By 2026, every board deck has that slide: AI, budget approved, pilot renamed “platform,” chart pointing confidently up.

Inside the actual teams? Different story.

Some people live in AI tools all day. Others dodge them. Most are quietly experimenting in a second tab, half-wondering if they’re using any of it the way they should.

If you’re a founder, angel, or early-stage VC, that gap between the slide and the workflow is where the real upside sits.

Here’s what we’re going to decode:

• What people actually want to read and learn about AI
  
• How AI tools really fit (or clash) with daily workflows
  
• What still feels hard, risky, or frustrating about using AI at work
  

What people actually want to read about AI in 2026

Ask people what kind of AI content they want more of and the answers fall into a few clear buckets.

1. Tips that change the workday, not just the narrative

When people say, “tips on how to work with AI,” they’re really asking:

• Show me how to prep a client call in 10 minutes
  
• Show me how to clear a backlog without burning out the team
  
• Show me how to draft docs, specs, or emails that don’t sound copy-pasted
  

The content that wins is grounded and concrete, not abstract:

• “How our CS team uses AI to handle renewals without losing the human touch”
  
• “The prompt set our sales team uses before every demo”
  

That’s exactly what buyers want in a product story too: not a feature tour, but a workflow they can steal.

2. Clear views on where AI is taking work

Some questions surrounded leaders:

• How will roles shift in the next 2–3 years?
  
• Which skills are gaining value, which ones are fading?
  
• How will AI reshape team structure, not just job titles

3. Proof from real teams, not theoretical decks

Stories from real teams land far better than abstract essays.

People want to see:

• The starting point: “We couldn’t keep up with tickets.”
  
• The first move: “We used AI to draft replies, not send them.”
  
• The result: “Time to resolution dropped, satisfaction held or improved.”
  

Have you ever thought about what leaders might want? We have prepared a short list that answers some questions: Does this hit our industry?Does this touch our data and compliance?Does this change our next product move?

What still feels hard about using AI at work

Even in 2026, a few pain points keep coming up on repeat.

1. Cleaning up AI mess
People ship half-baked AI output, and someone else has to fix it.

If review time is unpredictable, teams drift back to doing it all manually.

2. Little real backing from the company
No clear policy, vague “use AI more” messages, zero training. A couple of power users experiment, everyone else watches from the sidelines. Tools that bring playbooks and onboarding out of the box will win.

3. Quiet job anxiety
Support, ops, finance, people see roles changing and wonder if they’re next. Adoption works when leaders:

• Position AI as a skill booster, not a replacement plan
  
• Invest in upskilling
  
• Link AI use to career growth, not just cost cuts
  

4. Thin trust in the output
People worry about wrong facts, subtle errors, off-tone replies. Trust grows when tools:

• Show exactly where AI touched the work
  
• Make comparison and rollback easy
  
• Encourage checks on high-stakes steps
  

5. No clear playbook
Most don’t dislike AI; they just don’t know what “good” looks like for their role. That’s the gap to fill with sharp, role-based examples:

• A baseline setup for a product manager
  
• A safe pattern for a CS lead on tickets
  
• A realistic weekly rhythm for a founder using AI on real decisions
  

If you’re serious about AI at work, you’re not shipping demos, you’re changing how work actually happens. Build around real workflows so AI sits in the path from ticket to resolution or lead to deal, visible to the whole team with clear review and ownership. Treat learning as part of the product with role-based examples, and track outcomes like quality, sentiment, revenue, and risk, not just usage, because that’s what leaders actually care about.