@ramahluwalia@LumidaWealth may seem very minor, but it matters how you present your values.
a standard piece of copier paper taped to the conference room door is a choice.
A friend of mine works in natural resource conservation along Florida's beaches.
He told me about a project near Cape Canaveral where the Army Corps of Engineers built a dune to protect the launch pads. The Corps has exceptional engineers. But military engineers don't have a whole lot of taste.
They designed the dune exactly as an engineer would. Ramrod straight. Perfect 3:1 slope. Crisp edges, like a bunk bed. Compacted so hard that all the plants blew away in the first storm. He said you could land a plane on it.
Technically correct. Contextually wrong.
That is what happens when execution runs ahead of taste.
AI is the best execution instrument we have ever seen. It will build you the straightest dune possible. It has no idea whether it belongs there. Whether you can plant anything on it.
Taste is knowing the environment and designing to fit it. Not dictating it. It is a uniquely human trait. AI can mimic it. It cannot replicate it.
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A workflow audit is no longer the best way to figure out how to use AI in your job.
Despite the advice from AI labs, I'm more convinced, because of AI's reasoning capabilities and long context horizon, that the right starting point is goals + context connectors + interview.
The workflow audit is moving too many people into quick-win "yay I just did that one thing so much faster!" productivity land and missing the entire business transformation side.
For example...
Team does research. Person A reads six articles and hands them to Person B. Person B pulls it together into a report. The report goes to the client, who uses it to audit part of their own work.
If you run a workflow audit on that, the "AI upgrade" is both painfully obvious and dreadfully boring. You have AI do the research. You have AI draft the first pass. The human reviews.
Maybe a fancier version of that workflow builds a branded template, lets AI run the research itself, and a human adds a voice memo for context. Then you ship... the exact same report. But faster! Which feels great for about a week. And then you assume the next steps are making more frequent reports.
BUT that report only ever existed because it was the right asset in 2019 to help your client run their own audit, when your real constraint was time and people. That constraint is gone. So why are you still manufacturing the identical artifact at every step?
It just so happens that in 2019 the best way to complete that outcome for the client was a report. It just so happens that the best way to make the report was Person B compiling everyone's input. It just so happens that the best way to get input was reading articles and writing them up.
A lot of "it just so happens" from 2019 DO NOT hold in 2026. And based on my AI advising work, a workflow audit just doesn't surface them, because it optimizes the steps instead of questioning the destination.
Goals + context connectors + interview does.
Start from the goal (ex "help the client audit their work", not "produce a report"). Connect Codex or Claude Code into tools (especially notes, email, CRM, chat like Slack, meetings, docs/drive) so AI pulls from live sources and the client's own systems instead of a static handoff. Have AI interview the human to capture the nuance and judgment for why this process even exists.
The answer might look nothing like a report. Maybe it's a brand-new auditing product business line that you can launch. Maybe it's an interactive report that the end client can plug straight into. Maybe it's a more real-time research repository the end client can talk to all year instead of waiting for the big monthly drop. Maybe you realize that the client can make the same report in 2 seconds and the resources it takes to create and distribute it on your end just doesn't make sense to keep running in 2026.
For every asset you make, ask the very obvious question "why the hell do we do this thing?" Ask yourself what outcome or influence or action you are actually trying to solve for. Then ask the 2026 version. Is this still the most effective, efficient, valuable, creative, low friction, engaging, and long-term relationship-deepening way to get there?
A workflow audit gets you a faster horse.
Steal my prompt and replace your workflow audit - pasting it in the next tweet.
Vision is not the ability to spot an opportunity.
Anyone can spot an opportunity. That part is easy. The hard part is understanding how a problem turns into a realistic solution.
Real vision comes from knowing a problem deeply. Knowing what's already been tried. Not just that those attempts failed, but why they failed.
AI can generate options. A lot of them. Faster than you can read them.
What it cannot do is tell you which one is worth your time.
If vision doesn't come naturally to you, that's okay. It doesn’t come naturally to me.
It's not a fixed trait. It's a skill that develops through exposure, through failure, through paying close attention to the space you're working in.
The tools are no longer the constraint. Knowing what to build, and why it's the right thing to build, is the work now.
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This is effectively the #1 problem for AI agents in the enterprise.
As we go from agentic coding (where a large amount of context is in the code base, and users are technical enough to get the rest to the agent easily) to a world of knowledge work agents, the context problem becomes much more acute.
We see this every day with customers at Box. For existing digital knowledge, it’s often fragmented across legacy systems or environments that don’t play nice with agents, and have access controls that don’t map to the real work that needs to be done, which become a huge hurdle for getting agents the context they need. This has to all get moved to modern, secure cloud environments.
But also, companies often haven’t captured and digitized some of the critical context that agents need to work with. Decisions, processes, and workflows often live in people’s heads and tribal knowledge that need to get turned into unstructured data for agents.
This is actually one of the biggest points of leverage for applied AI companies, because they can work to specialize in getting agents exactly the information and domain expertise they need. But it’s also one of the reasons why FDEs and new system integrator plays will also work so well right now.
The companies that figure this out will be able to get the most out of AI going forward.
@gregorykennedy We think with the right data and system it can achieve 80/20
Social media is so ephemeral it’s hard to justify the intellectual effort on something so short.
Maybe we can do a demo once V1 is ready
I’d argue this:
You want to automate the distribution of your newsletter, not writing it.
Your voice is your unique fingerprint. It’s what makes you different from the AI slop.
For me:
I enjoy writing my newsletter.
I don’t enjoy writing the social media promoting it.
So we’re building an agent that uses my unique voice characteristics to write social media for me.
@AGloriaK is building it. Really amazing work.
@gregorykennedy Best thing you can do when you catch it doing those things is to ask it to introspect and tell you why it happened. Then tell it to change instructions to not do that again.
It 100% does both of those. You have to be paired enough to catch them.
The idea of fully handing off large scope items to an agent in a real enterprise environment is not reality.
Full handoff works better in a clean slate.
But AI is trained on human data. We lie, make mistakes and make stuff up. It will do the same.
A specific example:
Every day I have to check log files and the status of data
I can’t give AI access to the system
So I had it write an SOP telling me what it needed to do the analysis
It runs the analysis, branches based on what it sees, skips steps
And then writes the slack post to update my customers on the status
Turns a 90 minute task into 15 minutes
A specific example:
Every day I have to check log files and the status of data
I can’t give AI access to the system
So I had it write an SOP telling me what it needed to do the analysis
It runs the analysis, branches based on what it sees, skips steps
And then writes the slack post to update my customers on the status
Turns a 90 minute task into 15 minutes
@gregorykennedy There’s a difference between automation and AI.
Automation is deterministic
AI is probabilistic
Use AI to create the automation
https://t.co/BvM5ve99dO