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AppliedAI and McKinsey & Company announced on May 22, 2026 that they are collaborating to help regulated enterprises rewire mid- and back-office operations with agentic AI. The release describes a serious enterprise approach: McKinsey transformation support, QuantumBlack technical depth, and AppliedAI's Opus platform for governed, auditable process execution.

That is a strong signal for the market. AI is moving away from disconnected chat experiments and toward operational workflows that handle real process steps, connect to business systems, preserve evidence, and hold up in regulated environments.

It is also not the version most small and mid-sized companies can buy. Most SMEs do not have enterprise transformation budgets, internal AI teams, McKinsey-scale change programs, or months to rewire a function. They still need the same outcome: practical AI that makes work faster, cleaner, and easier to manage.

The enterprise AI playbook is finally becoming practical. Rockwell AI brings that same operating logic to SMEs without the enterprise overhead.

why this is a good step

The AppliedAI and McKinsey release focuses on a problem that matters: model capability is no longer the only constraint. The harder work is turning AI into governed, auditable workflows that run inside actual operations.

The example in the release is vendor onboarding for a regulated European chemicals manufacturer. AppliedAI and McKinsey say the process moved from roughly two weeks to under five minutes of active processing, with a greater than 99 percent reduction in manual effort. Whether a company operates at that scale or not, the pattern is worth paying attention to.

• The workflow was specific, not abstract. Vendor onboarding is a real operating process with documents, checks, follow-ups, and approvals.
• The work was regulated, which means governance and evidence could not be treated as afterthoughts.
• The value came from reducing manual coordination, not from replacing the entire business function.
• The process owners stayed close to the work instead of handing the whole project to a distant technical team.

why the enterprise model is out of reach for most SMEs

The challenge is that enterprise AI programs are built for enterprise conditions. They assume large process volumes, complex systems of record, formal transformation offices, executive steering committees, long procurement cycles, and dedicated implementation teams.

SMEs usually operate differently. The workflow may be spread across a CRM, email, spreadsheets, accounting software, shared drives, and a few people who simply know how things get done. The process is important, but it may not justify a major platform rollout or consulting engagement.

That does not mean AI is out of reach. It means the implementation has to be smaller, sharper, and closer to the business. The right SME AI project should start with one workflow, one clear owner, one measurable outcome, and a design that the team can actually maintain.

what SMEs should copy from the enterprise playbook

The best part of the AppliedAI and McKinsey announcement is not the scale. It is the discipline. SMEs can borrow the useful parts without copying the entire enterprise apparatus.

• Start with a high-friction process: Find the work that creates repeated delays, handoffs, data cleanup, follow-ups, or avoidable review time.
• Keep governance built in: Define what the AI can read, what it can draft, what it can update, and what requires human approval.
• Connect existing tools: The goal is not a shiny new dashboard. The goal is to make the tools the company already uses work together with less manual effort.
• Measure the operating gain: Track time saved, turnaround time, error rates, response quality, exception volume, and adoption by the people closest to the process.
• Improve after launch: Treat the first version as a working system that gets tuned, not a one-time automation handoff.

where Rockwell AI fits

Rockwell AI is built for the companies that see the enterprise AI direction clearly but do not need an enterprise AI program. We help operators turn one messy workflow into a practical AI system: scoped, connected, reviewed, measured, and improved.

That can mean a vendor onboarding workflow, client intake process, compliance review queue, lead qualification flow, internal reporting system, customer support assistant, finance operations brief, or company knowledge layer. The common thread is that the build starts where the work already happens.

For SMEs, the advantage is speed and focus. Rockwell does not need to rebuild the company. We identify the workflow, map the handoffs, connect the tools, build the automation or agent layer, create the approval path, and help the team measure whether it is actually working.

what Rockwell would build first

The first SME AI project should be useful enough to matter and bounded enough to ship. Rockwell would usually look for a workflow with clear inputs, repeatable decisions, visible manual effort, and a human owner who can review exceptions.

• Client or vendor onboarding: Collect documents, check completeness, draft follow-ups, flag missing information, and preserve review evidence.
• Sales intake and qualification: Enrich leads, summarize fit, draft outreach, update the CRM, and route high-value opportunities.
• Compliance operations: Monitor obligations, organize case notes, prepare review summaries, and escalate items that need human judgment.
• Internal reporting: Pull updates from spreadsheets, databases, project tools, and documents into a weekly operating memo.
• Customer support workflows: Summarize context, draft responses, classify issues, and preserve a clean trail for escalation.

the non-enterprise path to useful AI

AppliedAI and McKinsey are showing what AI-enabled process redesign can look like at enterprise scale. That is good for the market. It raises the standard from demos to real workflows.

But SMEs need a different path. They need the same discipline in a smaller package: one workflow at a time, designed around the tools they already use, with governance and measurement baked in from the start.

Work with Rockwell's fractional AI team, or explore custom AI agent development if you want the practical version of enterprise AI: scoped, governed, connected to your tools, and built for the way your team actually works.