicker access to a technology and probably a shorter lock-in period can be negotiated while building needs a longer commitment to use a certain technology or product. In the case of generative AI, I think, it should be a relatively easier decision for more than 90% of the organizations and that should be buy. The reasoning for this opinion is that generative AI is evolving so quickly nowadays that if you try to build a solution in-house there is a 50% chance that there would be a better way to implement it when you would be done. Additionally, a lot of the generative AI use-cases are experimental, so it is not a given that an organization would like to continue spending money on generative AI solutions if it does give enough value. So, if your organization is in the evaluation stage, I would recommend experimenting with a third party vendor. After the Proof of Concept is done, then it should be evaluated whether it makes sense to build it in house and dedicate more resources to it or keep working with a third party vendor. Data sharing is often a critical piece when using third party solutions for generative AI solutions and is often highly scrutinized by the internal InfoSec teams. So its often relatively easier for smaller organizations to pull the trigger on buying generative AI solutions.
One critical factor to consider when deciding whether to build or buy generative AI solutions is the availability of skilled talent within your organization. AI development demands highly specialized expertise, from data scientists to machine learning engineers. If your team lacks experience in AI tools or frameworks, building in-house could lead to delays, errors, or even failed projects. I've seen businesses underestimate this challenge and end up overspending on talent acquisition or training, only to find their solutions lagging behind competitors who opted for ready-made tools. When I helped a mid-sized firm integrate AI-driven compliance tools, they initially wanted to build their solution. However, their IT team had limited AI experience, and hiring experts would have added months to the project timeline. We recommended purchasing a proven platform instead. The firm was up and running within weeks, saving both time and money. Decisions like this illustrate how buying prebuilt tools can provide immediate functionality while freeing your team to focus on their strengths. Evaluate your team's expertise carefully and involve them in discussions about the project. Encourage input from end-users who'll rely on the solution. If building in-house is feasible, ensure clear timelines and budgets are in place. Otherwise, buying a commercial AI solution allows you to hit the ground running with expert support from the vendor. This approach ensures you stay competitive and meet business goals efficiently.
Deciding whether to build or buy a generative AI solution often comes down to **data ownership and customization needs**. Here's how I think about it. Imagine generative AI as a recipe book. Buying a cookbook (third-party AI) gives you ready-made recipes, but they may not suit your unique taste-or business needs. If your recipe (data) involves secret ingredients (proprietary information), you might not want to share that. Building your own ensures control over what goes in, how it's used, and the results you get. Pre-built solutions save time but can lack the flexibility for specialized needs. Building takes more effort but allows precise tailoring. This decision matters because generative AI isn't just a tool; it's part of your strategy. Missteps here can lead to wasted resources or solutions that don't deliver. For me, it's always about balancing control with effort-and that's where the choice becomes clear.
Data security and privacy are crucial factors organizations must consider when considering building or buying generative AI (GenAI) solutions. This factor must heavily influence the decision-making process because GenAI, by its nature, learns from and often retains aspects of the data it's trained on. The implications are significant for organizations dealing with sensitive customer data, proprietary information, or regulated data subject to compliance requirements like HIPAA or GDPR. Building an in-house GenAI solution offers greater control over data security. Organizations can implement robust security measures from the ground up, tailoring them to their specific needs and compliance obligations. They can choose where data is stored, how it's processed, and who has access. This level of control is significant for industries like healthcare and finance, where data breaches can have severe legal and reputational consequences. Furthermore, owning the model allows for greater flexibility in customizing its outputs and ensuring alignment with internal ethical guidelines. However, building requires significant investment in specialized talent, infrastructure, and ongoing maintenance. Buying a pre-built GenAI solution can be faster and more cost-efficient upfront. These solutions usually have built-in security features and may benefit from the provider's compliance certifications. However, organizations relinquish some control over their data. They must rely on the vendor's security practices and data handling policies, which might not fully align with their stringent requirements. Sharing sensitive data with third-party providers also introduces potential data breaches or misuse risks. Furthermore, customization options might be limited, potentially restricting the model's applicability to specific organizational needs. Ultimately, the decision hinges on a careful risk assessment. Organizations must weigh the cost and complexity of building against the potential security and privacy implications of entrusting their data to a third-party provider. Factors like the sensitivity of the data, the organization's internal resources and expertise, the desired level of customization, and the specific regulatory landscape should all inform the final decision. A hybrid approach might be appropriate for some organizations, leveraging pre-built models for less sensitive tasks while building custom solutions for critical applications requiring maximum data security.
One critical factor an organization must consider when deciding whether to build or buy generative AI solutions is scalability. The scalability of the solution will directly impact how well it aligns with the company's growth goals and future needs. A "build" approach allows for customized scalability tailored to your unique operations, but it requires significant investment in technical expertise, infrastructure, and ongoing development. On the other hand, buying an off the shelf solution provides immediate scalability benefits but may limit customization and long-term adaptability. The decision making process must weigh whether the organization has the inhouse capacity and long term resources to maintain and evolve a proprietary solution or whether a prebuilt solution is more cost-effective and functional for immediate needs. Scalability matters because a solution that cannot grow with your business will lead to bottlenecks, inefficiency, and, ultimately, higher costs down the line. A great example of this comes from my experience coaching a midsized healthcare company in the UAE that was grappling with this exact decision. They were considering building a generative AI platform to streamline patient intake processes but were concerned about the initial costs and technical hurdles. After evaluating their internal capabilities and growth projections, I recommended they invest in a hybrid solution. They purchased a robust off the shelf AI platform with scalable architecture and worked with an external developer to layer on customized features specific to their industry. This approach allowed them to rapidly implement the solution while maintaining flexibility for future growth. Within 18 months, their operational efficiency improved and they saw a significant reduction in staff turnover due to a smoother workflow. My years of experience in business strategy and my MBA in finance enabled me to help them make a financially sound decision that supported both their short term needs and long term goals. This success highlights the importance of making scalability a cornerstone of your decision when exploring generative AI solutions.
One factor an organization must consider when buying generative AI solutions is strategic alignment with core competencies and goals. If generative AI is key to organizations' offerings and value proposition, building a solution in-house may be a better choice. For example, a company specializing in customer service with very good internal data that can be used to train generative AI may develop or fine-tune its own generative AI solution to align closely with customers and have greater control over customization. If the purpose of using generative AI as an operational tool like for example you use a tool to handle your website's customer service or analyze your competitors websites regularly for updates. Pre built solutions can offer you faster deployment, and better value. Organizations can assess these goals carefully.
When deciding on whether to builld or buy generative AI solutions, the dictum "Know Thyself" should be a top priority. Gen AI solutions are drug-like in their addictive properties. By default, they are programmed to play closer and more exact attention to everything you say/output than any human being. The possibilities can seem endless and they can provoke minds into a frenzy. The strategic difficulty with Generative AI is that it is both a game-changer and an accelerator. Nowhere has this become more obvious than in the domain of SEO content generation, A simple SWOT analysis for a marketer might look as follows: Strength - Understands SEO Weakness - Can only hand-write a few SEO-optimized articles per day. No skills to build an AI solution. Threat - Gen AI can write hundreds of articles per day Opportunity - Gen AI based solutions such as Scalenut and Spinrewriter cost less than 100 Dollars/Euros per month The obvious conclusion is that the marketing professional in questions should purchase a Gen AI based solution. It's inexpensive and it will make them more competitive. However, how about the example of financial institution that needs to carry out semantic analysis of hundreds of thousands of financial reports per month, in many different proprietary categories and is tightly constrained by privacy regulations? Strengths - Has both software engineers and deep local understanding/authorship of report formats. Weaknesses - Report reviews can take weeks at a time. Hundreds of report categories. Threats - Competitors constantly fine-tuning their own financial analysis methodologies Opportunities - Reduce review time to hours using Gen AI automation In this use case, there are clear reasons for building an in-house Gen AI solution. The financial institution can improve its competitiveness without surrendering its IP or data processing to a third party. The scale of the task also means it's likely less expensive to build than to outsource. Of course, there are many ways to "know thyself" and SWOT is just an example. The point is that the traditional approach of constantly gathering and understanding your own requirements should still be top priority. Gen AI does not change that. Your business is non-monolithic - it consists of many different needs. The larger it is, the more it will compete on different fronts, from your supply chain through to customer retention. You'll quickly find that Gen AI has a unique relationship to each need.