Birmingham's AI Moment: From Using Tools to Building Systems

Birmingham, Alabama, January 14, 2026 — AI is only a competitive advantage when it moves from "interesting" to "operational."

If it stays as a handful of experiments, a few clever prompts, or a side project owned by no one, it becomes noise. If it becomes a capability with ownership, guardrails, and measurable outcomes, it becomes leverage that improves customer experience, strengthens customer engagement, and accelerates strategic growth.

That conviction felt especially real on Wednesday, January 14, 2026 at the Vulcan Museum in Birmingham, Alabama. The workshop ratings were clear:

• Content: 5

• Deliverability: 5

• Applicability: 5

• Would Recommend: 100%

But what stood out most was the kind of feedback leaders shared after a session like this—the kind that signals readiness for the next level. One comment said it plainly:

"I would have liked to have seen more detail about how to create the agents and automation connections, not just how to use them."

That is not a criticism. That is a signal. It means the room is already beyond curiosity. Leaders are asking for build-level AI strategy, not just use-level tips. They want the capability to design and deploy systems that create lasting competitive advantage.

What We Learned From the Birmingham Survey Responses

Twenty attendees completed the post-session survey. When you look at their answers together, a pattern emerges that applies to almost every industry right now: leaders want results quickly, but most organizations are still missing the operating structure that makes AI repeatable.

Here are the most important takeaways, with the numbers that tell the story.

1. Ownership is still the first bottleneck

When asked who is the single person responsible for driving AI and automation outcomes:

• 8 of 20 (40%) said there is no clear owner

• 5 of 20 (25%) said a functional leader (Sales/Ops/IT)

• 5 of 20 (25%) said the CEO/GM is the named, accountable owner

• 2 of 20 (10%) said there is a working group, but no single owner

This is the starting point for AI leadership. If ownership is unclear, adoption becomes inconsistent. If adoption becomes inconsistent, results never compound.

2. Most teams are still operating with slow response cycles

When a key performance number drops 15%, how quickly can teams make a change in production?

• 12 of 20 (60%) said within a month or quarterly

• 6 of 20 (30%) said within a week

• 1 of 20 (5%) said same day

• 1 of 20 (5%) said rarely, or only in crisis mode

This matters because the companies that win with transformation are the ones that shorten the distance between signal and response. Faster decisions create better customer insights. Better customer insights create better execution. Better execution improves customer experience.

3. Data readiness is promising, but still messy

If they wanted to run a 30-day AI pilot this month, what data do they already have ready?

• 11 of 20 (55%) said raw data they could label if needed

• 4 of 20 (20%) said scattered or siloed exports only

• 3 of 20 (15%) said a clean, labeled dataset with access controls

• 2 of 20 (10%) said nothing accessible yet

Most teams are not starting from zero. But only 15% are truly "ready" in a way that supports speed and safety. This is why AI in marketing, sales operations, and customer support often starts strong, then stalls. The data exists, but it is not structured for repeatable use.

4. Guardrails are improving, but still inconsistent

How strong are current rules for using AI safely?

• 9 of 20 (45%) said rules exist, but are only partly enforced

• 4 of 20 (20%) rely on informal habits (no consistent enforcement)

• 4 of 20 (20%) have no protections in place yet

• 3 of 20 (15%) block sensitive data from entering AI tools, with activity logged and reviewed

That last number is important. Only 15% are operating with mature guardrails. If your goal is better customer experience and stronger customer engagement, safety and trust are not "later." They are now.

5. Pilots are happening, but production adoption is still hard

In the last 12 months, how many AI or automation pilots have made it into production?

• 9 of 20 (45%) said 1–2

• 8 of 20 (40%) said 0 (pilots only so far)

• 2 of 20 (10%) said 3 or more

• 1 of 20 (5%) said they paused pilots

This is a common inflection point. Organizations are experimenting, but many are not yet building the internal systems to move experiments into production workflows.

6. Measurement is still the missing piece

Which statement best describes today?

• 12 of 20 (60%) have no KPIs tied to AI yet

• 5 of 20 (25%) track results occasionally, but no one owns a KPI

• 3 of 20 (15%) have at least one use case with a clear KPI, a named owner, and a regular review cadence

AI strategy becomes real when it is measured and reviewed like any other operational priority. Without KPIs, AI becomes "activity." With KPIs, AI becomes strategic growth infrastructure.

7. Leaders want growth, but skills are the biggest blocker

The top outcomes leaders want from AI:

• Revenue growth: 9 of 20 (45%)

• Cost reduction: 6 of 20 (30%)

• Customer experience: 3 of 20 (15%)

• Talent/skills gap: 2 of 20 (10%)

And the #1 blocker to adoption:

• Talent/skills: 11 of 20 (55%)

• Data quality: 3 of 20 (15%)

• Leadership buy-in: 3 of 20 (15%)

• Tech stack/tools: 2 of 20 (10%)

• Budget: 1 of 20 (5%)

This is the heart of AI leadership. Most companies do not need more tools. They need internal capability—the kind of capability that turns tools into outcomes.

A few comments captured the human side of that capability gap:

"Looking for best practices in a non-profit."

"We may have a Luke Skywalker candidate on site."

"We aren't late at everything, but have inefficiencies."

"Very eye opening. Great information."

That mix is exactly what leaders are living right now: urgency, optimism, constraints, and a desire to make AI practical.

Why the "Agent-Building" Feedback Matters

The request for more detail on creating agents and automation connections is not a technical side note. It's the next stage of maturity.

Using AI is helpful.Building AI-powered systems is transformative.

When leaders say they want to know how to create agents and connect automations, they're really asking:

• How do we make AI repeatable across the team, not just useful for one person?

• How do we connect AI outputs to real workflows, like follow-up, reporting, onboarding, customer support, and marketing execution?

• How do we reduce friction and protect customer experience at the same time?

That is the difference between occasional productivity gains and sustained competitive advantage.

A Learning Moment: The Agent and Automation Blueprint

If you want a practical starting point, here is a simple blueprint for building your first internal agent and connecting it to automation in a way your team can manage.

Step 1: Start with one workflow that touches the customer

Choose a workflow where improved speed and consistency will improve customer engagement and customer experience.

Good candidates:

• Lead follow-up and qualification

• Customer support response drafting with human review

• Proposal and statement-of-work drafting

• Content repurposing for AI in marketing (from one source to many channels)

• Weekly reporting summaries that turn data into customer insights

Step 2: Define the agent's job like a role description

Write a short "role card" for your agent:

• Purpose: what it helps the business do

• Inputs: what it can access (documents, notes, CRM fields, approved data)

• Constraints: what it must never do (invent facts, share sensitive data, skip review)

• Output format: what "done" looks like (email draft, summary, checklist, next-step plan)

• Escalation rule: when it must hand off to a human

This step is AI strategy in action. Clarity prevents chaos.

Step 3: Build a minimum safe review loop

Before anything reaches customers, define:

• Who approves outputs

• What gets checked (accuracy, tone, compliance, clarity, next step)

• Where approved outputs are stored (so the team builds a reusable library)

This is how you protect customer experience while scaling speed.

Step 4: Connect the agent to a single automation trigger

Keep the first automation simple. One trigger, one action, one logging step.

Examples:

Trigger: New inbound lead form submitted

Action: Agent drafts a personalized response and follow-up plan

Log: Store the output and tag it for review

Trigger: Support ticket created

Action: Agent drafts a response and suggests knowledge-base updates

Log: Store the response draft and confidence notes

Trigger: Weekly sales report export available

Action: Agent summarizes trends and flags anomalies as customer insights

Log: Store summary in a shared dashboard

Step 5: Measure one KPI for 30 days

Pick one measurable outcome tied to strategic growth or customer experience.

Examples:

• Response time

• Conversion rate

• Ticket resolution time

• Proposal turnaround time

• Lead-to-meeting rate

• Customer satisfaction signals

Then review weekly. Improve what's weak. Keep what works. That's how transformation becomes an operating rhythm.

Where GPS Summit Fits

Birmingham made something clear: leaders want practical AI, and they want the capability to build, connect, and operationalize it.

GPS Summit exists for that exact moment. It is a three-day intensive (February 25-27, 2026) designed to develop your high-potential leader into an AI Systems Generalist—the internal connector who can:

✓ Build AI-powered agents and automation systems (not just use pre-built tools)

✓ Connect AI outputs to real workflows across marketing, sales, and operations

✓ Lead responsible adoption with clear guardrails that protect customer experience

✓ Turn pilots into production systems with measurable KPIs

✓ Create repeatable AI strategy that compounds over time

Your HiPo will leave with:

• A 90-day implementation roadmap with specific agent-building and automation projects

• Hands-on skills in designing agents, connecting automations, and building review loops

• A peer network of AI Systems Generalists from other organizations

• The confidence to answer "how do we create the agents and automation connections?"—not just "how do we use them?"

This is People-Process-Tech integration at the systems level. This is how you turn "we want to build, not just use" into operational reality.

Take the Next Step

• Explore GPS Summit: https://www.breatheexp.com/gps-summit

• Enroll your high-potential leader in GPS Summit: https://www.breatheexp.com/event-details/breathe-gps-summit

• See the full competitive comparison: https://www.breatheexp.com/corporate-cohort

• Learn more about BREATHE! Exp: https://www.breatheexp.com/

Closing Question

If you could choose one customer-facing workflow to improve in the next 30 days, what would you build your first AI agent to handle so your team moves faster without sacrificing customer experience?

And who on your team could become the AI Systems Generalist who knows how to create the agents and automation connections—not just how to use them?

BREATHE! Exp is a strategic growth firm that develops internal AI capability through world-class learning experiences. GPS Summit is our flagship three-day intensive for organizations ready to move from using AI tools to building AI systems—with clear ownership, measurable outcomes, and repeatable processes.