Build vs. Buy
The AI Decision Every Company Must Make
Artificial intelligence is rapidly becoming embedded in the very tools organizations already use—CRM platforms, ERPs, office productivity suites, and collaboration software. At the same time, enterprises have the option to build their own AI solutions, tailored to their unique processes and data.
This creates a pressing question for leaders: Should we rely on AI features within vendor tools, or invest in building our own AI capabilities?
I don’t think the answer is straightforward. Each approach has benefits and risks, and the right path often depends on organizational strategy, governance posture, data readiness, and long-term vision.
The Vendor Tool Approach
The major technology providers—Microsoft, Salesforce, Adobe, SAP, ServiceNow, and others—are embedding AI features into their platforms. These come pre-integrated, supported, and regularly updated. And they bring a lot of advantages, such as:
Speed to value: Capabilities are available immediately. A sales team can use AI-driven forecasting or Copilot in Microsoft 365 without lengthy development cycles.
Integration with existing workflows: Vendor AI is built into tools employees already use, lowering adoption barriers.
Security and compliance assurances: Enterprise-grade vendors generally provide built-in compliance frameworks.
Lower upfront investment: No need for large development teams or infrastructure.
But there are also a number of limitations to consider, such as these:
Functional overlap and redundancy: Multiple tools may introduce overlapping AI features, creating confusion and inefficiency .
Limited customization: AI models and outputs are controlled by the vendor. Tailoring them to unique business needs is often difficult.
Vendor lock-in: Organizations become dependent on a provider’s roadmap and pricing models.
Data governance concerns: Sensitive data may flow into third-party systems, creating regulatory and ethical risks.
The Build-Your-Own Approach
Some organizations choose to develop AI solutions in-house—using foundation models, open-source frameworks, or custom-trained systems. The advantages of this approach are things like:
Tailored to business needs: AI can be optimized for unique processes, customer experiences, or proprietary data.
Competitive differentiation: In-house capabilities can become strategic assets, creating advantages competitors cannot easily replicate.
Control over governance and ethics: Companies can embed responsible AI practices into design, ensuring alignment with their values.
Data sovereignty: Sensitive data can remain within enterprise-controlled environments.
And again, there are limitations:
Higher upfront costs: Requires investment in infrastructure, talent, and training.
Skill shortages: Many organizations lack in-house expertise across data science, ML engineering, and responsible AI.
Longer time-to-value: Building custom AI solutions takes months or years.
Ongoing maintenance: Models require retraining, monitoring, and updating—demanding sustained investment.
A Hybrid Strategy: Best of Both Worlds
In practice, most organizations will adopt a hybrid model. Vendor tools are leveraged for productivity gains and quick wins, while in-house AI capabilities are developed in areas where differentiation and governance are critical.
For example:
Use vendor tools for knowledge worker productivity (e.g., document summarization, meeting transcription).
Build internally for customer-facing AI systems, proprietary decision-making models, or sensitive data environments.
This aligns with the parallel approach I advocate: organizations can adopt vendor AI features quickly while building custom capabilities in parallel .
Strategic Considerations for Leaders
When deciding between vendor AI and building your own, consider these guiding questions:
What’s the strategic importance of the capability?
If it touches your core IP or customer experience, build may be better.
What is your data posture?
Weak data foundations may limit the value of custom AI—start with vendor tools while building data maturity.
How fast do you need results?
Vendor tools provide velocity; custom builds provide control. Balance short-term and long-term needs.
What is your governance maturity?
Mature governance enables custom development; weaker governance may necessitate relying on vendor assurances.
AI adoption is no longer optional. But the choice between vendor features and custom builds is not a simple either/or. The best organizations blend both—leveraging vendor tools for efficiency while investing in custom AI for differentiation, governance, and long-term resilience.
Leaders should avoid two extremes: being locked entirely into vendor ecosystems or overinvesting in bespoke AI without clear ROI. Instead, balance speed and control, aligning decisions with business strategy and governance.
Ultimately, the decision really comes down to this: What role should AI play in your organization’s future, and how will you govern its path?
Yes