The fastest path to agentic AI doesn’t start with agents. It starts with the assistants and automation that most mid-market companies haven’t fully deployed yet.
That’s a hard sell right now. McKinsey’s survey found that 62% of organizations are experimenting with AI agents, but AIIM’s research shows that only 3% have the automation maturity required to support them.
The gap between ambition and infrastructure has been widening for years, but the cost is compounding faster now.
Every quarter a team spends on an agentic pilot it can’t support is a quarter of proven productivity gains left on the table.
Assistants, copilots, and targeted automation deliver faster ROI, and they build the data pipelines, API integrations, and workflow foundations that agents eventually depend on.
Most teams are treating that step as optional. The data says it’s the foundation they can’t.
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Assistants are outperforming agents, and you aren’t paying attention
Medigold Health, a UK occupational health provider with around 1,000 employees, deployed Azure OpenAI to automate the generation of clinician reports.
No agents, no orchestration layers, just a well-scoped assistant on a painful workflow. The result was a 58% rise in clinician retention.
Ally Financial did something similar with Azure OpenAI for call summarization: 30% reduction in post-call effort across 700+ associates, from pilot to production in eight weeks.
These are straightforward implementations, and that’s the point.
In Deloitte’s Survey, 45% of leaders expect basic automation to deliver the desired ROI within 3 years. For basic automation combined with AI agents, that confidence drops to 12%.
Companies are pouring engineering capacity into the highest-complexity option while the highest-confidence option sits half-deployed.
So how do you know which one your workflow actually needs?
Low-variance, predictable processes such as payroll, report generation, investor onboarding, and password resets belong to assistants or automation.
Agents become more complex when the workflow involves high-variance inputs, cross-domain coordination, and multi-step reasoning that shifts based on context rather than rules.
If you can describe the job as “retrieve, process, respond,” an assistant handles it faster, cheaper, and with far fewer failure modes.
Getting this distinction wrong costs more than the budget. Repeated failures erode board confidence, shrink future AI budgets, and make teams skeptical before the next initiative even starts.
If agents are justified, the next question is how many
Once a workflow clears the bar with high-variance inputs, cross-domain data, and multi-step reasoning, the next step is an architecture decision. The default assumption is that complex work needs multiple agents. Usually, it needs one agent with the right tools. Multi-agent coordination earns its overhead only in a specific pattern: the work splits into parallel tracks that require distinct knowledge domains and can run simultaneously.
Fujitsu’s sales teams were spending days assembling proposals: gathering product specs, pulling market context, checking pricing, and drafting documents.
Using the Azure AI Agent Service and Semantic Kernel, they built a system in which specialized AI agents handle data analysis, market research, and document creation as parallel tasks, coordinated by an orchestrator.
The result was 67% productivity gain across 38,000 users. What made this a multi-agent problem was that each agent operated in a different knowledge domain, such as product data, competitive intelligence, proposal formatting, and that those tasks could run simultaneously rather than in sequence.
Microsoft’s own Store Assistant follows a similar pattern. A coordinator agent orchestrates five specialized skills across sales advice, technical support, live agent handoff, and conversation management — spanning hundreds of thousands of product pages.
The results: +142% revenue versus forecast and 46% fewer human transfers. No single assistant could hold that breadth.
So what separated these from single-agent problems?
Both workflows decompose into parallel tracks with distinct data, distinct reasoning, and clear boundaries between what each agent handles. Fujitsu’s agents didn’t pass work down a chain. They ran simultaneously across different knowledge domains.
Microsoft’s Store Assistant needed five specialized skills because no single assistant could span hundreds of thousands of product pages across sales, support, and conversation management.
That’s the test. If your workflow runs as a chain (where each step waits on the previous one), a single agent with the right tools will be faster and cheaper.
Multi-agent coordination earns its overhead only when the work genuinely splits into independent, concurrent tracks. Map the workflow before you pick the architecture.
Closing thoughts
One of the hardest parts of scaling agentic AI is getting your teams comfortable evaluating, governing, and trusting AI outputs in production. Every time a team reviews an AI-generated report, corrects a summarization error, or sets a quality threshold for an automated workflow, they’re building the operational discipline that agents will depend on.
That discipline comes from running AI in production, at low stakes, long enough for it to become routine.
The companies that lead in agentic AI by 2028 won’t be the ones that started with agents earliest. They’ll be the ones whose teams already know how to run AI well.
If you’re evaluating where AI fits in your workflows, we can help you identify the highest-value starting point.
