Businesses face a critical decision when determining how much control to retain over the technology, data and third-party vendors sustaining their AI systems. Making that decision requires evaluating governance, operational dependencies, risk management and internal capabilities.
Integrating AI raises many issues, including choosing between building a proprietary solution and buying an off-the-shelf one—the build vs. buy dilemma. To complicate matters, the choice must be made against an evolving regulatory backdrop and a constantly shifting technological landscape.
- Should an in-house tool tailored to organizational needs be built or an existing commercial product be bought?
- Which functions should be controlled internally?
- What data can be shared with third parties, and who will have access to it?
- What technological or contractual dependencies stem from these choices?
The choice between building in-house AI capabilities and buying an off-the-shelf solution carries ramifications that reach far beyond the technology itself, requiring a broader analysis of governance and how much control an organization intends to maintain over its AI environment.
Building or buying an AI solution: a strategic decision
The business needs that AI can address vary widely across organizations and can include automation, productivity gains, decision-making support, data analytics and processing sensitive information.
Beyond the identified need, a strategic assessment must factor in several elements:
- Intended uses
- Data sensitivity
- Acceptable risk tolerance
- Internal capabilities
- Regulatory requirements
- Current and future needs
A commercial product can quickly address certain requirements without tying up a specialized internal team. Conversely, adaptability requirements or sensitive data processing can lead a business to favour in-house development.
The real issue is weighing the control each option offers against the dependencies it creates.
AI and data governance
AI governance is inextricably linked to data governance. When an organization entrusts data to a third party, it is not enough for it to simply know where that data is hosted. It must also have full visibility into the environment in which the data flows and who can access and process it.
Data is a strategic asset that organizations cannot expose without accounting for risk, particularly in sectors where confidentiality is critical. The legal sector is a prime example given the sensitive nature of client information, but the principle holds true across every industry.
Governance is not merely a matter of compliance. It also dictates how data is protected, organized and used to support organizational objectives.
Exercising governance requires a precise grasp of how data flows through the technical environment.
Organizations must be able to obtain clear answers to questions such as:
- Where is the data hosted?
- Who can access it?
- Which jurisdictions may have authority?
Even when a commercial vendor states that it stores data within a specific jurisdiction, its underlying infrastructure, redundancy mechanisms or third-party dependencies can create far more complex data flows.
Selecting an AI vendor: assessing technical and contractual dependencies
Selecting a vendor involves more than evaluating a solution’s capabilities or cost. Using a commercial AI solution can create technical and contractual dependencies that an organization must be able to identify and govern.
This means determining:
- Which third-party vendors may be involved
- What infrastructure underpins the solution
- Which jurisdictions are involved
This assessment should enable the organization to determine what it will control directly and what it will outsource to third parties.
Deciding whether to build or buy AI tools is not merely a technical choice —it dictates operational autonomy and exposure to third-party risk.
Integrating AI into a business: balancing adoption and critical thinking
For many organizations, employee adoption is already a concern. Some employees may hesitate to use AI, while others may embrace it too quickly without understanding how it works or what risks it carries. Considerations include:
- whether users will use AI securely;
- how to mitigate data leaks; and
- how steep the learning curve will be.
The challenge lies in building an environment where employees trust AI enough to use it, while continuing to exercise sound judgment and critical thinking.
Striking that balance requires not only robust governance frameworks, but also trained employees who can use AI and question its output when warranted. As AI capabilities evolve, human oversight will remain essential.
Long-term AI governance: future-proofing a business
What will an organization that has truly integrated AI look like in ten or twenty years?
It will have:
- Appropriate governance
- Clear lines of accountability
- The required skill sets
- A culture supporting deliberate AI use
- Control mechanisms
- High-quality information and data
An organization must be able not only to use AI, but also have full visibility into how its data is handled, how its vendor network operates and what risks come with AI use.
An AI strategy does not simply involve rolling out a new technology. It must rest on data governance, workforce skills, corporate culture and the controls necessary to drive responsible, value-generating use.
Making AI a collective decision
Whether an organization chooses to build its own AI capabilities or buy an off-the-shelf solution, the decision should never be made in a silo. Technical considerations are only one part of the equation.
Data, cybersecurity, data protection, legal and regulatory requirements, vendor dependencies, internal capabilities, costs, user adoption and business objectives are deeply intertwined. Looking at the decision through a single lens risks shifting risk exposure elsewhere rather than mitigating it.
Integrating AI successfully means bringing all key decision-makers to the table right from the start.
As AI takes on a larger role, businesses that take a unified approach to oversight will be far better equipped to make informed choices and turn AI investments into enduring, value-generating capabilities.
- Survey of 495 American AI decision-makers conducted from November 7 to 25, 2025. Participants included executives, VPs of engineering and product and leaders charged with AI procurement and development within organizations actively using AI. Read the 2025 survey.