In 2025, the question facing every AI leader isn’t whether to build or buy — it’s how to do both, in the right order.
At the Zartis AI Summit, panelists from startups to global enterprises agreed: the smartest organizations start by buying to deliver instant value and prove a use case. Then, once the path is clear, they build custom systems to reduce long-term costs, improve performance, and own their competitive edge.
This article unpacks that insight and presents the Zartis AI Maturity Framework, a practical model that guides leaders through three phases — Experiment (Buy), Extend (Hybrid), and Evolve (Build).
Along the way, it explores real-world lessons from the summit, industry research from Gartner, McKinsey, and Deloitte, and the human factors that make or break every AI strategy.
Because in the end, AI success isn’t about choosing sides.
It’s about mastering the sequence — knowing when to buy, when to build, and how to orchestrate both to scale sustainably.
The Crossroads of AI Strategy
At the Zartis AI Summit, one debate sparked more discussion than any other:
Should you build your own AI solution — or buy one off the shelf?
Around the panel table sat leaders from startups, scale-ups, and enterprises, all wrestling with the same dilemma. Some were under pressure to deliver immediate results; others were thinking about scalability and ownership. But what united them was a single realization: this decision can make or break your AI strategy.
At Zartis, we’ve guided clients across both paths. We’ve seen fast wins from teams who used commercial AI tools to validate their ideas — and we’ve seen long-term value created by those who later invested in custom-built systems.
There isn’t a one-size-fits-all answer. But there is a sequence that works.
This article distills those lessons — and the collective wisdom of our panelists — into a strategic framework for leaders deciding when to build, when to buy, and how to do both wisely.
1. The False Binary: Why Build vs. Buy Isn’t Either/Or
“The best AI strategies don’t pick a side — they design for both.” – Panel insight
For decades, technology decisions came down to a clear divide: you either bought software or built it. But AI has blurred that boundary beyond recognition.
Today’s AI ecosystem is composable — models, APIs, and low-code tools can be integrated like Lego blocks. You can buy a chatbot, plug in an open-source model for reasoning, and layer your own data pipeline on top. The question isn’t build or buy anymore. It’s what do we build on, and when?
According to Gartner’s 2024 Composable AI report, 65% of enterprises now use hybrid AI architectures, combining commercial APIs with in-house models and tools. The smartest teams design systems that can evolve over time rather than locking themselves into one path from the start.
“Start with off-the-shelf tools to prove value; build custom when you’re ready to scale sustainably.”
That sequence is critical — and it starts with buying.
The Case for Buy: Speed, Simplicity, and Proof
“Buy first. Deliver something visible, fast, and safe. Then decide if it’s worth building.” – Panel insight
For organizations beginning their AI journey, buying is the pragmatic choice.
It delivers what executives and investors crave most — momentum.
Buying off-the-shelf or using ready-made APIs allows teams to deploy something real within weeks instead of months. It gives stakeholders a tangible demonstration of AI’s potential, builds internal credibility, and gathers early data to validate whether a use case is even worth scaling.
The advantages are clear:
- Speed to value: Go live fast and learn faster.
- Predictable cost: Subscriptions or API-based pricing make budgeting simpler.
- Lower risk: Proven, tested solutions handle compliance, reliability, and security.
A 2023 McKinsey study found that organizations delivering “quick AI wins” in their first year were twice as likely to achieve long-term success compared to those that focused solely on developing internal platforms.
In early stages, velocity matters more than perfection. As our panelists emphasized, “It’s not about showing how sophisticated you are — it’s about showing that AI can make a difference today.”
However, there’s a catch. The very convenience that makes buying attractive can also become a trap.
The Limits of Buying: The Plateau of Convenience
Off-the-shelf AI is like renting a high-end apartment: it’s convenient, comfortable, and pre-decorated — but you can’t knock down walls.
Many teams reach a “plateau of convenience” where the bought solution stops fitting their growth or ambition. At this point, several issues emerge:
- Vendor lock-in: Pricing and roadmap changes are outside your control.
- Limited customization: Generic models can’t capture your domain-specific nuance.
- Data ownership: Your data enriches someone else’s platform, not your own IP.
- Performance ceilings: Third-party systems can’t always meet enterprise-scale demands.
As one panelist remarked,
“Every SaaS shortcut comes with a long-term invoice.”
Deloitte’s 2024 AI Strategy survey backs this up: 41% of companies cited “lack of flexibility or customization” as the main reason they eventually moved from vendor AI to internal development.
In other words, buying can get you to proof — but it won’t get you to performance.
The Case for Build: Ownership, Optimization, and Differentiation
“Building gives you control — but control costs time and talent.”
At some point, organizations outgrow their rented space. The limitations of generic tools push them toward building their own AI systems — a move from convenience to capability.
Building means:
- Owning the core IP. You’re no longer at the mercy of a vendor’s roadmap.
- Optimizing for your data. You can fine-tune models to your context.
- Differentiating strategically. Your AI becomes a competitive advantage, not a commodity.
However, building demands patience. According to MIT Sloan (2023), only 10% of companies with internal AI labs report positive ROI within the first 12 months. The reasons are familiar: underestimated complexity, lack of internal expertise, and poor integration between technical and business teams.
Our panelists captured it best:
“Don’t start with build — earn the right to build.”
Building should be a strategic evolution, not a reflex. It requires mature processes, aligned stakeholders, and the discipline to know when complexity adds value — and when it just adds cost.
The Zartis Decision Framework: From Buy → Build
At Zartis, we often advise clients to think of the build-vs-buy dilemma as a journey, not a choice. The right decision depends on where you are in that journey — and what you’re trying to achieve.
From the insights of our AI Summit panel, we’ve developed a simple model for decision-making: the AI Maturity Ladder.
Phase 1: Experiment (Buy)
- Goal: Validate ROI, build confidence, and identify champions.
- Tools: Off-the-shelf AI APIs, SaaS platforms, plug-in integrations.
- Mindset: “Learn by using.”
- Success metric: Proof of value — small wins that generate buy-in.
Example:
A fintech team deploys OpenAI’s GPT-4 to automate customer support. Within weeks, call handling time drops 30%. The team now has real data and stakeholder excitement.
Phase 2: Extend (Hybrid)
- Goal: Customize workflows, integrate data, and prepare for scale.
- Tools: Mix of vendor APIs, orchestration layers, and lightweight retraining.
- Mindset: “Make it ours.”
- Success metric: Performance stability and user adoption.
Example:
The same fintech now connects the chatbot to its proprietary CRM, retrains the model on company FAQs, and uses prompt engineering for accuracy. They still “buy,” but their orchestration adds ownership.
Phase 3: Evolve (Build)
- Goal: Develop proprietary models and systems to control cost, performance, and compliance.
- Tools: Open-source models, internal hosting, MLOps pipelines, data governance frameworks.
- Mindset: “Build for the future.”
- Success metric: Long-term ROI, IP creation, and operational efficiency.
Example:
After a year, the fintech launches its own fine-tuned LLM on internal servers. Costs drop by 40%, accuracy improves, and the system becomes a strategic differentiator.
As we often tell clients:
“Buy to learn. Build to last.”
This isn’t theory — it’s how the best companies evolve.
Forrester’s 2024 “Progressive Internalization” report found that organizations following this staged approach (start buy → then hybrid → then build) achieved sustainable AI ROI 60% faster than those that jumped straight into custom development.
The Hidden Variable: People and Process
Technology decisions often look rational on paper — but their success depends on people.
The panelists were unanimous: the hardest part of building AI isn’t the code; it’s the coordination.
Building an internal AI capability demands cross-functional collaboration between engineering, product, operations, and compliance. It requires reskilling teams, redefining ownership, and nurturing a culture where experimentation is safe.
That’s why, as Accenture’s 2024 AI Transformation Study notes, 68% of failed AI initiatives collapse not because of technical limitations, but because of misaligned people and processes.
In many ways, this mirrors what Suda Ragmi from Nourish Care highlighted in his Summit session: the human layer is the make-or-break factor. Whether you buy or build, your people must feel ready to adopt and adapt.
It’s not just a procurement decision, it’s a cultural one.
Making the Decision: Questions Every Leader Should Ask
The build vs. buy dilemma isn’t solved by comparing features or price. It’s solved by asking the right questions.
Here’s what every leadership team should consider before making the call:
- Do we truly understand the problem?
If the use case isn’t validated, start with buy. Use the speed of commercial tools to learn fast. - Is this core to our competitive advantage?
If the answer is yes, you’ll likely need to build eventually — to differentiate and own the IP. - Do we have the talent and time?
Building requires AI engineers, data scientists, DevOps, and product teams aligned. Be honest about readiness. - Can we sustain it?
Every AI model is a living system — it needs maintenance, retraining, and monitoring. - What’s our risk tolerance?
Buying reduces risk but limits control. Building gives control but increases exposure. Which risk matters more?
“You’re not choosing between tools — you’re choosing between trajectories.”
That’s the essence of strategy — knowing not just what you need now, but what you’ll need to grow into.
The Future: From Ownership to Orchestration
“In the end, you don’t need to own every piece — you just need to own the outcomes.”
By 2025, the companies succeeding with AI won’t define themselves by what they own — but by how well they orchestrate.
AI ecosystems are becoming modular, distributed, and collaborative. The edge will belong to organizations that can blend third-party tools, open-source intelligence, and internal systems seamlessly.
Gartner (2024) projects that by 2026, 70% of enterprise AI workloads will operate on hybrid architectures combining vendor and in-house components. The future isn’t build or buy — it’s build and buy, in harmony.
Think of it like a symphony.
Vendors provide instruments. Your data is the score.
Your people — engineers, designers, analysts, leaders — are the conductors who make it sing.
The Zartis Perspective: Mastering the Sequence
After working with clients across industries, our takeaway is simple: the smartest leaders don’t rush to choose a side. They master the sequence.
- Buy to validate ideas and win early confidence.
- Hybridize to personalize and integrate.
- Build to scale, optimize, and differentiate.
This progression balances the energy of experimentation with the discipline of execution.
Because building too early wastes money.
Buying too long wastes opportunity.
The art lies in knowing when to switch.
In AI, as in leadership, timing is everything.
“The future of AI strategy isn’t about choosing sides — it’s about mastering the sequence.”
And that’s what we help our clients do — make decisions today that scale tomorrow.
References
- Gartner (2024). Composable AI: The Future of Modular Intelligence.
- McKinsey & Company (2023). The State of AI Report.
- Deloitte Insights (2024). From Off-the-Shelf to In-House: AI Strategy in Flux.
- MIT Sloan Management Review (2023). Why Internal AI Labs Struggle to Deliver ROI.
- Forrester (2024). Progressive Internalization in Enterprise AI.
- Accenture (2024). AI Transformation Survey: People, Process, and Performance.