How AI agents strengthen SIAM in multivendor environments
For several years I have seen organisations explore generative AI with great expectations, only to find themselves frustrated by inconsistent results. This is especially true in multivendor environments where effective Service Integration and Management is essential. Business leaders want reliable operations, a clear operating model and the ability to scale without adding more manual work. Yet general purpose AI tools often behave unpredictably when the scope is unclear.
I believe AI can deliver real value in SIAM, but only when it is applied in a structured way. AI Agents offer this structure. They work inside defined roles and tasks, respect the organisation’s operating model and support the flow of work across teams and vendors. When designed well, they make SIAM operations more reliable, more scalable and far easier to manage, and bringing more business value at the same time.
Why SIAM organisations struggle with early Gen AI attempts
Many organisations start with broad questions. They ask AI to redesign a process or a piece of a process, draft a strategy or analyse a complex service issue with little context. In my experience this leads to vague or incorrect answers. The model simply does not have the boundaries or information it needs. What follows is not insight but guesswork.
This behaviour, often called hallucination, does not surprise me. I have spent decades working with business and IT operations, and I know that even humans struggle without structure. SIAM exists because roles, responsibilities, practices and processes matter. Without them, vendors interpret things differently, quality declines and governance becomes fragile. AI behaves in much the same way.
This is why I see role-based agents as a natural next step. They give AI the same clarity and constraints that guide human teams every day.
How role and task-based AI Agents solve the problem
AI Agents replicate the roles and tasks already defined in a SIAM operating model. A Role Agent behaves like a service manager, a development lead or a business analyst. It understands its responsibilities and expects users to follow the same way of working. When the role requires specific outputs, the agent asks a Task Agent to produce them or asks the user to start conversation with certain Task Agent.
A Task Agent is even more focused. It performs one activity such as creating a capability plan, summarising a case, analysing an attachment or preparing an improvement suggestion. I have found that this focus is what makes the output both reliable and repeatable.
This mirrors how we already work in SIAM. People take responsibility for their part of the operating model. AI can now do the same, without inventing steps or drifting from the process.
How AI Agents fit naturally into the SIAM lifecycle
In a higher level, one of the good practices is to divide organisation’s operating model to three different phases: demand management, development and services. I see opportunities for AI Agents in all three.
In the demand management phase, Role Agents help teams consider the right questions early. They support in business design, capability planning and business cases, guiding teams to document what truly matters. They ensure background work aligns with the organisation’s model rather than individual preferences.
In development, Agents help with feasibility checks, planning and design documentation. They work with the templates and information the organisation already trusts and ensure no essential element is missed.
In the services phase, the value becomes crystal clear. A Service Manager Agent can collect and review service data every day. It can combine information from platforms such as ServiceNow and Dynatrace, analyse feedback from users and highlight emerging issues. I think this is one of the strongest use cases. It provides a consistent view of service health without relying on manual effort.
Task Agents then pick up individual activities. They can collect data, prepare improvement suggestions, analyse attachments, complete summaries or support portal interactions based on available data. In my view, this frees service managers to focus on decisions rather than administrative work.
What organisations are already achieving
The business outcomes speak for themselves. One example I often share is user access authorisation. By allowing Task Agents to handle analysis and processing, organisations have achieved impressive results:
• 90 per cent faster authorisation processing
• Ten times the ticket handling capacity with the same team
• Fully automated flow after approvals
These are not theoretical benefits. I have watched organisations achieve them in live environments. Also, improvements appear in service management tasks such as attachment review, case summarisation and portal support. When work becomes repeatable and automated, quality increases and throughput rises.
How to begin the journey
From my perspective, the best starting point is not technology. It is clarity about your current SIAM maturity. Agents only work well when the operating model, roles and responsibilities are well defined. Once this is in place, choose one process that would benefit from more reliability or greater capacity. Introduce one Role Agent, then add the Task Agents that support its responsibilities.
Success depends on involving people who understand how your organisation works and what good looks like. AI cannot invent a strong operating model yet. It can only enhance the one you already trust. With clear processes, the right data and an open mindset, AI Agents can become a dependable part of daily SIAM operations.
I see this as a significant shift. AI is no longer an experiment or a side project. It becomes a partner in SIAM, something that strengthens governance and delivers scale where teams need it most. I believe organisations that take this structured approach will set a new standard for how SIAM operates in multivendor environments.
Jyri Kosonen works as CTO, Service Integration at Sofigate. He has worked for 30 years in various roles and companies within the ICT solution area, both in customer and supplier organizations. On his responsibilities have included helping clients build and implement strategies, procurement and budgeting of solutions, as well as building and managing solution and service business operations and processes.
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