Some big ideas.
A recent article from Harvard Business Review highlights the importance of designing effective workflows for agentic artificial intelligence systems, which are capable of executing tasks, making decisions, and coordinating across various departments. Companies that implement these systems must focus on outcomes and designate mission owners to direct both human and AI efforts to avoid the pitfalls experienced by others who failed to adapt their organizational structures. Historically, organizations have often merely adjusted existing processes or added new tools without making necessary structural changes. According to a past analysis, many high-profile digital transformations have stumbled, not due to a lack of innovation but because they did not reorganize effectively around new technologies. As agentic AI gains traction, it presents an even greater challenge, requiring a departure from siloed operations and outdated work methods to achieve successful implementation.
How about saying no to AI? A growing movement is emerging, consisting of individuals who actively resist the integration of artificial intelligence into daily life. These advocates raise concerns about the ethical implications and potential job displacement associated with the increasing reliance on AI technologies. Recent surveys indicate that nearly 40% of workers express apprehension regarding the impact of artificial intelligence on employment, according to a report from the Pew Research Center. Furthermore, experts warn that while AI can enhance efficiency, it could also exacerbate existing inequalities if not implemented thoughtfully. The article highlights the voices of those who prioritize human-centric approaches over automated solutions, advocating for a more cautious and considered adoption of technology in society.
Why do we care?
So Harvard Business Review’s talking about agentic AI — systems that don’t just give you answers, they take action. They make decisions, they run workflows, they coordinate across teams. That’s a big leap from “ask the chatbot.”
But here’s the key question: who owns the mission?
If AI starts doing real work, who’s accountable for the outcome? Somebody has to own that mission, human or not. In an MSP, that’s the same challenge — if your automation loop starts taking tickets, rebooting systems, or provisioning users, who’s responsible when it messes up? If the answer is “no one,” you’ve got a governance problem, not an AI one.
And here’s another: are you redesigning your process, or just adding another tool?
This is the trap so many organizations fall into. They buy something shiny, call it transformation, and keep running the same broken workflows. If you’re just layering AI on top of the same messy ticket queues, you’re not evolving — you’re just automating the mess.
Then there’s the movement saying, maybe we don’t want AI everywhere.
And you know what? That’s worth considering. Some workflows shouldn’t be automated — not because the tech can’t, but because it shouldn’t. Could saying “no” to AI actually be the right strategic move sometimes? That’s the mark of maturity — knowing where human empathy, trust, or creativity matters more than speed.
What about the people?
If nearly 40% of workers are worried about AI, you can’t ignore that. MSPs deploying AI need to think beyond the tech — how do you help clients handle change management? Training, transparency, governance — that’s where a lot of the value will be created.
And maybe the biggest one: what’s your differentiator when AI can execute the task?
If an agent can handle remediation or reporting automatically, your edge becomes interpretation — helping clients understand what the result means and how to act on it. That’s where you prove value.
So here’s the question I’ll leave hanging: is your business model ready for that?
Because agentic AI doesn’t care about your hourly rates. It’s going to push the entire service economy toward outcome-based delivery. If you’re still billing for tickets and time, you’re going to get left behind.
The MSP industry’s next stage isn’t about who has the best AI tool — it’s about who builds trust and accountability around it. As automation scales, leadership and governance become the new differentiators. The shakeout has already started — and the smart operators are designing for it.