AI Applied: Turning hype into real-world impact

Artificial intelligence (AI) is everywhere, but for most organizations, the challenge isn’t awareness. It’s knowing where to start and how to turn potential into practical, measurable value. 

The path forward doesn’t require a complete transformation overnight. Instead, it starts with a clear understanding of where AI can meaningfully support day-to-day work, such as improving efficiency, reducing manual effort and helping teams focus on higher-value activities. 

Start with the problem, not the technology 

One of the most common pitfalls in AI adoption is starting with the tool instead of the problem. Organizations often look for a “perfect” use case. When in reality, value is usually found in smaller, everyday inefficiencies. 

A better starting point is simple: What does a typical workday look like, and where does it break down? 

Across industries, similar challenges show up: 

• Manual data entry across multiple systems  
• High volumes of emails and repetitive communication  
• Disconnected workflows between teams and tools  

AI doesn’t need to solve everything to be valuable. If it can give employees even a few hours back per week, the impact compounds quickly across teams and over time.  

Think in workflows, not buzzwords 

AI conversations are often filled with technical language that can make adoption feel more complex than it needs to be. 

In practice, AI is best understood as part of a workflow. 

Think of it as a digital extension of your workforce: 

• Some tools handle simple, repeatable tasks  
• Others can analyze, summarize or generate content  
• Together, they support end-to-end processes  

The goal isn’t to master terminology. It’s to create workflows that remove friction and make work easier. 

The 90% rule: Progress over perfection 

Another key mindset shift is letting go of the idea that AI must be perfect to be useful. 

Many of the most effective use cases today focus on acceleration, not replacement: 

• Drafting emails or reports  
• Summarizing meetings or documents  
• Organizing notes and action items  

These outputs often get users 90–95% of the way there, with humans providing final review and context. That last 5–10% still matters, but the time saved getting to that point is where the value lies.  

A new era of development and new risks 

AI is also changing how software is created. Capabilities like prompt-driven development are lowering the barrier to entry, enabling more people to build applications and automate processes without traditional coding expertise. 

This creates new opportunities: 

• Faster prototyping and innovation  
• Greater business-user involvement  
• Reduced development timelines  

But it also introduces real risks: 

• Sensitive data being exposed or mishandled  
• Applications built without proper security controls  
• Lack of alignment with enterprise systems  

In this environment, governance becomes critical. Just because something can be built quickly doesn’t mean it should be deployed without structure. 

Governance: Finding the right balance 

Effective AI adoption requires a balance between enablement and control. 

Organizations need to: 

• Encourage experimentation and innovation  
• Protect sensitive data and systems  
• Ensure consistency across tools and platforms  

There’s no one-size-fits-all model. Governance should reflect: 

• Industry requirements (e.g., healthcare vs. manufacturing)  
• Data sensitivity levels  
• Organizational risk tolerance  

Rather than restricting access entirely or allowing unchecked usage, leading organizations are creating guardrails, clear guidelines that allow teams to innovate safely. 

Building AI fluency across the organization 

Technology alone isn’t enough. One of the most important enablers of successful AI adoption is AI fluency. 

This means helping employees understand: 

• What AI can realistically do  
• How it fits into their daily work  
• How to use it responsibly  

Without this foundation, usage becomes fragmented; different tools, inconsistent approaches and increased risk. With it, organizations can scale adoption more effectively and unlock broader value across teams. 

A practical path forward 

For organizations looking to get started, or move beyond early experimentation, a structured, iterative approach works best: 

1. Listen to the business: Engage teams to understand real workflows, challenges and inefficiencies 

2. Prioritize use cases: Focus on opportunities that offer clear, measurable impact 

3. Launch targeted pilots: Test solutions in a controlled, low-risk environment 

4. Evaluate and refine: Assess outcomes, gather feedback and improve 

5. Scale what works: Expand successful use cases with the right governance in place 

This approach allows organizations to build momentum while managing risk and complexity. 

How Baker Tilly can help 

Baker Tilly helps organizations move from AI exploration to real results—quickly and responsibly. 

• Identify high-value use cases: We align AI to your business priorities to focus on what delivers the most impact. 
• Enable your workforce: We build AI fluency so teams can use these tools effectively and confidently. 
• Implement and scale solutions: From pilots to enterprise rollout, we design and deploy AI that fits your environment. 
• Establish governance and security: We put the right guardrails in place to protect data while enabling innovation. 

Wherever you are in your AI journey, we help you take the next step with clarity and confidence.