Oklahoma City AI Leadership: Build Capability Without Overload
- JR
- Jan 22
- 6 min read
Oklahoma City, Oklahoma, January 22, 2026 — Artificial Intelligence should make leadership simpler, not louder.
If AI creates more activity but not better decisions, it isn't AI Strategy. It's expensive busywork. But when AI becomes repeatable internal capability, it creates competitive advantage: faster response cycles, clearer customer insights, stronger customer engagement, and customer experience that feels consistent and personal, not automated.
That conviction was tested on Thursday, January 22, 2026 with a CEO advisory groups session in Oklahoma City.
The ratings landed strong:
Quality of Content: 5
Quality of Delivery: 4.83
Applicability: 4.67
Would Recommend: 83%
The written feedback gave us something more valuable than scores: a clear picture of what leaders want next, and what needs to change to serve them better.
What Leaders Said, In Plain Terms
Several comments were immediate:
"Awesome content."
"The timing was perfect and actionable."
Others highlighted practical value:
"Stormie is a bona fide expert on AI and shared many valuable nuggets with us during his presentation. I appreciated the breadth of content… and in many different forms. Stormie also responded helpfully and professionally to questions that came up during the presentation."
One comment captured real tension that shows up in every AI workshop:
"Great presentation. Info overload. Awesome… just a lot for the allotted time. Need to focus on key topics and offer some micro sessions that focus on specific customer needs…"
That feedback matters. When leaders say "micro sessions," they're not asking for less value. They're asking for value delivered in a way that matches how their week actually works.
There was also critical feedback about session length, excessive self-promotion, and follow-up that felt like solicitation rather than invitation.
That's not small. Customer experience starts before someone becomes a customer. How you teach, present, and follow up is part of your customer experience. It either builds trust or creates friction.
Honest takeaway: the content is landing, but delivery format must respect time, attention, and consent more tightly.
The Survey Signals Behind the Feedback
Thirteen attendees completed the readiness assessment. Results explain why the room had both excitement and "info overload": most leaders operate with high urgency, uneven internal capability, and strong desire for structure.
Who Was in the Room
Role mix leaned heavily toward owners:
CEO/Owner: 10 of 13 (76.9%)
VP/Director: 1 of 13 (7.7%)
Other: 2 of 13 (15.4%)
Company size skewed small to mid-size:
1–50 employees: 8 of 13 (61.5%)
51–250 employees: 4 of 13 (30.8%)
251–1,000 employees: 1 of 13 (7.7%)
That context matters. Smaller teams move quickly but feel "info overload" faster because fewer people absorb and implement ideas.
AI Leadership Is Still Unclear
Who is the single person responsible for driving AI and automation outcomes:
No clear owner: 6 of 13 (46.2%)
CEO/GM (named, accountable): 3 of 13 (23.1%)
Functional leader (Sales/Ops/IT): 2 of 13 (15.4%)
Working group, no single owner: 2 of 13 (15.4%)
That's the first bottleneck. If ownership is unclear, AI initiatives don't become systems. They become experiments.
Speed Is the Advantage Most Teams Don't Have
When a key performance number drops 15%, how quickly can teams make production changes:
Within a month/quarterly: 7 of 13 (53.8%)
Within a week: 3 of 13 (23.1%)
Same day: 2 of 13 (15.4%)
Rarely, or crisis mode only: 1 of 13 (7.7%)
More than half operate in month-or-quarter cycles. That's where competitive advantage gets lost. Markets move weekly. Customer expectations change daily. Slow response cycles show up in customer experience.
Data Is Closer Than You Think, But Not Organized
If they wanted to run a 30-day AI pilot this month, what data is ready:
Scattered or siloed exports only: 5 of 13 (38.5%)
Clean, labeled dataset with access controls: 4 of 13 (30.8%)
Raw data we could label if needed: 2 of 13 (15.4%)
Nothing accessible yet: 2 of 13 (15.4%)
Good news and warning. Many organizations have enough to start, but "start" and "scale" are different. If you want AI to be repeatable, the data pathway must be repeatable too.
Safety Practices Are Uneven
Current rules for using AI safely:
No protections in place yet: 7 of 13 (53.8%)
Sensitive data blocked, activity logged/reviewed: 4 of 13 (30.8%)
Informal habits: 1 of 13 (7.7%)
Rules exist, partly enforced: 1 of 13 (7.7%)
This shows a split: some organizations build guardrails, others operate without protections. If you care about customer experience, you cannot afford inconsistent safety practices.
Most Teams Struggle Moving from Pilot to Production
AI or automation pilots that made it to production in 12 months:
0 (pilots only): 6 of 13 (46.2%)
1–2: 5 of 13 (38.5%)
3 or more: 2 of 13 (15.4%)
Nearly half remain in pilot mode. That explains the "give us micro sessions" request: leaders aren't just learning. They're trying to implement while running businesses.
KPIs Are Missing
Current measurement approach:
No KPIs tied to AI yet: 9 of 13 (69.2%)
At least one use case has KPI, owner, review cadence: 3 of 13 (23.1%)
Track results occasionally, no one owns KPI: 1 of 13 (7.7%)
If AI isn't measured, it stays inspirational. If it's measured, it becomes part of how business runs.
What Leaders Want Most: Better Customer Outcomes
Number one outcome leaders want from AI:
Customer experience: 7 of 13 (53.8%)
Revenue growth: 5 of 13 (38.5%)
Cost reduction: 1 of 13 (7.7%)
Most telling result. In this room, AI Strategy isn't primarily about cutting costs. It's about delivering better customer experience and strengthening customer engagement that supports measurable ROI.
The Biggest Blocker Is Capability, Not Tools
Number one blocker to AI adoption:
Talent/skills: 8 of 13 (61.5%)
Tech stack/tools: 2 of 13 (15.4%)
Leadership buy-in: 2 of 13 (15.4%)
Data quality: 1 of 13 (7.7%)
The room wasn't asking for "more AI." It was asking for clarity and competence: internal ability to use AI without breaking trust, overwhelming teams, or chasing shiny objects.
Leaders want that capability built: Do you want to learn more about developing an internal AI leader:
Yes: 12 of 13 (92.3%)
No: 1 of 13 (7.7%)
Confidence Is Cautious, Not Naive
Confidence that company will be competitive in AI by 2027 (1-10 scale):
Average: 5.8 out of 10
8–10: 3 of 13 (23.1%)
5–7: 7 of 13 (53.8%)
1–4: 3 of 13 (23.1%)
That's not pessimism. That's realism. And realism is where modernization without chaos becomes sustainable.
How to Avoid "Info Overload" and Still Build Advantage
If you felt the pull between "this is amazing" and "this is a lot," here's how to move forward without drowning your team.
Think of AI Strategy as a three-layer operating system. You don't install everything at once. You install layers in the right order.
Layer 1: Customer Clarity (Before Automation)
If you want AI to work, you need dependable customer insights. That starts with two decisions:
Persona: who are we serving right now?
Positioning: what do we want to be known for, and what experience are we committed to delivering?
Simple persona clarity checklist:
What are they trying to achieve this quarter?
What do they fear will go wrong?
What slows them down right now?
What does "great service" look like to them?
What makes them hesitate before buying?
Layer 2: One Workflow That Improves Customer Experience
Pick one customer-facing workflow where speed and consistency improve customer engagement immediately.
Good first choices for small teams:
Lead follow-up and appointment setting
Proposal drafting and follow-up
Customer support response drafting with human review
Onboarding emails and check-ins
Content repurposing that stays aligned to positioning
Layer 3: Guardrails, Measurement, and 30-Day Pilot
Before scaling, put basic protections in place and measure one KPI.
Minimum guardrails:
What data can never go into AI tools
When human review is required
Where prompts and approved outputs are stored
Who owns the workflow and approves changes
Pick one KPI for 30 days:
Response time
Lead-to-meeting conversion rate
Proposal turnaround time
Ticket resolution time
Hours saved without quality loss
Customer satisfaction signals
This is how you build capability without overload. One persona. One workflow. One KPI. Thirty days.
How We're Using Feedback to Improve Experience
Oklahoma City's feedback wasn't just about content. It was about experience design. Leaders are telling us what great looks like:
Tighten setup and get to core faster
Focus on smaller number of high-impact tools
Break content into micro sessions by specific customer needs
Reduce self-promotion inside training environment
Keep follow-up permission-based, not assumed
That last point matters. Customer engagement should feel invited, not pushed. If someone wants to go deeper, the path should be clear. If they don't, the relationship should still feel respectful.
Where GPS Summit Fits
A workshop creates clarity in a single day. The next challenge is implementation: building internal AI leadership, connecting workflows, and turning customer insights into repeatable execution.
That's what GPS Summit is designed to support, especially for companies that want to move from pilots to production while protecting customer experience.
GPS Summit transforms high-potential employees into AI Systems Generalists in three days—faster than MIT's 8-week program, more practical than Stanford's $18K certificate, and designed for companies that need internal capability, not consultant dependence.
Your designated leader learns to identify bottlenecks, build solutions, and drive measurable efficiency gains within 90 days. Not theory. Structural capability.
Ready to develop your internal AI leader? Enroll your high-potential employee in GPS Summit
A Closing Question
If you had to choose one customer-facing workflow to improve in the next 30 days using AI, what would you pick so customers feel the upgrade in customer experience and your team sees measurable ROI without overload?
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