From Legacy to AI: The Crawl, Walk, Run Approach

The prospect of migrating a busy contact centre to AI can feel overwhelming. Years of established processes, trained agents, and customer expectations don't change overnight. Legacy systems that have served you well suddenly feel inadequate in the age of GPT-4 and real-time voice AI, yet ripping everything out and starting fresh isn't realistic for a live operation handling thousands of customer interactions daily.

That's why we advocate a phased approach we call Crawl, Walk, Run. It's not just a catchy framework—it's a proven migration path that minimises risk, builds institutional confidence, and delivers measurable value at every stage.

Crawl: Listen and Learn (Weeks 1-4)

The beauty of the crawl phase is that it requires zero disruption to your existing operations. You don't change a single workflow, retrain a single agent, or alter a single customer touchpoint. Instead, you layer AI analytics on top of what you're already doing.

What You'll Deploy

Connect your existing call recordings—whether from a legacy PBX, a cloud contact centre platform, or even basic voicemail systems—to an AI-powered analytics engine. Modern platforms can process hundreds of calls per hour, transcribing conversations and extracting structured insights in near real-time.

Within days, you'll have visibility you've never had before:

Automated quality scoring across every call — not just the 2% sample your QA team manually reviews, but 100% coverage with consistent, objective criteria
Sentiment analysis — AI detects frustration, confusion, satisfaction, and escalation triggers in every conversation
Compliance monitoring — automatic flagging when required disclosures are skipped or regulatory language is missing
Agent performance benchmarks — identify your top performers, spot coaching opportunities, and see exactly which behaviours correlate with better outcomes
Customer satisfaction trends — track CSAT patterns by time of day, agent, query type, and customer segment

Why This Matters

You're building a data-driven foundation for every decision that follows. Instead of guessing which processes to automate first, you'll know. Instead of wondering whether AI can handle your customers' questions, you'll have evidence. Most importantly, you'll uncover the call types that consume disproportionate agent time but follow predictable patterns—these are your prime candidates for automation.

Typical insights from the crawl phase:

40% of inbound calls are asking variations of the same five questions
After-hours voicemail messages often go unanswered for 12+ hours
First-call resolution rates drop significantly after 6pm when senior staff aren't available
Compliance breaches cluster around specific product lines or new-agent onboarding periods

Armed with this intelligence, you move to the walk phase with clarity and confidence.

Walk: AI-Assisted Operations (Weeks 5-12)

Now you start introducing AI into live operations—but humans remain in control. The goal isn't to replace your agents; it's to make them more effective and extend your service capability beyond current constraints.

Where to Start

Begin with the channels and scenarios where AI adds the most value with the least risk:

1. After-Hours Coverage

Deploy AI voice agents to handle calls outside your staffed hours. If a customer rings at midnight, they get an intelligent agent that can answer FAQs, take messages, or even complete simple transactions like booking appointments or updating account details. When your human team arrives in the morning, the AI hands over a structured summary of overnight interactions.

Why this works: There's no displacement of existing staff, customers get immediate service instead of voicemail, and you validate AI performance in a lower-stakes environment before expanding to peak hours.

2. FAQ Automation

Route known frequent queries—password resets, order status checks, balance inquiries—directly to AI. These interactions typically follow a script anyway, and AI handles them faster and more consistently than even your best agents.

3. Intelligent Call Triage

Before connecting a caller to a human agent, let AI conduct initial qualification: confirm identity, gather context about the reason for the call, pull up relevant account history, and route to the right department or specialist. Your agents receive warm transfers with full context, eliminating the "Can you repeat that?" dance that wastes time and frustrates customers.

4. Real-Time Agent Assist

Surface AI-powered suggestions during live calls. As an agent speaks with a customer, the AI listens in (with appropriate disclosure) and provides real-time guidance: relevant knowledge base articles, recommended next-best actions, compliance reminders, or even suggested phrasing for difficult conversations. Think of it as a co-pilot that makes junior agents perform like veterans.

What to Measure

Track these metrics obsessively during the walk phase:

Containment rate — what percentage of AI interactions resolve without human escalation?
Customer satisfaction — are AI-assisted interactions scoring as well as human-only interactions?
Time savings — how much agent capacity has been freed up by automation?
Error rates — is AI making mistakes? What kind, and how often?

Expect to iterate. Your first AI scripts will be imperfect. Customers will ask questions you didn't anticipate. Edge cases will surface. That's fine—this is why you start small and expand gradually.

Cultural Change Management

Don't underestimate the human side of this transition. Agents may feel threatened, or they might resent being "monitored" by AI. Be transparent about your goals: you're not eliminating jobs, you're eliminating the tedious, repetitive work that causes burnout and making room for more valuable, fulfilling interactions. Involve your team in refining the AI's performance. When they see AI as a tool that makes their job easier—not a replacement—adoption accelerates.

Run: Autonomous AI Agents (Month 4+)

With confidence built over weeks or months, you're ready to let AI handle complete interactions autonomously—no human in the loop, no safety net. This is where cost reduction and scalability truly unlock.

Full Autonomy Across Channels

At this stage, AI isn't just assisting—it's operating independently:

Voice conversations — AI conducts natural, multi-turn phone calls with booking, payment processing, account updates, and complex query resolution. Customers often don't realise they're speaking with AI until told.
Email triage and response — AI reads incoming emails, categorises by intent and urgency, and drafts contextually appropriate replies. A human approves (or doesn't) before sending, or you can enable full auto-send for certain categories.
Web chat — AI-powered chat widgets handle site visitors 24/7, answering product questions, guiding users through processes, and seamlessly escalating to humans when needed.
Proactive outbound campaigns — AI makes thousands of calls per day for appointment reminders, payment collections, satisfaction surveys, or re-engagement campaigns—personalising each conversation based on CRM data.

Integration and Orchestration

Autonomous AI is only as good as its integrations. Ensure your AI platform can:

Read and write to your CRM (Salesforce, HubSpot, Dynamics, etc.)
Access your knowledge base in real-time
Trigger workflows in adjacent systems (ticketing, billing, fulfilment)
Authenticate customers securely without human intervention

This isn't just a contact centre play—it's a full customer experience transformation. AI becomes the connective tissue between all your customer-facing systems.

Governance and Oversight

Even at full autonomy, you need guardrails:

Human escalation paths — certain queries or customer sentiment triggers should always route to a human
Regular audits — sample AI interactions weekly to catch drift or degradation in quality
Continuous training — feed edge cases and mistakes back into your AI models so they improve over time

The goal isn't "set it and forget it." It's "set it and refine it."

Timeline and Pitfalls

A realistic crawl-walk-run transition takes 3-6 months for most organisations. Smaller operations with simpler use cases can compress this; large enterprises with compliance-heavy requirements may take longer.

Common Pitfalls to Avoid

Skipping the crawl phase — jumping straight to automation without understanding your data is a recipe for failure. You'll automate the wrong things, in the wrong way, and erode trust with both customers and staff.
Over-customising too early — resist the temptation to build bespoke AI models for every edge case. Start with out-of-the-box solutions and only customise once you've validated the core use case.
Underestimating change management — technology is the easy part. Getting buy-in from agents, managers, and executives requires communication, training, and visible quick wins.
Ignoring customer communication — some customers want to speak to a human, and that's fine. Always offer an opt-out, and never hide the fact that AI is involved.

The Bottom Line

Organisations that adopt the crawl-walk-run approach typically see:

65% cost reduction within the first quarter of full autonomy
30-50% improvement in first-call resolution as AI handles routine queries perfectly every time
24/7 service availability without proportional staffing costs
Higher agent satisfaction as they're freed from repetitive, low-value work

More importantly, they avoid the chaos, wasted budget, and reputational damage of a big-bang migration that fails publicly.

AI in contact centres isn't a question of "if"—it's a question of "how fast" and "how well." The organisations that take a phased, evidence-led approach will pull ahead. Those that wait, or worse, rush in unprepared, will struggle.

Ready to start your journey? Get in touch and we'll help you build a transition plan tailored to your operation—whether you're still in crawl mode or ready to run.

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