“I tried AI, and it made more work. It’s easier to do it myself.”
We’ve heard this from dozens of smart, capable professionals. Different sectors, different challenges, same frustration. They finally carved out time to try it – typed in their question – and got back something so generic, so obviously robotic, that they closed the tab and went back to doing it themselves.
And yet other professionals sing AI’s praises. On our team, AI has helped us reduce the time spent creating meeting agendas by hours each month.
So what’s different? Is it what you’re using AI for or how you’re using AI? We think it’s the latter.
Situational Leadership for AI
In our popular Inclusive Manager Program, we expose new managers to the Situational Leadership model developed by Paul Hersey and Ken Blanchard. This is a model where managers assess a direct report on two factors: motivation and confidence. They then adopt their management style based on whether direct reports are high or low on each. Because a direct report who has a lot of motivation but low competence (such as a new college graduate) needs a different manager than someone who is highly competent and highly motivated (such as a seasoned individual contributor who just needs you to get out of their way).
For many of us, the highly motivated, highly competent employee is the dream. We give them little direction, and they get things done. They are efficient, and they make our jobs easier.
When it comes to AI, we’ve been told it’s efficient and will make our jobs easier. So we assume we can treat it like a highly motivated and highly competent co-worker. Unfortunately, that’s not the case. Artificial intelligence is highly motivated but actually low on competence when it comes to delivering the work you need.
Thus, the professionals who work best with AI are the ones who can switch to a management style more applicable to high motivation-low competence employees. And according to Situational Leadership, those employees need lots of direction.
How to Direct AI
When providing a new employee with direction and instruction, context makes a large difference. Without context, “updating the database” may be alphabetizing entries when the flagged addresses need attention before next week’s mailing. With context – what needs updating, why it matters, what good looks like – the same task gets done right the first time.
You’re managing AI more than you’re co-working with it. And when you’re conscious about providing context, the output of your tool goes from “meh” to “this actually works”.
The Situation Context
AI needs to know: Who, what, when, where, and why.
Before: “Write a project update email.”
After adding Situation Context: “Write a project update email to the executive team about our CRM migration. We’re 3 weeks behind schedule because the vendor changed API requirements mid-project. The CFO is nervous about budget overruns. The CEO doesn’t understand technical details but cares about customer impact. I need to be honest about delays while maintaining confidence that we’ll deliver.”
Notice what changed – not more “professional,” just more specific. You’re explaining the situation like you would to a trusted colleague who just joined the team.
The Outcome Context
AI needs to know: What good looks like, what to avoid, and who this serves.
Before: “Help me prepare for a difficult conversation with an underperforming team member.”
After adding Outcome Context: “Help me prepare for a difficult conversation with an underperforming team member. This person is talented but consistently misses deadlines, which is impacting team morale. I want to be direct about the problem without being cruel. I need them to leave the conversation with clear expectations and a sense that I’m invested in their success, not looking to manage them out. I tend to over-soften feedback, so push me toward clarity.”
This is powerful because you’re saying what success looks like AND what failure patterns to avoid. You’re coaching AI like you’d coach a direct report.
The Style/Constraint Context
AI needs to know: Voice, format, boundaries, preferences.
Before: “Draft a LinkedIn post about our company values”
After adding Style Context: “Draft a LinkedIn post about our company values. Our brand voice is conversational but not cutesy – think ‘smart friend’, not ‘ robot’ or ‘trying too hard to be relatable.’ Around 150 words. Lead with a specific story or observation, not an abstract statement about values. Avoid buzzwords like ‘synergy,’ ‘innovative,’ or ‘passionate.’ Our audience is skeptical of empty language.”
This is where your taste and judgment come in. You’re not asking AI to decide what sounds good: you’re telling it what good sounds like for your context.
The Learning Curve Paradox
When it comes to AI (and many strategic endeavors), the upfront work feels heavier, but it makes you faster overall.
We know – you don’t have time to write paragraphs to AI when you could just do the thing yourself. But here’s what we’ve noticed working with leaders:
- First time using a detailed prompt: Takes 10 minutes to write, saves 30 minutes of editing garbage output
- Fifth time using that same prompt structure: Takes 2 minutes to customize, saves 45 minutes of work
- After a month, you’re thinking in “prompt-ready” ways automatically
AI is a tool you can shape to serve your unique business needs and context. And by learning to provide it with the information it needs to succeed, it’s possible you’ll also become a better manager to humans.
To learn more about how to be a better manager and get more mundane tasks off your plate with AI, we recommend you download our Conscious AI Guide. It addresses some of the major concerns about AI (environmental, data privacy, bias) and gives you practical tips and tools for adopting AI ethically and effectively.
Like any effective manager knows, over-explaining is annoying at first, but saves you time in the long run. Now apply that thinking to AI, and you’ve got a powerful tool for making your job–and your life–easier.
This was written by Minal Bopaiah and Sage Eubanks with the help of claude.ai. As a small business, we thoughtfully engage artificial intelligence to improve our efficiency, while also using human experts and reviewers to ensure quality and mitigate bias. Graphic by: Black Illustrations