Step 1: Set an AI vision

From isolated automations to a transformed service model.

Why vision matters as much as technology

Most teams start with “what can we automate?” The better question is, “what experience do we want to deliver and how will AI materially enable it?”

Unfortunately, up to 95% of AI pilots stall out before they scale, not because the tech isn’t ready, but because the vision wasn’t big or clear enough to drive real transformation.

Vision is the difference between a clever bot and a compounding advantage. The point isn’t to stand up a few flows; it’s to re-architect how service works so that AI carries the operational load and humans lean into empathy, judgment, and growth.

Define a bold north star

Begin with a north star that’s bold and measurable. “Reach 80% containment without sacrificing CSAT” is far more catalytic than “launch an AI assistant.” A strong north star forces tradeoffs, sets sequencing, and clarifies success. It also shifts mindsets from “let’s try a use case” to “let’s transform how customers get help.”

Define the “why” in business—not technical—terms. What are the three outcomes that matter this year: shorter time to resolution, lower cost-per-call, higher loyalty, fewer escalations? Tie each to the behaviors AI will enable: zero-wait service during peaks, consistent handling of repetitive requests, proactive next steps instead of reactive transfers. 

When the ‘why’ is crisp, the ‘what’ falls into place.

Visualize the end-state

Paint the end-state so teams can see it. Picture a contact center where AI resolves routine interactions across voice, chat, and SMS; surfaces context before humans ever touch a case; and, when necessary, hands off with full context so agents never re-ask questions. Picture workforce plans that focus on coaching and complex work instead of staffing for spikes. Picture governance that treats AI like any other critical system: versioned, monitored, and improved.

This isn’t just theoretical. Leading organizations like AAA Auto Club Group, Fanatics, and CorVel are great examples of how a well-defined AI north star can transform a contact center. Organizations have already achieved automating 30–50%+ of call volume while redeploying human talent to higher-impact work and maintaining NPS scores above 80.

Translate vision into operating principles

Turn your vision into operating principles you can enforce. 

Examples: 

• “Transform the contact center structure so more contacts are contained by automation than handled by humans.”
• “Design human handoffs that feel premium, not punitive.” 
• “Measure success weekly and ship improvements continuously.” 
• “One brain across channels; no duplicate flows.” 

These principles keep execution consistent as you scale.

Avoid the common traps

Three mistakes that derail momentum:

1. Tech-first thinking: Too many teams declare victory at “we deployed AI.” But deployment isn’t a result, the impact is. If the initiative isn’t tied to measurable outcomes like cutting AHT by 30%, reducing cost per contact by “x amount”, or lifting CSAT, the program will stall before it proves value. 
2. Tiny targets: Starting with a trivial or edge-case use case sends the signal that the ambition is small. Low-impact beginnings produce low-momentum outcomes, which makes it harder to earn investment or organizational belief. Choose a use case that matters enough to create visible momentum.
3. Inexperienced partners: The hype of AI can lead teams to partner with vendors that have impressive demos, but unproven solutions. This opens the door to common risks like slow rollouts, limited use cases with no clear path to scale, and off-brand experiences that ultimately frustrate customers. 

Write and share your vision

Finally, write your vision like a product brief, not a press release: 

• A headline (your north star) 
• the business outcomes 
• what great looks like at steady state 
• the principles you’ll hold 
• the first three decisions that flow from it 

Share your vision across stakeholders, debate it early, and solicit feedback. Alignment starts with transparency. Step 1 isn’t about a diagram, it’s about alignment around a destination that’s worthy of the effort to get there. Let's go to Step 2 - the playbook for getting AI buy-in across your org.