Build vs. Buy: Key Considerations for Generative AI Adoption
The rise of generative AI has presented organizations with a compelling opportunity to transform their operations, enhance creativity, and unlock new levels of productivity.
But as companies consider integrating these powerful tools, a fundamental question arises: should they build their own generative AI solutions or buy existing ones from vendors?
This decision carries significant strategic implications, impacting everything from resource allocation to long-term competitiveness.
To offer our readers more practical information on this critical choice, we asked a panel of experienced tech leaders to identify the single most important factor organizations must consider in the build-or-buy dilemma for generative AI, and to explain why this factor is so crucial to the decision-making process.
Their insights offer a roadmap for navigating this complex landscape.
Read on!
Current State and Future Goals
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 to 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 are 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 not 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 it’s often relatively easier for smaller organizations to pull the trigger on buying generative AI solutions.

Pushkar Garg
Staff Machine Learning Engineer, Clari
Team Expertise
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.

Elmo Taddeo
CEO, Parachute
Data Ownership and Customization
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.

Michael Ferrara
Information Technology Specialist, Conceptual Technology
Data Security and Privacy
Data security and privacy are crucial factors organizations must consider when considering building or buying generative AI (GenAI) solutions. They should 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.
Scalability and Growth Goals
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 in-house 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.
AI and Core Competencies
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 an organization’s 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 is as an operational tool, like for example, 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.

Barkan Saeed
CEO, AIFORMVP
Uniqueness of Use Cases and Resources
Build vs Buy should depend on the uniqueness of their use case and resources.
If they are looking for Gen AI solutions that solve a generic need that most other enterprises also have, they should consider commercial solutions.
But if they have unique data and use cases, it’s highly unlikely that off the shelf commercial solutions would support them.
In addition to that, building Gen AI solutions requires a lot of software development and AI expertise and this talent is in hot demand right now.

Edward Wu
Founder and CEO, Dropzone AI
The Cost of Ongoing Optimization
After working with dozens of small businesses implementing AI solutions, I’ve identified a critical blind spot in the build-versus-buy decision:
While many organizations fixate on initial development costs, they often miss the substantial ongoing investments required for successful AI implementation. We are literally busy every day optimizing the system built 1 year ago.
I also consistently see companies underestimating expenses like model retraining, prompt engineering, and the need for specialized technical talent. This is especially crucial for small businesses, where resources are often limited.
In my humble opinion, selecting pre-built solutions can offer more predictable costs and faster time-to-value compared to in-house development.
We have different AI Agents for different tasks for small businesses and have found our clients to benefit greatly from the decision of buying these pre-built yet customizable solutions from us.

Danish Soomro
Founder & CEO, Devi AI
Finding the Right Experts
One critical factor when deciding to build or buy generative AI solutions is finding people with the expertise to create something tailored to your business.
Off-the-shelf tools might seem quicker, but they often fall short when it comes to meeting specific needs or offering a true competitive edge.
By working with experts who understand your industry, you can ensure your custom AI solutions are designed to address your unique challenges, integrate seamlessly with your systems, and evolve with your business.
For example, in logistics, a custom-built AI model could optimize your specific supply chain workflows far more effectively than generic software.
Plus, owning a solution designed just for your business helps protect proprietary insights and keeps competitors from gaining access to the same capabilities.
Building with experts ensures you get the best fit and a competitive advantage for the future.

Rob Lubeck
CRO, RTS Labs
Staffing Capabilities
One important factor to consider that really can’t be overlooked when deciding whether to build or buy is staffing capabilities.
For example, it is the smaller businesses that are typically at the biggest disadvantage in terms of having experienced coders on staff. Even if they have one or two, the task of building their own in-house generative AI solution may simply be too big of a task for that small of a team to handle.
Whether a company coding personnel altogether or don’t have many coders on staff to be able to take care of everything in-house, buying may be the most realistic choice to access high-quality generative AI solutions.

Edward Tian
CEO, GPTZero
On behalf of the Techronicler community of readers, we thank these leaders and experts for taking the time to share valuable insights that stem from years of experience and in-depth expertise in their respective niches.
If you wish to showcase your experience and expertise, participate in industry-leading discussions, and add visibility and impact to your personal brand and business, get in touch with the Techronicler team to feature in our fast-growing publication.