What If Your AI Strategy Is Holding You Back?

For service leaders, AI isn’t just another technology trend - it’s a major opportunity to transform how work gets done.

But as many organizations are discovering, moving fast with AI doesn’t always mean moving in the right direction.

In this episode of UNSCRIPTED, host Sarah Nicastro sits down with Jayda Nance, AI Product Owner at IBM, to explore why clear problem determination beats trendy tech adoption, how to distinguish between automation and true AI, and why pilots - not large-scale rollouts - are the key to proving real business value.

The result? A practical, no-hype approach to AI that helps service leaders create value — not just activity.

Listen to the Full Episode

Stop Chasing Trends: Why AI Strategies Often Miss the Mark

One of the biggest risks in AI adoption today is the tendency to chase momentum instead of solving meaningful problems.

Organizations feel pressure to:

• Keep pace with competitors
• Align with industry trends
• Demonstrate progress with AI

However, as Jayda explains, this often creates short-term momentum without long-term impact.

Without a clear understanding of the underlying problem, AI initiatives can lead to:

• Misaligned investments
• Low adoption
• Solutions that fail to scale

Start With the Problem - Not the Technology

A central theme of the conversation is adopting a “reporter mindset.”

Before selecting any technology, leaders must take the time to:

• Observe what is actually happening
• Ask deeper, more meaningful questions
• Understand root causes

In many cases, what appears to be an AI opportunity is not a technology gap at all.

More often, the issue lies in a lack of process clarity.

Fix the Process Before Adding Intelligence

One of the most practical insights from this discussion is that not every challenge requires AI.

In many instances, the real issue can be addressed by:

• Redesigning workflows
• Improving data quality
• Clarifying roles and responsibilities

Introducing AI into a flawed process does not resolve the issue — it amplifies it.

A strong operational foundation must come first.

AI vs. Automation: Understanding What You Actually Need

Another important distinction is understanding when to use automation versus AI.

• Automation is suited to repetitive, rule-based tasks
• AI is required when systems need to learn, adapt, and make decisions

Applying AI where automation would suffice increases cost and complexity unnecessarily, while failing to use AI where it is needed limits potential impact.

The objective is not to use AI everywhere, but to use it where it creates the most value.

Why Pilots Beat Large-Scale Rollouts

Rather than committing to large, complex initiatives from the outset, Jayda emphasizes the importance of starting with pilots.

Pilots enable organizations to:

• Prove value quickly
• Minimize risk
• Build confidence across stakeholders

For example, testing a solution on a small number of service requests over a short period can validate whether the approach is viable before scaling further.

This approach ensures that investment decisions are based on evidence rather than assumption.

AI Doesn’t Work Without the Right Mindset

Technology alone does not drive transformation — people do.

A common barrier to AI adoption is mindset.

Concerns such as job displacement, lack of technical expertise, or perceived complexity can limit engagement.

However, AI tools are becoming increasingly accessible.

The differentiator is not technical background, but curiosity and willingness to engage.

Organizations that foster a culture of learning and experimentation are better positioned to succeed.

Building Momentum Through Early Wins

AI adoption should be viewed as a journey rather than a single initiative.

Early successes play a critical role in building trust and momentum.

Starting with focused pilots allows organizations to:

• Demonstrate tangible value
• Reduce resistance to change
• Create internal advocates

Over time, this momentum enables broader and more effective adoption.

Key Takeaways for Service Leaders

• Begin with clear problem determination before selecting technology
• Address process inefficiencies before introducing AI
• Understand the distinction between automation and AI
• Use pilots to validate value before scaling
• Build trust through early, measurable successes
• Prioritize mindset and culture alongside technology

The Future of AI in Field Service Is Intentional

This conversation reinforces that AI is not about chasing trends or deploying technology for its own sake.

It is about solving real problems, creating measurable value, and building strategies that can scale sustainably.

For service leaders, the implication is clear:

Organizations that succeed with AI will not necessarily be the fastest adopters, but the most deliberate in how they apply it.