Podcast Summary
Episode Overview
Episode 93: Using Generative AI to Develop a Winning Strategy for Business Leaders with Justin Trombold
Host: Chris Daigle
Guest: Justin Trombold, Founder & President, Antisen Advisors
Release Date: March 2, 2026
This episode provides a deep dive into how business leaders can practically adopt and leverage generative AI to drive real transformation in their organizations. Chris Daigle speaks with Justin Trombold, an experienced AI strategy consultant, about what companies get right and wrong in AI implementation, the critical distinction between IT and business transformation, and actionable frameworks for piloting and scaling AI initiatives. The discussion is packed with real-world examples, diagnostic approaches, and candid advice for executives looking to move beyond hype to measurable outcomes.
Key Discussion Points & Insights
1. The Right Frame for AI Projects
- Generative AI is not just an IT project, but a transformation project.
- Treating AI solely as an IT initiative is a common mistake; instead, it needs to be viewed as a deep operational and cultural transformation.
- Quote: “There’s a train of thought that treats it more like an IT project when it’s actually more of a transformation project.” – Justin (00:12)
2. Centralizing vs. Decentralizing AI Decisions
- Centralize tool selection (IT), decentralize use case deployment (business units).
- IT should select and secure tools (“the toys in the sandbox”), but actual deployment and use case decision-making must reside with business unit leaders who feel the real pain points.
- Quote: “Centralizing the decision making from a tool perspective, but decentralizing the decision making and deployment from a use case perspective.” – Justin (00:23, 12:30)
3. Avoiding Bottleneck Shifts
- AI often just moves bottlenecks down the workflow rather than eliminating them.
- Without considering impact on downstream/upstream processes, AI improvements can “flood” areas of the business unprepared for new throughput.
- Quote: “What usually happens is it improves some aspect of process Y, and so maybe that’s accelerated…but the process itself just gets stopped.” – Justin (05:16)
- Example discussed at 19:49: AI accelerates lead qualification, but if sales incentives/processes aren’t adjusted, qualified lead surplus is not converted into revenue.
4. Who Owns AI in the Business?
- Business unit leaders, not just IT, should own the decision of how tools are used.
- IT’s job: set boundaries, security, vendor relationships.
- Business leaders’ job: identify, pilot, and refine use cases in real workflows.
- Quote: “The people that you really need to have engaged…for them to truly have ownership over how it’s being used, where it’s being used.” – Justin (09:37)
5. Diagnostics & Readiness Assessment
- Start with a diagnostic to measure generative AI readiness along five pillars:
- Alignment with strategic vision
- End user proficiency
- Cross-functional collaboration
- Scalability and adaptability
- Governance/compliance
- Quote: “We do a simple one that goes through five pillars…It’s a nice place for anyone to start.” – Justin (28:07)
6. Start Small: The Power of Micro-Experiments
- Pilot with small teams or individuals, focused on one workflow and a few key KPIs (not just ROI).
- Use a simple charter: define experiment, KPIs, owner, and success criteria, then run 7–30 day pilots.
- Quote: “Pick one workflow, pick one part…define one KPI that isn’t ROI. Do some exploration…then see if that KPI is sufficient to understand if it’s working.” – Justin (31:41)
7. Don’t Get Stuck on LLM Brand-Wars
- For most, which LLM you have doesn’t matter—just start with what is accessible and scalable.
- Power users adapt to any LLM after spending a few hours with it.
- Quote: “Once you spend a half day with any of these models, you almost forget the model that you’re working in.” – Justin (13:13)
8. Cross-Functional Collaboration is Critical
- Siloed organizations risk friction and failure as AI solutions cross boundaries.
- Operating model and culture around collaboration often determine success more than technical ability.
- Quote: “The ability for organizations to collaborate cross functionally and not work in silos is a big driver” – Justin (24:58)
9. Fostering a Culture of Curiosity & Exploration
- AI adoption thrives where there’s encouragement to experiment—and psychological safety to fail.
- Example: Client who encouraged 20% of time be spent applying generative AI to existing projects.
- Quote: “Have an exploratory, have a curious, have an excited mindset about doing this. Because if they don’t…you just aren’t going to get the same level of buy-in.” – Justin (28:07)
10. Training That Embeds AI in Real Work
- True “AI thinking” comes from hands-on, embedded experience, not generic training modules.
- Assess if staff know to ask LLMs about their current workflow, not just execute canned prompts.
- Quote: “They have to get their hands dirty. They have to be fully immersed in using AI in their own work.” – Justin (00:36, 41:09)
Notable Quotes & Memorable Moments
“There’s a train of thought that treats it more like an IT project when it’s actually more of a transformation project.”
– Justin Trombold (00:12)
“Centralizing the decision-making from a tool perspective, but decentralizing the decision-making and deployment from a use case perspective.”
– Justin Trombold (00:23, 12:30)
“Often you get a different type of burnout from doing generative AI over and over and over again on the same thing.”
– Justin Trombold (00:00, 35:42)
“What usually happens is it [AI] improves some aspect of process Y… but the process itself just gets stopped.”
– Justin Trombold (05:16)
“If you make it an IT first priority… you start to get a fundamental conflict.”
– Justin Trombold (08:56)
“Have an exploratory, have a curious, have an excited mindset about doing this.”
– Justin Trombold (28:07)
“If you feel like you can problem solve in an LLM, then you’re kind of where you need to be.”
– Justin Trombold (41:36)
“Pick one workflow, pick one part… define one KPI that isn’t ROI. Do some exploration and then see if that KPI is sufficient to understand if it’s working.”
– Justin Trombold (31:41)
“Once you spend a half day with any of these models, you almost forget the model that you’re working in.”
– Justin Trombold (13:13)
Chris Daigle on early adoption pain:
“You kind of get started with those things, but you expose the company to risk because there’s no use policy in place… the training came from somebody that watched a TikTok video and is now doing the thing in their role.” (26:53)
Timeline & Timestamps
| Segment | Topic | Timestamp (MM:SS) | |---------|-------|------------------| | 00:00 | Burnout from repetitive LLM work | 00:00–00:12 | | 00:12 | Mistaking AI for an IT project vs. transformation | 00:12–00:19 | | 00:23 | Centralizing tools, decentralizing use cases | 00:23–00:36 | | 02:43 | Justin’s career background and consulting experience | 02:43–05:03 | | 05:16 | Common AI mistakes: shifting bottlenecks | 05:16–07:38 | | 08:56 | The IT vs. business leadership challenge | 08:56–12:30 | | 13:13 | LLM brand doesn’t matter for most users | 13:13–15:43 | | 16:57 | Structuring AI conversations, importance of strategy | 16:57–19:49 | | 19:49 | Example: Lead qualification and sales process bottleneck | 19:49–21:12 | | 24:58 | Silos and cross-functional collaboration | 24:58–26:14 | | 28:07 | Fostering curiosity, micro-experiments, and KPIs | 28:07–31:41 | | 31:41 | Experiment charters and success criteria | 31:41–34:49 | | 41:09 | Critical hands-on, embedded AI training | 41:09–43:37 |
Actionable Takeaways for Business Leaders
- Don’t frame AI as just an IT upgrade; treat it as business transformation.
- Let IT select tools for compliance/security, but empower business units to test and own use cases.
- Expect process bottlenecks to shift; address the full workflow, including incentive structures, not just the part you’re automating.
- Begin with diagnostics and a readiness survey—know your strengths and blind spots.
- Pilot in small, focused experiments, setting clear KPIs and decision criteria upfront.
- Avoid getting hung up on which AI model or tool vendor is best—execution and adoption matter most.
- Encourage curiosity and exploration—allocate time specifically for staff to work with generative AI in their real roles.
- Provide AI training by embedding it into real work, not as a side activity.
- Measure success with more than ROI—track KPIs that reflect true process improvement.
- Be prepared for resistance in cross-unit initiatives, and make culture/collaboration a priority.
Resources and Further Reading
- Justin Trombold’s white paper and readiness diagnostic:
Available on Antisen Advisors’ website (see show notes for links) - Mini diagnostic survey:
Also available via their website for executives/teams to self-assess
This summary was created to capture the heart of the conversation, key ideas, and memorable moments in the language and intent of the podcast hosts and guest, making it actionable and digestible for business executives at any stage of AI adoption.