How to Build a Unified AI Strategy with Multiple Vendors Using Enterprise Architecture

Figure 1 - Building a Unified AI Strategy With Multiple Vendors Using EA.png

by Daniel Lambert

In today’s enterprise environment, simply adopting one AI platform or vendor isn’t enough. As organizations demand agility, innovation, and resilience, building a robust AI strategy means working across multiple AI vendors, and doing so within a well-defined enterprise architecture (EA) framework. You need a practical blueprint that aligns business, data, applications, and technology so that vendor proliferation becomes a strategic asset, not a chaotic liability.

Here’s how to do it.

1. Recognize the Reality: Your AI Projects Built in Silos

Many AI initiatives have been executed in isolation, by individual departments, with little regard for enterprise-wide integration. These siloed deployments result in duplicated efforts, conflicting analytics, disjointed customer experiences, and wasted investment.

When data is locked away in departmental systems, it’s nearly impossible for AI to deliver holistic insights.
Application sprawl compounds is a serious issue. One source reports that only ~28% of enterprise applications are integrated, and departmental AI agents often operate in isolation[i].
The result: multiple “shiny” AI proofs in individual functions, few of which can scale or align with strategic objectives.

This siloed approach is the opposite of what a unified, enterprise-level AI strategy demands. Recognizing this failure mode is step one. Call it out, don’t bury it.

2. Establish Clear Strategic Objectives and Use Cases

The first step beyond recognition is to ask what we want AI to do for the business. AI projects often fail because they aren’t grounded in business architecture. They’re driven by technology hype, not strategic alignment. Determining clear strategic objectives and use cases is imperative, as proposed here:

Identify your business goals (growth, cost reduction, new services) and map specific AI-use cases that directly support them.
Build a capability map: which parts of the business will be impacted, and what workflows will change.
Make sure that these use‐cases are vendor-agnostic at this stage (you’re defining the “what,” not the “who”).

Once you’re clear on where AI delivers value, you can invoke your EA layers (business, data, application, technology) to establish those use‐cases in the broader architecture.

3. Design a Multi-Vendor, Interoperable Architecture

If you have multiple AI vendors, you must make architecture decisions that reduce vendor lock-in, enable interchangeability, and ensure consistency, as explained here:

Use your architecture layers to decide how different AI vendor capabilities fit. At a minimum, you need to answer the following questions:

- Business layer. Which AI capability (e.g., customer service chatbot, predictive maintenance)?

- Data layer. What data is needed, how will it be governed, and shared across systems?

- Application layer. How does the vendor platform integrate (API, microservice, plugin)?

- Technology layer. What infrastructure is required: cloud, on-premises, or hybrid?

You might want to adopt a “composable” architecture where each vendor’s service fits into a larger ecosystem.
Architecture must explicitly prevent what occurs in siloed deployments: disconnected vendor systems that can’t share metadata, data, or decision logic.

The EA framework provides the backbone to align these vendor services. Without it you risk silos, duplication, and breakable dependencies.

4. Define Governance, Integration, and Data Architecture

A unified AI strategy across multiple vendors demands disciplined governance and integration planning. The key areas you need to focus on are as follows:

Data governance. AI depends on high-quality, well-governed data. Without it, you get garbage in → garbage out.
Vendor integration/interoperability. With multiple vendors, you’ll face heterogeneity of APIs, data formats, model behaviour, latency, and reliability. Planning integration via common data models and standard APIs helps.
Security, compliance & ethics. AI introduces new risks, like bias, explainability, and vendor black boxes. Your architecture must embed policy controls, auditability, and vendor onboarding criteria.
Architecture repository & artefacts. Use your EA tools and framework to document vendor services, data flows, dependency maps, and coverage across the architecture.

These areas ensure that your multi-vendor scenario doesn’t become uncontrollable.

5. Build the Vendor Selection and Orchestration Model

With the architecture and governance in place, you can build your vendor-orchestration strategy. How you select, deploy, and manage multiple AI vendors in coordination. Here are some of the best practices that you need to know:

Map vendor capabilities to use-cases. Not all vendors are equal. For example, one provider may be strong in real-time chat responses. Another may provide superior reasoning or domain-specific models. A multi-provider strategy exploits that.
Define fallback and routing logic. If vendor A fails, you can fallback to vendor B. If vendor C is better for reasoning tasks, route those there. Keep the user experience consistent.
Avoid lock-in. Architect vendor-agnostic interfaces and use standards where possible. That gives you negotiation leverage and flexibility.
Measure and optimise. Monitor cost, performance, relevance, and reliability across vendors. The architecture should include metrics and dashboards.
Plan for scale. As multiple vendors integrate into your ecosystem, ensure onboarding, monitoring, and lifecycle management are repeatable and aligned with your EA processes.

6. Link it All to an Architecture Roadmap

You now have elements: business objectives, architecture design, governance, and vendor-orchestration model. You finally need a roadmap that moves from the current state to the target state, as suggested here:

Use your EA framework to define “as‐is” and “to‐be” architectures, covering business, data, application, and technology layers.
Prioritise initiatives and projects like vendor integration pilots, data pipeline upgrades, governance framework rollout, and vendor-agnostic layer implementation.
Set milestones, ownership, and mapping to business capabilities.
Include risk mitigation and change management. New vendor models always require organisational adjustment, new skills, and updated processes.
Review and adapt. The AI landscape evolves rapidly. Your architecture must allow you to plug in new vendor capabilities as they emerge without re-architecting everything from scratch.

7. Monitor, Govern, Iterate

Execution is far from “set and forget.” Odds are very high that you will not get everything perfectly right the first time. In a multi-vendor AI architecture, iterations and adjustments are very important at several levels.

Govern usage and performance. Maintain architecture repository, update data flow diagrams, monitor vendor service health and compliance.
Review cost vs value. Are the vendor services delivering? Are you paying for overlapping or redundant capability?
Manage vendor lifecycle. On-board new vendors, retire old ones, and maintain versioning. Architecture artefacts must reflect changes.
Stay aligned with business. Ensure AI use-cases remain linked to business objectives. Metrics need to show value. Lack of strategic alignment is a major reason AI projects fail.
Architect for agility. The architecture must allow new vendor capabilities, evolving regulations, and shifting business priorities. Use modular components, microservices, and data mesh practices.

If you want a unified AI strategy with multiple vendors, do not treat each vendor as a one-off. You need enterprise architecture as the backbone to align business goals, design interoperable layers, enforce governance, and provide a roadmap and flexibility to evolve. Be disciplined. Build for interchangeability. Monitor relentlessly. And ensure every vendor decision serves the business architecture, not the other way around. You’ll then convert vendor multiplicity from a risk into a strategic advantage.

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[i] The 2024 Connectivity Benchmark Report by MuleSoft/Salesforce found that enterprises saw a 10% increase in their application footprint in just one year. The bigger problem? Only 28% of those applications are integrated, and 81% of IT leaders say data silos are actively slowing digital transformation.