How to Stop Stalling and Start Scaling

If you’re a CRO, CCO, or SVP of Services/Customer Success, you can feel the squeeze:

• Scale service delivery without scaling cost
• Improve CSAT and retention
• Drive profitable ARR growth—on fewer resources

The instinctive answer? AI.

The AI Promise vs. The AI Reality

Early in 2025, TSIA (State of Support Services 2025_ebook) technology leaders how AI-enabled initiatives were factored into their annual plans.

• 100% planned to implement something AI-enabled.
• Only 5% were scaling proven capabilities.
• 39% were still investigating.
• 56% were stuck piloting.

The Wall Street Journal (Steven Rosenbush, Companies Are Struggling to Drive a Return on AI”, WSJ, April 26, 2025) recently backed this up:

• 99% of companies investing in AI fail to scale it.
• 43% remain stuck in pilot mode.

And here’s the kicker: most of these annual plans already assumed in-year productivity gains from these AI initiatives. So when you are stuck in pilot mode and not getting the traction on your AI roadmap, bad things start to happen::

• Staff reductions hit harder.
• Service levels drop.
• Customer experience deteriorates.
• Renewal, upsell, and cross-sell opportunities vanish.

Two Common AI Implementation Pitfalls

1. Chasing the Shiny Penny

With all the hype and excitement surrounding AI, funding often goes only to projects with “AI” in the description. This pursuit of technology for technology’s sake results in leaders starting with, “What can AI do?” instead of, “What outcome do we need to achieve?”

The result? Exaggerated claims. Wasted spend.
Teams jump into execution before validating strategy, assessing capability gaps, or justifying budget. In Support Services, for example, some double down on AI initiatives focused on speed of response, when deeper analysis shows resolution time drives CSAT.

Better approach:
Start with a Performance & Capability Audit to identify the true business outcome gaps. Then determine where AI can directly close them.

2. Believing AI Is “Plug-and-Play”

As Pascal Bornet notes in his book “Agent Artificial Intelligence”:

“The future of AI isn’t plug-and-play—it’s plan-and-build.”

Bornet notes that in the rush to implement AI, most companies believe that all you have to do is plug in some data, let the AI model do it’s thing, and magic happens (see image below).

Real-world AI isn’t magic. You need to master the messy middle (data engineering, modeling, operationalization) or your agent is just a toy. Successful AI implementation needs:

• Context, memory, goals, tools, decision logic
• Real-time learning, process integration, human oversight
• A strong data foundation—or it magnifies risks

Better approach: 

ServiceNow began small—case summarization and knowledge creation—then scaled as use cases aligned with their “hospitality” vision:

• 72% of self-service interactions now AI-supported
• 37% of case workflows automated
• 60% of knowledge articles AI-generated, 88% faster to publish

The Breakthrough: The Perpetual Innovation Machine

Most companies fail at AI because they lack a disciplined method to connect AI initiatives to specific, measurable business outcomes.

The Perpetual Innovation Machine methodology fixes that with two integrated tools:

1. The 3-Page Strategic Plan – Clear Direction in Plain Language

• Page 1 – The What: Vision, mission, 5-year breakthrough strategies.
• Page 2 – The How: 12-month tactical plan + key performance metrics.
• Page 3 – Balanced Scorecard: Baselines, benchmarks, and multi-year targets.

This keeps everyone aligned and focused on what matters most.

2. Management by Fact (MBF) – Closing the Gap

• Quantifies current vs. desired performance for each strategic goal.
• Pinpoints root causes of gaps.
• Prioritizes only 2–3 tactics per breakthrough strategy.
• Ties investment directly to the biggest, most solvable gaps.

How It Works for AI Initiatives

1. Start with the business outcome gap – e.g., improve CSAT by reducing resolution times.
2. Use MBF to find the root cause – maybe poor knowledge management or slow escalation handling.
3. Propose AI solutions as targeted countermeasures – e.g., AI sentiment analysis for proactive escalation or AI-generated knowledge articles.
4. Run Predictive Lift analysis – estimate the measurable gain each AI initiative will deliver.
5. Track via the scorecard – adjust if performance drifts from plan.

Why This Works

When AI is managed through the Perpetual Innovation Machine:

• No pilot purgatory – every initiative has a business case before launch.
• No misalignment – tactics roll up to strategy, strategy rolls up to outcomes.
• No wasted spend – funding flows to what closes the biggest gaps.

AI won’t deliver growth, cost savings, or better customer experiences by accident. The difference between stalled pilots and scaled success starts with the business outcome, not the technology. The Perpetual Innovation Machine methodology—powered by a clear 3-page strategic plan and Management by Fact—ensures every AI initiative is tied to measurable value, prioritized against the biggest gaps, and continuously tracked for impact. With this approach, AI stops being an experiment and becomes a core driver of profitable ARR growth.

Want to learn more? Book your discovery briefing now.