Oklahoma City's AI Turning Point: From Concrete to Capable
- JR
- Jan 21
- 7 min read
Updated: 2 days ago
Oklahoma City, Oklahoma, January 21, 2026 — Artificial Intelligence should make work easier, not noisier.
That conviction drives every session we deliver. If AI increases activity without improving decisions, it's not AI Strategy—it's expensive busywork. But when AI becomes repeatable internal capability, it creates measurable competitive advantage: faster response cycles, clearer customer insights, stronger engagement, and customer experience improvements that customers actually feel.
This belief was validated on Wednesday, January 21, 2026, in Oklahoma City, Oklahoma.
Session ratings were near-perfect:
Quality of Content: 4.9
Quality of Delivery: 4.9
Applicability: 4.9
Would Recommend: 100%
Those numbers matter, but the written feedback reveals why: this didn't feel like theory. It felt like operational intelligence paired with practical frameworks, so automation becomes valuable instead of generic.
In Their Words
Comments that captured the spirit of the day:
"Best takeaway tools of any speaker to date… Great challenge, reflections, and ideas on pivoting business to AI integration… Personable and genuine speaker."
"Mind blowing."
"Stormie did a great job of presenting AI in a way that was very concrete and applicable to the listeners. Pairing it with valuable information about personas and positioning made the automation using AI even more valuable."
"This was a very eye-opening presentation for me! I will definitely be looking into AI opportunities for our organization."
The survey comments revealed what's happening inside real companies:
"I'm with a smaller nonprofit and run the one-man marketing department… trying to figure out how to implement this to help me get things done since so much is on my plate."
"Small company that is growing fast. How can we use AI… to bridge gaps and keep us nimble and automate workflows so we don't have to hire a ton of people."
"So many tools. Just trying to wrap my head [around] sales & ops to internal skills for improved focus and productivity."
That's the real Oklahoma City story: leaders aren't asking, "Is AI real?" They're asking, "How do we operationalize it without chaos, and how do we turn it into measurable ROI without sacrificing quality?"
What the Survey Revealed About AI Readiness
Sixteen attendees completed the readiness assessment. The value lies in revealing what most teams feel but don't always articulate: AI potential is high, but internal capability is still catching up.
1. Ownership Remains the First Leverage Point
Who is the single person responsible for driving AI and automation outcomes:
CEO/GM (named, accountable): 5 of 16 (31.2%)
No clear owner: 4 of 16 (25.0%)
Working group, no single owner: 4 of 16 (25.0%)
Functional leader (Sales/Ops/IT): 3 of 16 (18.8%)
Only one-third have clear top-level accountability. The rest split between "no owner" and "shared ownership"—which feels collaborative but often slows execution because no one has the mandate to set priorities, enforce guardrails, and drive adoption.
This is why AI leadership matters more than any tool. Companies can buy technology in a day. They cannot buy internal alignment and execution discipline overnight.
2. Response Speed Improving, But Many Teams Still Move in Weeks
When a key performance metric drops 15%, how quickly can the team make production changes:
Within a week: 8 of 16 (50.0%)
Within a month/quarterly: 6 of 16 (37.5%)
Same day: 2 of 16 (12.5%)
Half can respond within a week—a strong signal. But more than a third operate in month-or-quarter cycles. That's the hidden cost of slow feedback loops: by the time you respond, the market has moved, customers have decided, or damage has spread.
AI becomes meaningful when it shortens the distance between signal and response.
3. Most Teams Have Data, But It's Not Packaged for Speed
If they wanted to run a 30-day AI pilot this month, what data is ready:
Raw data we could label if needed: 11 of 16 (68.8%)
Scattered or siloed exports only: 2 of 16 (12.5%)
Clean, labeled dataset with access controls: 2 of 16 (12.5%)
Nothing accessible yet: 1 of 16 (6.2%)
Most companies aren't "data poor"—they're "data unorganized." That's why AI initiatives start with excitement then bog down: the data exists, but organizations lack clean, governed pathways to turn it into customer insights.
4. Safety Guardrails Remain Inconsistent
Current rules for using AI safely:
Informal habits (no consistent enforcement): 6 of 16 (37.5%)
Rules exist, partly enforced: 6 of 16 (37.5%)
No protections in place yet: 3 of 16 (18.8%)
Sensitive data blocked, activity logged/reviewed: 1 of 16 (6.2%)
That means 15 of 16 (93.8%) operate without mature, consistently enforced protections. Customer experience isn't only about speed and personalization—it's about trust. AI Strategy that ignores guardrails will eventually create customer-facing mistakes.
5. Pilots Happening, But Many Aren't Reaching Production
AI or automation pilots that made it to production in 12 months:
0 (pilots only): 9 of 16 (56.2%)
3 or more: 4 of 16 (25.0%)
1–2: 2 of 16 (12.5%)
Paused pilots: 1 of 16 (6.2%)
This is the "middle zone" many organizations occupy. They've experimented. They may have wins. But the bridge from pilot to production requires different discipline: documentation, training, ownership, review cadence, and measurement.
Modernization without chaos isn't a single project—it's a rhythm.
6. Most Teams Aren't Measuring AI Impact with KPIs
Current measurement approach:
No KPIs tied to AI yet: 11 of 16 (68.8%)
At least one use case has clear KPI, owner, review cadence: 3 of 16 (18.8%)
Track results occasionally, no one owns KPI: 2 of 16 (12.5%)
If AI work isn't tied to KPIs, it will drift. The moment quarters get tight, "interesting experiments" get deprioritized. This is why AI leadership is operational, not inspirational: it sets the scoreboard, assigns ownership, and maintains steady review cadence.
7. Leaders Want Growth, But Everyone Feels the Skills Gap
Number one outcome leaders want from AI:
Revenue growth: 10 of 16 (62.5%)
Cost reduction: 3 of 16 (18.8%)
Customer experience: 2 of 16 (12.5%)
Talent/skills gap: 1 of 16 (6.2%)
Number one blocker to AI adoption:
Talent/skills: 15 of 16 (93.8%)
Data quality: 1 of 16 (6.2%)
That blocker number isn't subtle. Oklahoma City leaders aren't saying, "We need better tools." They're saying, "We need internal capability."
They're also hungry to build it: 16 of 16 (100%) want to learn more about developing internal AI leaders.
Confidence in AI competitiveness by 2027 (1-10 scale):
Average: 6.4 out of 10
8–10: 4 of 16 (25.0%)
5–7: 9 of 16 (56.2%)
1–4: 3 of 16 (18.8%)
Most leaders are cautiously optimistic. They believe they can get there, but they know it requires focus, not hype.
Why Personas and Positioning Made Automation More Valuable
One of the most insightful comments: pairing AI with personas and positioning made automation "even more valuable." That captures the missing step in many AI efforts.
Teams can generate endless output with AI. But output isn't the goal. Customer engagement is. Customer experience is. Measurable ROI is.
Personas and positioning help AI work because they provide constraints:
Who are we speaking to?
What outcome are they trying to achieve right now?
What do they fear or resist?
What do they need to believe to take the next step?
What should our brand sound like when we respond?
Without those answers, AI generates words. With those answers, AI supports strategy.
A 30-Day Path from Interesting to Operational
If you want concrete steps from "AI is interesting" to "AI is operational," here's a 30-day approach that fits what Oklahoma City leaders requested: structural clarity, real constraints, and measurable outcomes that don't overwhelm teams.
Week 1: Choose One Workflow Tied to Customer Experience or Revenue
Pick one customer-facing workflow where speed and consistency will improve engagement.
Strong first choices:
Lead follow-up and appointment setting
Proposal and follow-up drafting
Customer support response drafting with human review
Customer onboarding communication and check-ins
Content repurposing that stays on-message
Week 2: Lock the Persona, Promise, and Guardrails
Document the basics in plain language:
Persona: who we're serving
Positioning: why they choose us and what experience we deliver
Guardrails: what data can't be entered, what must be reviewed, what "safe" looks like
This is where AI Strategy becomes trustworthy. Trust is a growth strategy.
Week 3: Build a Reusable Prompt Pack
Instead of telling people "use AI," give them repeatable prompts aligned to customer insights and brand voice.
Simple starter pack:
Persona summary prompt (turn notes into usable profiles)
Message clarity prompt (simplify, sharpen the promise)
Draft prompt (email, support response, follow-up)
Objection handling prompt (address top hesitations)
Quality check prompt (tone, accuracy, next steps, compliance)
Week 4: Measure One KPI and Decide Next Steps
Choose one KPI and review weekly. Then decide: scale, adjust, or stop.
Examples:
Response time to inbound leads
Lead-to-meeting conversion rate
Proposal turnaround time
Ticket resolution time
Hours saved without quality loss
Customer satisfaction signals
This weekly cadence turns modernization into a system. It also turns AI from "tool everyone tries" into capability the business can rely on.
How GPS Summit Builds Next-Level Capability
Oklahoma City made this clear: leaders want AI that's practical, safe, measurable, and connected to customers. They want tools, yes, but more importantly, they want internal capability.
That's exactly what GPS Summit is designed to build: AI leadership, repeatable AI Strategy, and implementation pathways that create competitive advantage without compromising 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. Not inspiration. Structural capability.
The 90-day transformation pathway includes:
Applied learning with immediate implementation frameworks
Force multiplier development (2x→10x→100x operational intelligence)
Speed to competency that outpaces traditional academic approaches
Measurable ROI within the first quarter
Ready to develop your internal AI leader? Enroll your high-potential employee in GPS Summit
A Closing Question
If your team picked one workflow to improve in the next 30 days using AI, what would you choose so customers feel the difference in their experience and your team sees measurable ROI?
That's the question GPS Summit answers—with precision, discipline, and applied learning that creates lasting competitive advantage.
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