Beyond AI Basics: Transform Your Agency Operations with This Framework

https://www.youtube.com/embed/cX3OJmtzUQ8

"Instead of creating an expert agent altogether who’s going to do everything for me, I started creating these objective task agents… and that’s where I started seeing the success.” - Khushbu Doshi

Ever feel like everyone's talking about AI for agencies, but no one's showing exactly HOW to implement it effectively?

In this week's Deep Specialization™ Podcast, I interview Khushbu Doshi, COO at E2M Solutions, who shares the exact AI implementation framework they've used with 357+ agencies to create measurable productivity gains.

What you'll discover:

• The "objective task" method for breaking down complex processes into automatable components

• A smart client management system using ChatGPT projects that you can implement today

• How to measure AI ROI using "time to value" metrics

• The strategy for achieving 10% productivity increases every quarter through strategic AI adoption

Khushbu also shares a fascinating approach to overcoming team resistance to AI adoption through quarterly demos and competitions. It's completely changed how I think about change management for new tech.

If you're tired of generic AI advice and ready for actionable strategies that deliver real results, this episode is a must-listen.

🎧 Listen to the episode now:

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Best,

CQ

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Frequently Asked Questions

What is the "objective task" method for AI implementation?

Instead of building one expert agent meant to handle everything, it means breaking complex processes into smaller, automatable task agents. That narrower approach is where the real productivity gains started showing up.

How can I set up a smart client management system with AI?

Using ChatGPT projects is one approach shared in the episode, offering a way to organize client-related AI work that can be implemented right away without a complex buildout.

How do you actually measure whether AI adoption is working?

Using "time to value" metrics, which track how quickly AI implementation produces measurable results rather than just assuming productivity gains are happening.

What kind of productivity gains are realistic from strategic AI adoption?

Around 10% increases each quarter, according to the framework discussed, achieved through consistent, strategic adoption rather than one-off tool experiments.

How do you get a team on board with AI instead of resistant to it?

Quarterly demos and competitions were the approach used to overcome team resistance, turning adoption into something the team engages with directly rather than a mandate handed down.