AI made the work faster. But it all still flowed into the same human review queue.

More drafts. More alerts. More recommendations. Same people deciding what happens next.

The answer isn’t faster review. 𝗜𝘁’𝘀 𝗱𝗲𝗰𝗶𝗱𝗶𝗻𝗴 𝘄𝗵𝗮𝘁 𝗻𝗲𝘃𝗲𝗿 𝗻𝗲𝗲𝗱𝗲𝗱 𝗿𝗲𝘃𝗶𝗲𝘄 𝗶𝗻 𝘁𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝗽𝗹𝗮𝗰𝗲.

That’s what orchestration is for.

A well-designed system knows what to:

→ 𝗛𝗔𝗡𝗗𝗟𝗘 routine work within clear limits. → 𝗩𝗘𝗥𝗜𝗙𝗬 against policy, rules, and expected outcomes. → 𝗙𝗜𝗟𝗧𝗘𝗥 duplicates, noise, and low-value signals. → 𝗥𝗢𝗨𝗧𝗘 work to the right owner or system. → 𝗘𝗦𝗖𝗔𝗟𝗔𝗧𝗘 uncertainty and high-consequence decisions.

Humans handle the judgment calls. Sampling and audits check what did not escalate.

Those findings improve the rules, not just clear today’s queue.

It is how I run my own agent fleets.

Because if your dashboard counts AI output but ignores the human hours needed to review it, it isn’t measuring productivity.

𝗜𝘁’𝘀 𝗺𝗲𝗮𝘀𝘂𝗿𝗶𝗻𝗴 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻.

The goal isn’t to remove humans. It’s to stop making human attention the tollbooth every piece of work must pass through.

AI makes output abundant. 𝗛𝘂𝗺𝗮𝗻 𝗷𝘂𝗱𝗴𝗺𝗲𝗻𝘁 𝘀𝘁𝗮𝘆𝘀 𝘀𝗰𝗮𝗿𝗰𝗲.

The winning systems won’t generate the most. They’ll know 𝘄𝗵𝗮𝘁 𝗻𝗲𝘃𝗲𝗿 𝗻𝗲𝗲𝗱𝗲𝗱 𝘁𝗼 𝗿𝗲𝗮𝗰𝗵 𝗮 𝗵𝘂𝗺𝗮𝗻 𝗶𝗻 𝘁𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝗽𝗹𝗮𝗰𝗲.

If you’re building this into your workflows DM me for the escalation scorecard we use: five metrics, how to measure them, and what we check before expanding autonomy.

From Pilots to Platforms

“AI agents are going to kill SaaS.” That sounds dramatic. The reality is more interesting. Today, a person might open a CRM, check a lead, update its status, write an email, create a task, and notify the team across several different tools. Some teams are already using AI agents to simplify parts of workflows like these, and that capability is only getting broader. An AI agent could increasingly handle that workflow across those systems for them. That matters because people may need to spend less time manually working inside every software interface. But the software itself doesn’t simply disappear. The agent still needs the CRM data, permissions, APIs, payment systems, integrations, business rules, and infrastructure behind it. And this is where the hype often gets ahead of reality. An agent can misunderstand context. It can work with incorrect data. It may not have the right permissions. An API can fail or change. An integration can break. A business may have an unusual case the agent wasn’t prepared for. And when sensitive customer or financial data is involved, security becomes a much bigger question. So yes, the shift is real. Gartner estimates that up to $234B in enterprise application spending could be exposed to this change by 2030, roughly 20% of enterprise SaaS spending. But Gartner also says current market expectations for AI agents exceed what they can reliably do today. I don’t think the interesting question is whether SaaS “dies.” It’s how the value of software changes. Less value may sit in simply giving people another dashboard to operate. More may sit in the data, workflows, integrations, knowledge, trust, and actual outcomes the product can deliver. The technology is significant. But as usual, the headline is moving faster than the reality.