AI Strategy and Business Value 101

Why AI Strategy Matters

Most companies see AI as critical to their future, yet only a minority capture significant value from it. In Sept 2025, BCG reported that about 60% of companies are not achieving any material value from AI, often because they haven't paired AI investment with a clear value ambition and leadership commitment needed to scale beyond pilots.

The difference between high performers and the rest? High performers use AI for reshaping workflows and creating new revenue streams, going beyond automation and incremental productivity. They connect AI capabilities directly to strategic priorities and measurable outcomes.

The Reality Check

As an AI Champion, your role is to bridge this gap—identifying opportunities that matter and building cases that resonate.

Understanding GenAI Capabilities

Generative AI—large language models like GPT-5.x, Claude, and Gemini—has fundamentally changed what's possible for business users. Unlike traditional AI that required data scientists and months of model training, GenAI can be applied immediately by anyone who can write a clear prompt.

What GenAI Does Well

When GenAI Is a Good Fit

GenAI works well when:

• The task involves language (reading, writing, summarizing, translating)
• A knowledgeable human could do it, but it takes time
• "Good enough" output is valuable (with human review)
• The work is repetitive but requires judgment
• You need a first draft, not a final answer

When GenAI Is Not the Answer

GenAI is not the right tool when:

• You need precise numerical predictions (use predictive ML instead)
• Accuracy must be 100% with no human review
• The task requires real-time data the model doesn't have
• You're making high-stakes automated decisions without oversight
• The domain is highly specialized with no training data exposure

Note: Traditional predictive AI (forecasting, classification, anomaly detection) remains valuable for specific use cases. These require data science expertise and historical data. This curriculum focuses on GenAI, which business users can apply directly.

---

Workplace Scenario: Customer Service AI

You are a product manager tasked with improving customer service at your company. In an upcoming strategy meeting, you must propose how AI could help.

Your call center currently handles 50,000 calls monthly. Average handle time is 8 minutes. Customer satisfaction (CSAT) sits at 72%. Leadership wants both metrics improved without proportionally increasing headcount.

Your hypothesis: A GPT-powered assistant could support agents during calls—suggesting responses, surfacing relevant knowledge articles, and handling routine post-call summaries.

Before proposing, consider:

• Does this align with strategic priorities (cost efficiency + customer experience)?
• Which AI capability applies (NLP/generative)?
• How would you measure success (AHT reduction, CSAT improvement, agent adoption)?
• What's realistic to expect vs. hype?

---

Evaluating AI Business Cases

The Value Equation

AI creates value through four mechanisms:

1. Efficiency: Reduce time, cost, or errors in existing processes
2. Effectiveness: Improve quality of outcomes or decisions
3. Experience: Enhance customer or employee satisfaction
4. Enablement: Make previously impossible things possible

Avoiding Common Pitfalls

Hype Traps to Watch For:

Questions to Pressure-Test Your Case:

• What's the baseline we're improving against?
• How will we measure impact in the first 90 days?
• Who needs to change behavior for this to work?
• What happens when the AI is wrong?

---

Scenario: The Elevator Pitch

Imagine you have 60 seconds with your VP of Operations. How would you pitch the call center AI assistant?

Structure your pitch around:

1. The problem (one sentence)
2. The AI solution (one sentence)
3. The expected value (specific metrics)
4. The strategic alignment (why now, why this)

Example pitch:

"Our agents spend 40% of call time searching for information, driving up handle times and frustrating customers. A GPT-powered assistant can surface relevant answers in real-time, potentially reducing AHT by 15-20% while improving first-call resolution. This directly supports our efficiency targets and customer experience goals—and we have the call transcripts to train a proof of concept within 60 days."

---

Prompt Lab: Formulating AI Problems

Given a business goal, can you define a problem that AI could tackle?

Exercise: For each goal below, formulate a specific, AI-solvable problem statement:

1. Goal: Reduce customer churn

   • Example problem: "Identify customers likely to churn in the next 30 days so retention teams can intervene proactively."

2. Goal: Improve sales productivity

   • Your turn: What could AI predict, generate, or automate?

3. Goal: Reduce compliance risk

   • Your turn: Where could NLP or prediction help?

A well-formed AI problem statement:

• Specifies the decision or output needed
• Identifies available data inputs
• Connects to a measurable business outcome
• Has a human who can act on the AI's output

---

Completion: Your AI Proposal

To complete this module, submit a short proposal (1-2 paragraphs) for an AI project in your area.

Your proposal should include:

1. The Problem: What specific business challenge are you addressing?
2. The AI Solution: Which AI capability would you apply and how?
3. Expected Value: What metrics would improve and by roughly how much?
4. Strategic Alignment: How does this connect to organizational priorities?

Assessment Rubric:

Your proposal will be reviewed by your AI Champion cohort or facilitator for feedback.

---

Key Takeaways

• GenAI delivers value only when aligned with business strategy
• GenAI excels at language tasks: drafting, summarizing, analyzing, conversing
• Know when GenAI is the right tool—and when predictive AI or other solutions fit better
• Strong business cases quantify impact and acknowledge limitations
• The best GenAI opportunities involve repetitive language work where "good enough with review" is valuable

Sources

• The Widening AI Value Gap, Sept 2025 (BCG)
• AI at work but not at scale, Dec 2025 (McKinsey)

Next Steps

In the next module, we'll dive into Enterprise AI Readiness & Use Case Prioritization—learning how to assess your organization's readiness and systematically rank the highest-value opportunities.