I recently attended Microsoft Accelerate, an invitation-only partner conference designed to deepen and evolve Microsoft’s enterprise partnerships. The sessions were not just about go-to-market alignment or roadmap updates, though there was plenty of that. They were also intentionally focused on thought leadership and professional development, helping partners think differently about how AI is reshaping work, leadership, and collaboration.
I walked away with more than tactical takeaways. The conference genuinely changed how I think about AI, both personally and professionally.
From Analog Roots to AI Reality
I’m Gen X, which means I grew up in a world that was deeply analog. Technology evolved gradually. You learned a tool, you mastered it, and then you used it for a long time. Critical thinking, effort, and original work were not optional. They were expected.
So when AI first started showing up in my work, there was a quiet, nagging feeling: Is this cheating?
What I heard at Accelerate put that concern to rest. Across sessions, there was a strong, consistent message: AI makes the quality of thinking more visible. Clear thinking gets amplified. Shallow thinking gets exposed.
That reframing mattered.
What’s Changed About How We’re Using AI
One thing was very clear at Accelerate: the conversation about AI has shifted.
Early AI adoption was understandably naive. We treated AI like past productivity tools. Roll it out, train people, and move on. That approach worked for earlier technology waves.
AI behaves differently.
It influences how people think, decide, collaborate, and prioritize. It changes the experience of work itself. And when work changes, emotions come along for the ride. Adoption can feel uneven, sometimes messy, occasionally uncomfortable.
That’s not a sign something is broken. It’s what meaningful change involving humans actually looks like.
The Shift That Actually Matters: Human-Led, Agent-Operated Work
One concept that really stuck with me was human-led, agent-operated work.
Humans still lead. We bring judgment, creativity, relationships, ethics, and context. AI supports us by accelerating parts of work that benefit from structure and speed, such as drafting, summarizing, researching, or pattern analysis.
The goal is to reduce the amount of human energy spent on work machines are better suited to handle, so people can focus on work that genuinely requires human judgment.
Put simply, AI helps make the time we already have more useful.
The Moment That Really Clicked for Me
One of my biggest “aha” moments at Accelerate came from a session led by Tim Creasey from Prosci, a leading organization focused on change management. The session stood out because it was grounded and practical, focused on how work actually gets done.
He shared a simple way to think about AI integration by grouping work into three categories:
My Work: work that should remain human, including emotionally complex decisions, ethical judgment, improvisation, relationships, and values
"With Me" Work: work where AI collaborates with us to improve productivity, quality, and speed
"For Me" Work: routine, repeatable, or draining tasks AI can handle on our behalf
What resonated most is that this model keeps responsibility where it belongs, with the human.
AI assumes intent and clarity. It does not create them.
If I’m not thoughtful or intentional going in, AI will reflect that right back to me, quickly and confidently.
Garbage In, Garbage Out (a.k.a. AI Slop)
This leads to something many of us are now seeing: AI slop.
AI slop shows up as polished but empty output. Things like:
A strategy document full of buzzwords but no actual point of view
Marketing copy that sounds impressive but could describe almost any product
“Insights” that restate the obvious with more confidence than substance
Journalists and researchers have started calling this phenomenon AI slop, describing it as low-quality, mass-generated content that prioritizes speed over substance. Scientific American explores this in depth in “AI Slop: How Every Media Revolution Breeds Rubbish and Art”, which looks at how new technologies often produce volume faster than judgment can keep up.
None of this is an AI failure. It’s an input failure.
When prompts are vague, goals are unclear, or thinking hasn’t been done up front, AI fills the space with generic content. Fast. Fluent. And ultimately useless.
When I do the thinking first, when I’m clear on what I believe, what I’m trying to say, and why it matters, AI becomes genuinely powerful. It helps me pressure-test ideas, explore angles I might not have considered, and move faster without sacrificing quality.
That’s not dumbing ourselves down. That’s using a tool responsibly.
Why Frontier Companies Are Pulling Ahead
Microsoft also spent time talking about Frontier Companies, organizations that are pulling ahead as AI becomes embedded into everyday work.
Microsoft’s Work Trend Index identifies Frontier Firms as organizations that embed intelligence on demand and hybrid human-AI teams directly into their operating models. These companies rethink workflows, decision-making, and accountability to intentionally combine human judgment with AI execution. Microsoft outlines this shift in detail in “The Year the Frontier Firm Is Born."
What stood out to me is that Frontier Companies are not defined by how experimental they are. They are defined by how intentional they are.
They make deliberate choices early:
AI strategy is business-led
Data is treated as a foundation
Governance enables scale rather than slowing it down
Operating models evolve alongside the technology
They don’t layer AI onto broken workflows. They redesign workflows with AI in mind from the start.
The Discipline AI Still Requires
If I have a concern about where AI goes next, it’s not speed. It’s complacency.
One speaker framed it well: think of AI as an intern, not an oracle.
AI is excellent at acceleration and iteration. It struggles with judgment, accountability, and context. Treating it as an authority or expecting it to fix broken processes creates disappointment fast.
Organizations that are making progress are explicit about:
What work stays human-led
What work is done with AI support
What work can be automated on behalf of humans
That clarity builds trust, reduces fear, and makes adoption stick.
How I’m Using AI Going Forward
Rather than restating the theory, here’s how this is changing my own approach.
I’m using AI:
As a thinking partner, not a content generator
To pressure-test ideas before sharing them broadly
To explore adjacent data or perspectives I wouldn’t naturally reach
To speed up iteration once I’ve done the upfront thinking
I’m also being more disciplined about when not to use AI, especially for work that requires judgment, nuance, or accountability.
And I’m paying much closer attention to inputs, because quality thinking going in still determines quality output coming out.
That’s the real opportunity I took away from Microsoft Accelerate.
AI advantage doesn’t come from tools alone. It comes from how intentionally we design the way work gets done, with humans firmly in the loop.
Not flashy; not hype-driven - but far more durable.
