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The AI Assistant I Use Daily (And What It’s Replaced)

I’m not interested in hype. I care about tools that save time, improve output, and help me lead better.

So when AI entered the picture, I didn’t dive in headfirst. I tested. I questioned. And now? I use it daily—not as a novelty, but as a real assistant.

Here’s what I use, what it’s replaced, and how it’s changed how I work.

1. Brainstorming and outlining
What I used to do: Stare at a blank page, jot disconnected ideas, reorganize them endlessly.
What I do now: Ask AI to generate outlines based on a topic I’m thinking through—blog posts, internal comms, training content.
Result: I start 10x faster. I still tweak and guide the structure, but I’m never starting cold.

2. First drafts of communication
What I used to do: Spend too much time rewriting emails or announcement drafts to strike the right tone.
What I do now: Feed a few bullet points to AI and ask for a clear, professional first draft.
Result: It cuts my writing time in half. I still personalize and trim—but the heavy lifting is done in seconds.

3. Meeting prep and research
What I used to do: Search LinkedIn, dig through old emails, skim websites for client or prospect info.
What I do now: Ask AI to summarize a company, recent news, or role-specific concerns for the person I’m meeting.
Result: I walk into meetings sharper—with context and talking points ready.

4. Naming and titling
What I used to do: Lose time picking a blog title or subject line.
What I do now: Ask AI for 10 options and pick one.
Result: Better titles. Faster decision-making.

5. Idea vetting
What I used to do: Bounce ideas off a colleague or let them sit for days while I thought them through.
What I do now: Use AI as a sounding board—asking “What are the downsides?” or “What would a skeptic say?”
Result: Faster clarity. Still human judgment—just faster.

What AI hasn’t replaced:

My judgment
Strategy
People skills
Trust
Leadership

AI doesn’t replace the hard stuff. But it helps me get to the hard stuff faster. And that’s the point.

What I’ve Learned About Trust from 30 Years of Consulting

In consulting, trust isn’t a buzzword—it’s the whole game.
You can have the best tech stack, the sharpest team, and the flashiest slide deck in the room… but if the client doesn’t trust you, none of it matters.

After over 30 years in the business, here’s what I’ve learned about how trust is built (and lost).

1. Trust is consistency over time
It’s not about one impressive meeting or a great kickoff call. It’s about showing up, following through, and doing what you said you’d do—over and over again.
Trust builds slowly and silently. Then, one broken promise can blow it up.

2. You earn it faster by telling the truth sooner
Bad news doesn’t get better with time. When something goes sideways—and it will—clients want honesty, not spin.
I’ve found that the faster we admit a misstep and share how we’re fixing it, the more credibility we build. It’s counterintuitive but true.

3. Being technically right isn’t always enough
You can win the argument and still lose the room.
Trust isn’t just intellectual—it’s emotional. Clients trust people who listen, who meet them where they are, and who understand their pressure (not just their project scope).

4. Trust is built between meetings, not just in them
It’s the quick update when nothing’s changed. The extra question that shows you’re thinking ahead. The quiet follow-up that signals, “We’ve got you.”
These moments don’t get logged in JIRA or tracked in a spreadsheet—but they’re noticed.

5. Trust is fragile—and portable
People remember how you made them feel. If you’ve built trust with a client, they’ll take you with them when they move companies. If you’ve burned it, same deal.
In this business, your reputation travels faster than you do.

The longer I’ve led teams and worked with clients, the more I’ve realized: we’re not just in the software business. We’re in the trust business.

And like anything worth building, it takes time, intention, and care.

If AI Is So Smart, Why Can’t It Run a Project?

AI can draft an email, summarize a meeting, write code, and even crank out a blog post like this one (with help). But there’s one thing it still can’t do:

Run a real project.

We’ve tried. We’ve experimented with AI for status reports, timelines, risk assessments, and backlog grooming. It’s impressive—fast, helpful, and often accurate. But project management? That’s still human territory.

Here’s why:

1. AI can’t read the room.
Deadlines shift. Priorities change. A stakeholder’s “no big deal” tone in an email actually means “I’m about to escalate this.”
AI doesn’t catch nuance. It doesn’t read body language, office politics, or tension over Teams calls. Project leaders do.

2. Projects don’t follow scripts.
Even the best Gantt chart goes sideways by week two. People get sick. Budgets get cut. A client pivots.
AI is great at pattern recognition—but projects are often the opposite: messy, emotional, and unpredictable. Leading through ambiguity takes real-time judgment, not pre-trained algorithms.

3. Relationships still matter. A lot.
When things go south (and they will), people want to talk to someone they trust—not a chatbot.
A seasoned project lead knows how to listen, adjust, empathize, and reset expectations without blowing up the timeline—or the relationship.

4. AI doesn’t know your business.
It knows businesses in general. It doesn’t know your unique challenges, team dynamics, or what happened last quarter that’s still lingering in the background.
Good project leadership isn’t just about tasks. It’s about context—and context still requires a human brain.

That said—AI is an amazing co-pilot.
It can flag risks faster. Draft communication. Generate insights from sprint notes.
But it’s not leading the call, navigating egos, or rescuing a deliverable gone off the rails. That’s you.

So no, AI can’t run a project.

But it can help you run one better.

Your Best Consultant Might Be the One Who’s Not Billing (Yet)

In consulting, there’s a saying:
“If they’re not billing, they’re costing.”

That’s technically true. But it misses something bigger.

At Intertech, we’ve come to realize that our most valuable consultants aren’t always the ones billing every hour. Sometimes, the people on the bench are driving the most important transformation in our business.

Right now, that transformation is AI.

Here’s how we’re using bench time strategically:

1. Training on AI tools and frameworks
Rather than rushing consultants into the next project, we give them structured time to learn tools like GitHub Copilot, ChatGPT, and AI-assisted test generation platforms. The result? They re-enter projects faster, more capable, and AI-enabled.

2. Standardizing AI in our dev process
We’re using bench cycles to build internal playbooks for applying AI—from proposal writing to automated documentation to code review. These aren’t “nice to haves.” They’re practical assets that increase velocity and quality across the board.

3. Prototyping with purpose
Benched consultants are experimenting with real-world use cases: generating scaffolding code, refactoring legacy modules, streamlining unit test creation, and integrating AI-driven analytics into apps. It’s hands-on R&D—without the overhead of a live project.

4. Supporting AI adoption across teams
Having AI-fluent consultants available helps us accelerate adoption across project teams. They’re building demos, advising PMs, and helping clients understand what’s possible. They’re our internal accelerators.

5. Staying ahead of client expectations
Clients are asking: “What’s your AI strategy?”
Our bench consultants are a big part of the answer. They’re not just staying billable—they’re making sure we stay relevant.

Bottom line?
A smart bench strategy isn’t just about cost control. It’s about innovation.
Done right, your non-billing consultants might be your most valuable team members—because they’re building the future you’ll soon be charging for.

The 3 Kinds of Clients We Say No To—And Why

When you’re starting a business, you say yes to everything. Every prospect, every project, every “maybe” that could lead to a win. I’ve done it. Most founders have.

But after years of consulting work, here’s the truth:
Some clients just aren’t a good fit—and saying “yes” to the wrong ones costs more than you think.

Today, we’re more intentional. Not because we’re arrogant. Because we’re focused.
Here are three types of clients we politely decline—and why it’s better for everyone when we do.

1. The “We Just Need Bodies” Client
What they say: “We just need a few developers to crank out code.”
Why we say no: We’re not a temp agency. If a client only wants hands on a keyboard with no strategy, collaboration, or architecture involved, we’re not adding the value we’re built for. We help solve problems, not just fill seats.

2. The “Everything’s on Fire” Client
What they say: “Can you take over this broken project… yesterday?”
Why we say no: Sometimes urgency is real. But other times, it’s the result of poor planning, shifting priorities, or internal dysfunction. If we’re stepping into chaos without clear leadership or direction, success becomes a moving target—and both sides lose.

3. The “Budget Mystery” Client
What they say: “We don’t really have a set budget. Just give us a ballpark.”
Why we say no: No budget = no clarity. Good partnerships require transparency from both sides. If we’re forced to guess what they can spend, we’re already misaligned. We value trust and candor, and that starts on day one.

Here’s what we do look for:

Clients who want true collaboration
A clear business challenge with measurable impact
Openness to our process—not just our people

Saying no isn’t easy. But saying yes for the wrong reasons? That’s how you drain your team, dilute your value, and damage your reputation.

We’ve learned it’s better to walk away early than to regret staying too long.