Pensacola Leaders Are Ready for AI. Their Systems Mostly Aren’t.

Executive Summary

• This Pensacola workshop dataset includes 21 responses, which is enough to show patterns but still small enough to treat conclusions as directional rather than universal.

• Leaders most often want AI for revenue growth (61.9%), while customer experience and cost reduction follow far behind at 14.3% each.

• The biggest blocker is talent and skills (57.1%), with data quality (19.0%) as the second-largest constraint.

• Ownership remains unsettled: 42.9% report no clear AI owner, and another 19.0% rely on a working group without one accountable leader.

• Execution maturity is thin. 57.1% say no pilots have made it into production in the last year, and 71.4% say they have no KPIs tied to AI yet.

• Governance is also weak. 57.1% report no protections for safe AI use, and only 23.8% say sensitive data is blocked and activity is logged and reviewed.

• External sector research points to the same lesson: AI value is moving to organizations that redesign workflows, name accountable leaders, and treat governance and data quality as operating requirements, not add-ons.

What the Survey Reveals About AI Readiness

Outcomes leaders want

The Pensacola responses are more commercially focused than exploratory. Revenue growth is the dominant outcome by a wide margin, which suggests participants are not primarily evaluating AI as a back-office experiment. They want it to help drive demand, conversion, follow-up, and stronger execution. Customer experience and cost reduction matter, but they are clearly secondary in this group.

That focus matters because it changes how AI should be judged. If growth is the goal, then the real test is not whether teams can generate content or summarize data. It is whether they can improve a workflow that affects pipeline, booking, quoting, retention, or customer response time.

What’s blocking progress

The most consistent blocker is capability. Talent and skills lead the list by a large margin, followed by data quality. Budget, tooling, leadership buy-in, and compliance appear, but much less frequently as the primary obstacle.

That pattern suggests Pensacola leaders are not mainly stuck at the awareness stage. They are closer to the “now what?” stage. The question is less about whether AI is relevant and more about whether the team has the internal muscle to select the right use case, build it responsibly, and keep it moving after the workshop.

The ownership gap and why it matters

This is the clearest structural issue in the survey. Nearly 62% of respondents either have no clear owner or rely on a group without one accountable person. Only 14.3% say the CEO or GM is clearly accountable, and 23.8% place responsibility with a functional leader.

The consequences show up throughout the dataset. Most respondents report no AI KPIs. Most say no pilots have made it into production. Data readiness is uneven, with only 19.0% reporting a clean, labeled dataset ready for a 30-day pilot. Confidence is decent at 6.4 out of 10 on average, but it sits on top of thin systems. That is the real pattern here: optimism is stronger than operating readiness.

One internal contrast stands out. Respondents with at least one AI use case tied to a KPI, named owner, and review cadence reported meaningfully higher confidence than those with no KPIs. The survey does not prove cause and effect, but it does point to something practical: measurement, ownership, and confidence tend to rise together.

Industry Intelligence: How 5 Sectors Are Responding to AI Right Now

Hospitality, lodging, and tourism

This was the largest normalized sector in the Pensacola data, spanning hospitality, restaurant, vacation rental, sports tourism, events, and fishing-charter style operations. External research shows the sector is under pressure to improve both productivity and guest experience at the same time. Deloitte reports that 81% of hoteliers are prioritizing employee productivity and 49% list AI-powered solutions among their top technology priorities. Outdoor hospitality data shows the same practical orientation: 76% of parks report using some form of automation, and the median operator using automation saves about 10 hours per week on tasks that would otherwise be manual.

The realistic uses here are guest messaging, booking flow, FAQ handling, review response, staff enablement, and revenue management. The common pitfall is thinking customer-facing automation alone solves the problem. In practice, hospitality AI pays off when it reduces internal friction for teams as much as it reduces friction for guests.

Construction, roofing, and restoration

Construction-related businesses in the survey included contracting, roofing, restoration, and home-service variants. Deloitte’s 2025 construction study reports that 37% of construction businesses are now using AI or machine learning, up from 26% in 2023, and that the average business uses 6.2 technologies, up from 5.3 a year earlier. At the same time, construction productivity remains a stubborn industry problem: McKinsey estimates it improved only about 0.4% annually from 2000 to 2022.

That makes the likely AI use cases fairly direct: estimating support, submittal and RFI handling, documentation, schedule risk monitoring, quality control, and field coordination. The common pitfall is stacking tools on top of fragmented workflows. Construction tends to get value when AI shortens approval cycles and reduces rework, not when it simply adds one more dashboard.

Retail, consumer goods, and distribution

This cluster includes online sales, food, wholesale distribution, and small-format consumer-goods businesses. Deloitte’s 2026 retail outlook reports that 30% of retailers surveyed already use AI for supply-chain visibility, a figure expected to rise to 41% within a year, and 59% expect a positive ROI from those initiatives within 12 months. McKinsey estimates that generative AI could unlock $240 billion to $390 billion in economic value for retailers, equal to an industry margin lift of 1.2 to 1.9 percentage points. Gartner adds an important counterweight: only 23% of supply-chain organizations say they have a formal AI strategy in place.

The practical use cases are demand forecasting, inventory planning, lead handling, customer service, pricing support, and fulfillment visibility. The biggest pitfall is confusing experimentation with operating strategy. Retail and distribution firms can easily buy AI features faster than they can integrate them into merchandising, sales, or supply-chain decisions.

Healthcare and behavioral health

Healthcare and behavioral health show up twice in the raw Pensacola industry data, and the broader market is moving quickly. McKinsey reports that 85% of surveyed U.S. healthcare leaders were either exploring or had already adopted generative AI by late 2024. The AMA found that 66% of physicians reported using healthcare AI in 2024, up from 38% in 2023, and 57% identified reducing administrative burden through automation as the top opportunity. At the same time, the AMA highlights ongoing concern around governance, privacy, liability, and disclosure.

That combination makes healthcare a useful mirror for the Pensacola survey. The near-term value is obvious in documentation, care coordination, intake, admin burden, and communications. But healthcare also shows why governance cannot wait until after adoption begins. In regulated or sensitive environments, weak controls are not a side issue. They shape whether AI can be trusted at all.

Software and digital services

The Pensacola file includes at least one clear software-as-a-service response, and the sector’s adoption curve is among the fastest. Gartner says 63% of organizations are already piloting, deploying, or using AI code assistants, and predicts that 75% of enterprise software engineers will use them by 2028. Deloitte adds that worldwide AI spending is expected to grow at a 29% compound annual rate from 2024 to 2028, while global IT spending is projected to grow 9.3% in 2025. The same outlook warns that fewer than a quarter of AI initiatives are thought to be adequately secured.

The practical uses are coding assistance, internal support agents, product knowledge retrieval, QA acceleration, and customer enablement. The common pitfall is treating speed as value by itself. In software and digital services, AI increases leverage fastest when teams pair automation with stronger review practices, security discipline, and clearer definitions of where human validation is required.

What High-Performing Organizations Are Doing Differently

The external benchmark is consistent. McKinsey’s 2025 global survey shows that most organizations are still experimenting or piloting, not scaling. The companies getting the most value are more likely to pursue growth and innovation, redesign workflows rather than bolt AI onto old processes, track KPIs, define when human validation is required, and show visible senior-leadership ownership. High performers are also far more likely to have fundamentally redesigned workflows and embedded AI into business processes rather than treating it as an isolated tool layer.

That is the clearest takeaway for Pensacola. The survey does not show a market that lacks interest. It shows a market that still needs the operating model underneath the interest.

Recommendations Informed by the Workshop Data

1. Appoint one accountable AI owner for the next 90 days. This directly addresses the 42.9% reporting no clear owner and the additional 19.0% relying on a group without one accountable person.

2. Define one KPI before launching the next pilot. With 71.4% reporting no AI KPIs, the next step is not more tools. It is one measurable business outcome.

3. Start with one workflow, not one platform. Revenue growth is the dominant goal, so choose a process close to that result: lead follow-up, booking flow, quoting, intake, or customer response.

4. Use data readiness as a scoping tool. Only 19.0% have a clean, labeled dataset ready now. That means pilot selection should follow data reality, not abstract ambition.

5. Set minimum safe-use rules immediately. More than half report no protections in place. That is a manageable gap now and a costly one later.

6. Build skills around a live use case. Talent and skills are the top blocker. The fastest way to close that gap is not a generic AI lecture. It is learning one use case end to end.

7. Create a pilot-to-production gate. A use case should not move forward without a named owner, KPI baseline, data source, review process, and safe-use rule set.

8. Translate leadership interest into capability-building. Because 85.7% said they want to learn more about developing an internal AI leader, the most relevant next-step resources are GPS Summit enrollment, the comparison with university programs, and more on BREATHE! Exp.