AI Collaboration as a Service

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November 18, 2025

The most valuable AI initiatives today are the ones that leverage strategic partnerships. Organizations that treat AI as a shared capability, and one that operationalizes learning, are moving faster and realizing more value. The author, Joakim Wahlqvist, global head of Data & AI, shares his insights on how you can strengthen your foundation for continuous learning and enterprise-wide scale.

Every enterprise now has some form of AI initiative underway. Models are stronger, cloud foundations are mature, and business teams are asking for real outcomes. AI adoption has shifted from IT-driven experimentation to business-driven transformation. Momentum is high, and the pressure from boards and leadership to act decisively on AI is real.

The MIT study “State of AI in Business 2025” found that the most common barrier to scale isn’t infrastructure or regulation – it’s learning. Systems that don’t learn from usage, don’t integrate with workflows, or don’t evolve with the business stay on the wrong side of the “Gen AI Divide.”

The study also found that organizations working through strategic partnerships are twice as likely to reach deployment as those doing it alone. The differentiator is how organizations partner and operationalize learning within their AI systems.

From the MIT State of AI in Business 2025

~30-40B

in enterprise Gen AI investment to date

5%

of integrated pilots deliver measurable business value

95%

of organizations are not realizing ROI.

Strategic partnerships succeed twice as often as internal builds.

The POC mindset – and the case for continuous collaboration

AI initiatives are traditionally run as short-term projects, often without persistent ownership or continuity. Teams assemble for a proof of concept, test an idea, and disband once results are in. The approach validates feasibility but rarely builds maturity. The next team starts again from zero – new people, new context, same questions.

Unlike digital transformation programs, AI adoption has no fixed endpoint. It keeps evolving as capabilities expand. Use cases emerge quickly; others shift or mature in new directions. The pace is more marathon than sprint, and the experimental mindset that drives early proofs of concept lacks the durability and governance needed for sustained progress.

What’s needed now is a flexible, collaborative model that sustains this evolution.

Introducing ACaaS

AI Collaboration as a Service (ACaaS) is built for continuity. It establishes a shared, long-term structure where internal and external experts operate as one core team. Rather than a series of short-term projects, it functions as an ongoing practice.

ACaaS brings together client’s front-runners and partner’s expertise into a single team, continuous framework – a joint core that designs, deploys, and evolves AI initiatives in step with business priorities. Each side contributes its strengths: the client’s domain insight and operational context meet the partner’s technical depth, accelerators, and cross-industry experience. Together they build a growing set of capabilities that improves with every iteration.

At a global life sciences company, an enterprise-wide collaboration model replaced fragmented pilots with governed, domain-specific systems built inside the client’s own secure environment.

Policy teams now produce validated research 70% faster, QA accuracy has improved by 40%, and connected engineering frameworks have become the foundation for ongoing innovation.

Each success strengthened the next, driven by the trust and capability built by continuous collaboration.

Know more

ACaaS emphasizes micro transformation – continuous improvement through focused, high-ROI initiatives. Each success builds on the last, creating a chain of measurable value over time. The result is steady gains in productivity, stronger integration between systems, and growing confidence across teams.

And a partnership built this way maintains progress through shared ownership and continuity. The joint team retains institutional knowledge, applies learnings across projects, and adapts to new requirements without losing momentum.

Guiding principles for AI collaboration

• Build for durability. Keep a core team that stays engaged.
• Focus on measurable value and keep ROI visible.
• Advance through micro-transformations rather than large, disruptive programs.
• Take the long view, progress compounds through continuity.
• Draw on external experience to accelerate internal growth.

Looking ahead

AI adoption no longer follows a start-and-finish pattern. Technology evolves daily, and the organizations that succeed will be those that evolve with it.

The next stage of enterprise AI

Business leaders will define clear, high-value use cases and develop custom AI that interacts with enterprise systems through tool-calling, custom logic, and intelligent interfaces.
As maturity grows, multiple teams will operate in parallel, creating a true AI factory environment.
Progress will be continuous, driven by learning, grounded in value, and shaped by collaboration.

For enterprises building their next phase of AI maturity, the way forward lies in forming durable teams, aligning around business outcomes, and creating systems that learn alongside the organization.

A strong strategic partnership builds continuity and knowledge retention across the AI journey. The next chapter will belong to those who think long-term, act collaboratively, and design their partnerships to learn as fast as technology itself.

Talk to an expert to explore how AI Collaboration as a Service can strengthen your foundation for continuous learning and enterprise-wide scale.