Why an Open Architecture for AI Matters for the Future of BPM

Every few days, a new AI model arrives that is faster, cheaper or smarter than the last. It is a good problem to have, until you have built your process management around one of them and the ground shifts.

That tension, wanting the best of a fast-moving technology without being tied to any single piece of it, is becoming one of the defining questions in BPM. This is where the idea of an open architecture for AI comes in, and it is worth understanding why it may matter more than any individual model you could choose.

BPM helps organizations understand, improve, execute and govern the way work gets done, from designing a process through to automating and running it. It exists so companies can keep pace as their goals shift and as regulations and markets change around them.

AI is the latest of those changes, and process teams are already putting it to work. They use it to create and analyze processes faster, to support compliance and improvement, and now to trigger process steps as they run. Used well, it takes over routine effort and makes process knowledge easier to reach.

For AI to produce reliable results, it needs the process knowledge held in the BPM platform: the models, roles, rules and controls that describe how the organization actually runs. As long as that knowledge stays accurate and governed, AI has something solid to work from.

Hint: Discover how organizations find, diagnose, and fix underperforming processes with AI.

BPM serves many roles, and each one has different AI needs:

• Process analysts want to create and refine processes faster, with documentation that keeps pace.
• Compliance teams need secure analysis of sensitive process and control data.
• Operational excellence teams want improvement ideas and recommendations that fit their context.
• Automation and operations teams want AI that helps build and run automated process steps reliably.
• Transformation leaders want easier access to knowledge across processes and the wider enterprise.
• IT and platform owners need AI that aligns with IT policy and enterprise AI strategy, with governance built in.

These needs pull in different directions. Some tasks call for the strongest cloud models, while sensitive data often needs a private or local model that keeps information inside your own environment. Routine, high-volume work runs better on something small and inexpensive. Across every use case, cost profile and compliance requirement, no single model covers them all.

The market makes the point clearer. New models arrive continuously, and their prices and capabilities keep changing, so a strategy built around one model locks you out of the next option that turns out better, cheaper or more compliant for your situation.

BPM sits at the center of operations, compliance and transformation, so tying its AI to a single provider concentrates a lot of risk in one place. That risk shows up on several fronts at once, from pricing and availability to data protection and regulatory compliance, and it extends to model performance, internal IT policy and long-term control over your roadmap.

Cost volatility is something that is already visible. Zylo’s 2026 SaaS Management Index found that 78% of IT leaders faced unexpected charges tied to AI or consumption-based pricing, with average spend on AI-native applications rising 108% in a single year. Because prices and terms can change after a contract is signed, budgeting gets harder.

Availability is another exposure. When your processes depend on one AI service and it goes down, that becomes a business continuity problem with no fallback, and once you have built deep custom integration with a single provider, moving away later means expensive rework.

Most enterprises already run multi-cloud for these same reasons. Multi-model AI follows the same logic. The aim is the freedom to evolve your AI strategy while keeping your BPM foundation intact.

An open architecture for AI keeps your BPM platform independent of any single AI provider, model or deployment method. It holds your business knowledge in one place and lets AI models connect to it through open interfaces, so no single vendor ends up owning your process intelligence.

The BPM platform becomes the stable business-knowledge layer, while the AI models sit above it and stay interchangeable. When a stronger model appears, a regulation changes or a cost shifts, you adjust the AI while the process knowledge underneath stays put.

ADONIS follows this platform-agnostic approach. Organizations choose the AI setup that fits their objectives, whether that means embedded cloud-based assistance, enterprise cloud infrastructure, local deployment or a specific model’s capabilities. Its AI ecosystem currently includes cloud models such as OpenAI, Azure OpenAI, Amazon Bedrock, Google Gemini and Anthropic Claude, alongside European models like Mistral that can run locally through Ollama. Because a different model can be configured for each use case, organizations can fine-tune the choice per task, using one model for document extraction and another for analysis.

This is a strategic choice as much as a technical one. You keep control over how AI enters your BPM environment, and your process intelligence stays connected, reusable and ready for whatever models come next.

A large language model carries broad general knowledge but knows little about how your specific organization works. To produce useful, reliable answers, it needs access to your trusted operational knowledge, from processes and the roles and responsibilities behind them to the applications, risks and controls in play, along with the handovers, dependencies and decision points that connect them.

This is where BPM earns its place in an AI strategy. Techniques like retrieval-augmented generation (RAG) connect a model to an organization’s own data so its answers stay grounded in fact, which IBM describes as a way to give models access to internal knowledge for more accurate, domain-specific results. Without that context, a model can return answers that sound plausible but are wrong. Give it the context and AI can tell you who signs off on a process step, or which control mitigates a given risk.

By holding that context in a structured, governed form, BPM gives AI the business reality it needs to be useful.

AI is moving from isolated tools toward assistants and agents that act across many systems at once. Coordinating that reliably requires a shared standard for how models connect to those systems, which the Model Context Protocol (MCP) provides. Anthropic introduced MCP in 2024 as an open standard for connecting AI models to external data and tools, and OpenAI, Google and Microsoft have since adopted it. In December 2025, it moved to the Agentic AI Foundation under the Linux Foundation, putting it on vendor-neutral ground.

MCP reinforces the same principle behind open architecture, keeping business knowledge separate from the AI model or agent using it. Access control and logging are handled at the connection point rather than inside the model, which means any model can be swapped in without changing who is allowed to see what. In this setup, ADONIS serves as the governed process-knowledge layer that AI systems draw on, independent of any single AI provider.

The pace of AI is not going to slow down. What leads the field this year will be one option among many by the next, and organizations that hard-wire themselves to a single model today will spend tomorrow unpicking that decision. The way to stay ahead of that churn is to not tie your foundation to it in the first place.

This is the thinking ADONIS is designed around. Your process intelligence stays put as the stable core, while the AI connected to it can be swapped, upgraded or rethought whenever a better option comes along, keeping you ready for whatever arrives next.