AI Roadmap Series: Step 2 — AI Value

In the first part of this series, we covered AI Strategy and how leaders should approach AI adoption with clarity and purpose (read here).

Now, let's move on to AI Value. The big question is:
How does AI actually add value to your company and customers?

The Deceptively Simple Answer

Many CIOs are eager to leverage AI — especially Generative AI (GenAI) — to:

• Improve employee productivity
• Optimize business processes
• Redefine their business model

On paper, this seems like the "obvious" answer. But in reality, it's a deceptively simple response to a complex problem.

The truth is that AI value isn't limited to GenAI. Other mature AI fields — such as image recognition, natural language processing, and anomaly detection — have already proven their worth. Ignoring them means leaving value on the table.

The First Question: Where Do I Need to Add Value?

Too often, leaders default to "employee performance optimization" as their main AI objective. But this decision should align with the AI Strategy you've built, not just a productivity narrative.

• Low-complexity jobs: GenAI can be a game-changer for newcomers. It removes repetitive tasks and accelerates learning. But for experienced employees, the added value may be minimal.
• High-complexity jobs: A junior accountant using GenAI might struggle to validate outputs. A senior accountant, however, can use it as a digital "junior assistant," drastically boosting performance.

If employee optimization isn't where value lies, look elsewhere:

• Support teams: Natural language models for auto-prioritization.
• Retail: Image recognition for stock management.
• Tech companies: Anomaly detection for identifying DDoS attacks.

The key is to challenge assumptions and define value clearly.

Divide and Conquer: The AI Approach

Why put all your eggs in one basket when you can divide and conquer?

Small, targeted AI projects deliver quicker wins and clearer ROI. For example, consider applying AI in customer support. Instead of building a massive "AI support platform," break down the funnel:

1. Discover → Identify user pain points (e.g., NLP log analysis).
2. Intake → Automate ticket creation and context retrieval.
3. Triage → Classify severity, intent, and assign correct SLAs.
4. Routing → Match tickets to the right solver with balanced workloads.
5. Resolve → Equip agents with AI-powered diagnosis and escalation tools.
6. Close → Ensure clean ticket closure and measure satisfaction.
7. Learn → Feed back insights to prevent recurring issues.

Each step is its own opportunity for value creation. Tackling them one by one is often more effective than launching a sweeping transformation project.

The Hidden Challenge: AI Costs

Here's the hard truth: AI is expensive.

CIOs frequently underestimate costs by 500%–1000% when building large, scalable AI products. These costs will only grow.

Consider this:

• Gartner's 2023 AI in the Enterprise Survey showed that companies deploying GenAI spent an average of $2.3M in just the proof-of-concept phase.
• Small enterprises: ~$300K
• Large global enterprises: ~$2.9M
• Expected increase: 40% next year

This is why financial discipline in AI is non-negotiable. Just as cloud adoption created the role of AWS Financial Engineers, AI will need AI Financial Engineers to ensure cost-effective usage of models and techniques.

Sometimes, a lightweight 300MB model can do the job better than an oversized "bazooka." (See our post here).

Final Takeaway

AI value is not about chasing the latest trend. It's about:

• Knowing where value matters most
• Breaking challenges into manageable projects
• Keeping a close eye on costs and ROI

Get this right, and AI won't just be a buzzword — it will be a true driver of transformation.

Having doubts or want to build your AI Strategy? Feel free to reach out!