🦾How You Can Leverage:

What’s that….you’re gonna run a 12-month AI pilot to determine ROI on a set of tasks that you’ve never created a human baseline for? 

Spoiler alert: you’ve failed before you started. 

By month 3 you're evaluating Stone Age AI while already missing features your competitors are using to leapfrog you. 

Companies are burning budgets testing yesterday's capabilities while their competitors moved workflows into front-end AI platforms that function as full operating systems now.

Not chatbots. Operating systems.

ChatGPT, Claude, and Gemini aren't simple AI chatbots anymore y’all. 

Suddenly, front-end AI chatbots agnatically research, write and render code and connect dynamically to your business data. 

Moving your daily business processes inside a front-end LLM is the same inflection point companies faced choosing Windows versus Mac versus Linux in the 90s.

Except nobody's treating it that way.

So, we’re showing you why and how on today’s show. 

Let’s dive in. 

You open ChatGPT and see 12 model options staring back at you.

Which one do you pick?

Wrong question.

Here's the right one: why did CEO Sam Altman reveal only 7% of paid users actually use thinking models? Because 93% of people paying premium prices are actively choosing inferior capabilities for their work.

That's not a user problem.

That's your training crisis.

ChatGPT isn't a chatbot anymore. Neither is Claude or Gemini. They're operating systems combining models with modes that create entirely new ways of working.

Canvas for iterative editing. Deep research for autonomous investigation. Connectors creating mini RAG pipelines with your live company data. OpenAI just announced apps this week at dev day, meaning entire external user interfaces now function inside ChatGPT itself.

Your team isn't prompting a model.

They're orchestrating an entire ecosystem that updates constantly without warning. When GPT-5 launched it somehow made everything more complicated rather than simpler. More model choices, more modes, more features buried without documentation.

Zero training for the people expected to extract value from this chaos.

Try This: Ask three random employees which ChatGPT model they use for their common tasks and why. You'll discover most people default to whatever the interface selected initially, never exploring options that might work 10x better for their actual work.

Here's the conversation that happens at every company:

"This project takes 8 hours with AI now."

"Great! What did it take before?"

"Uh……. khakis?"

No human baseline equals no measurable ROI on GenAI. Full stop sonny.

Just expensive guessing dressed up as AI innovation.

But it gets worse. Generative AI generates different outputs every single run, which means your one spectacular demo might be pure luck rather than reliable capability. Companies celebrate single successful results without testing whether quality stays consistent.

One good result proves nothing about reliability.

Then there's the change management failure. People get ChatGPT Teams access, watch maybe one onboarding video, get told to be productive. Zero systematic training on which models suit which tasks. Zero understanding of when to use thinking mode versus instant responses.

And the shiny object syndrome never ends. Google ships something, your team pivots. OpenAI responds, priorities shift. Anthropic launches a feature, direction changes again.

Implementation never actually finishes.

Meanwhile your competitors finished their two-week evaluation sprint three months ago and are already capturing compound advantages while you're still "exploring use cases."

Try This: Pick your most common AI task right now and time three employees completing it manually with zero AI. Track every step including research, drafts, and revisions. Calculate fully loaded hourly costs. This becomes your only honest comparison when leadership asks whether your AI spend actually pays for itself.

Stop overthinking this. Two to four weeks maximum per workflow evaluation.

Here’s our 7-step plan for evaluating models to get an ROI on GenAI.

Step 1: Define Success Before Testing Write exactly what makes this test pass or fail before anyone touches AI. No vague goals. Explicit outcomes, constraints, allowed tools, forbidden shortcuts.

Step 2: Measure Your Human Baseline First Multiple employees, identical workflow, no AI, everything timed and documented. Errors counted. Revisions tracked. This data becomes sacred.

Step 3: Build Realistic Test Datasets Twenty to forty actual work examples with real messiness. Renamed files, dead links, deliberate traps. Both humans and AI navigate identical chaos.

Step 4: Configure Production Environments Exactly Use the exact models, modes, and permissions your team will actually have. Not free versions. Not idealized setups. The real thing or your results mean nothing.

Step 5: Run Everything Three Times Minimum Separate chats, memory disabled, different days. One good result proves nothing. Require working citations and proof for every accepted answer. Calculate reliability scores.

Step 6: Calculate Real ROI With Blind Grading Graders can't know what came from humans versus AI. Convert time savings to actual dollars using fully loaded rates. Subtract subscription costs. Report net ROI alongside cost, latency, accuracy, stability, safety, integration, compliance.

Step 7: Retest Monthly Because Everything Changes GPT-5 Thinking updated multiple times since August. Canvas mode refreshes constantly. GPT-5 Auto shipped changes this week. Track trends against three-month averages. Investigate immediately when accuracy or savings drop.

Start with public benchmarks like LM Arena, LiveBench, Epoch AI, and Scale's LLM leaderboard to narrow which platforms deserve your time. Then run this framework on your simplest workflow first.

One workflow. Two to four weeks. Frozen model choice ignoring every shiny announcement during testing.

Try This: Choose your simplest AI use case today and commit to this two-week sprint. Document everything obsessively because this first rigorous evaluation becomes your template for every other workflow where AI might create measurable value instead of expensive theater.

Anotha one?

Yup.

Go repost today’s LinkedIn livestream and we’ll send you this (freakishly detailed) guide on evaluating front-end LLMs for ROI.

Pretty sure you could prolly resell this thing and people would pay TBH.

Go repost today’s LinkedIn livestream and we’ll DM you access!