Step 1: Set an AI vision

Designing the future operating model, not just another tool.

Why vision defines the architecture

For technology leaders, the hardest part of AI transformation isn’t proving that AI works, it’s designing systems that can support, scale, and are reliable. The difference between pilots that stall and enterprise systems that last isn’t the model. It’s the vision and architecture behind it.

IT leaders worry about integration complexity, uptime, stack reliability, modernization, and reducing operational risk. These teams carry responsibility for ensuring every system is stable, secure, and integrated into the broader tech ecosystem. Their AI vision must reflect those realities, not sit outside them.

Too many organizations start with the technology: test an LLM, run a workflow, stand up a POC. But without clear alignment on the business outcomes, the integration path, and the governance model, even promising pilots break when introduced into legacy systems, telephony, CRM, or ticketing platforms.

Vision is architecture. It defines where AI fits, how it integrates, and the level of reliability required to serve customers at scale.

DoorDash shows what a strong AI vision enables. After clearly defining their desired outcomes, technical requirements, and governance model, they were ready for launch in just 6 weeks, and now automate 350,000 customer calls every day with enterprise-grade reliability and scale. This level of performance isn’t the result of a better model. It's the result of a clear, well-architected vision that aligned IT, Product, Ops and C-suite from the start.

Define outcomes, not models

A meaningful AI vision begins with outcomes that matter to the business, not technical novelty.

Technical buyers care about modernization, stack ROI, system uptime, reliability, and compliance.

Your success metrics should include:

• Reliability: Enterprise-grade uptime and performance under real-world load.
• Latency: Sub-second responses across systems to avoid customer frustration.
• Resolution accuracy: 90%+ completion for top workflows, improving NPS and reducing load on agents.
• Cost efficiency: Lower total cost of ownership, increased throughput, and reduced maintenance burden.
• Compliance: Zero violations through deterministic guardrails, ensuring governance and auditability.

These outcomes guide every architectural decision. When you start here, the technology naturally aligns with business priorities, not the other way around.

AI success is measured by what it delivers safely, consistently, and at scale. Not by what it can demo.

Partner with the business early

A common misstep for technical leaders is designing AI systems based on feasibility alone. But the business defines the why, and IT defines the how. Both are required.

Tech/Product leaders focus on innovation, customer experience, and speed; IT leaders focus on governance, security, and modernization. Successful AI vision requires blending these forces.

Bring business partners in early to answer questions like:

• What outcomes fundamentally change how we serve customers?
• Where can AI reduce backlog, busywork, or variability?
• What metrics matter most to executives — efficiency, NPS, cost, or reliability?

When IT collaborates early with CX, Operations, and the C-suite, the vision aligns to the organization’s risk tolerance, ambition, and roadmap timelines.

Architect for the blended workforce

The future contact center is a hybrid environment: AI handles the volume; humans handle what requires judgment, empathy, or specialized skill.

Leaders want scalability without creating operational drag, and care about freeing skilled teams from repetitive tasks while maintaining governance.

Your AI vision should answer:

• What percentage of interactions should AI resolve?
• How do humans and AI exchange context seamlessly?
• How is compliance maintained across channels?
  

This blended operating model is not “AI vs. humans.” It’s a strategic reallocation of effort: AI absorbs the predictable; humans elevate the experience.

Think in systems, not pilots

A pilot is not a system and without systems thinking, pilots break in production.

Your AI vision should explicitly include:

• Shared infrastructure
• Clear governance structures
• Observability across every model, decision, and integration
• A roadmap for scalability without burdening engineering

This approach prevents “pilot sprawl” by avoiding a patchwork of vendors and experiments that fragment your enterprise instead of building toward a unified standard.

It turns AI from a collection of isolated trials into a repeatable, scalable operating capability.

Document and communicate the vision

A strong AI vision should be documented like a product strategy, not a technical spec. Leaders need a shared, concise articulation of what AI will achieve, how it will be measured, and what it requires from the business to succeed. Expanding each component ensures your roadmap is clear, durable, and executable.

Capture your plan like a product strategy:

• North Star: Define the measurable end-state you’re driving toward, such as target containment, reliability, or cost efficiency, so every team understands what “great” looks like.
• Operational metrics: Specify the KPIs you’ll track such as latency, accuracy, uptime, compliance to ensure the AI initiative is evaluated with the same rigor as any mission-critical system.
• Required integrations: Map the systems, APIs, and data dependencies the AI agent must connect with so IT, engineering, and business teams understand the technical foundation required for success.
• Governance model: Outline who owns which decisions, how changes are approved, and how updates are monitored so your AI deployment remains stable, compliant, and predictable as it grows.
• Timeline for value: Lay out the first 90 days, six months, and one year of expected milestones to set realistic expectations and demonstrate when the organization will begin seeing measurable returns.
  

When business and technology share a single definition of success, every decision from vendor selection to governance moves in the same direction.

Your AI vision becomes the bridge between ambition and execution. Define it early, and every other part of the strategy can scale with confidence.

Let's move on to Step 2 - Design the right partnership model (build vs. buy).