April 28, 2026

This piece is sponsored by Eide Bailly LLP.

By Brad Mendel, principal

A version of this article originally appeared on eidebailly.com.

Artificial intelligence quickly has become part of everyday business. Yet for all the attention AI receives, many organizations still approach it as a technology decision rather than a business one.

Organizations adopt AI before aligning it to their goals, preparing their teams or establishing the controls needed to manage risk responsibly. It’s no wonder then that 57 percent of C-suite leaders feel their company is not fully prepared for AI adoption and only 36 percent say they have scaled generative AI solutions.

The real question isn’t whether to use AI. It’s how to use it in a way that delivers real business value.

Business problems haven’t changed

AI doesn’t change the core problems businesses are trying to solve. Rather, AI provides new ways to approach familiar challenges. It lowers the barrier to entry for advanced analytics, accelerates workflows and helps teams extract more value from the data they already have.

But the outcomes businesses care about — performance, protection and growth — remain the same.

You still need to:

• Make better, faster decisions.
• Reduce manual work and inefficiency.
• Manage risk and maintain compliance.
• Scale operations and grow revenue.

What AI changes is how effectively those problems can be addressed.

Practical AI is about outcomes, not tools

No shortage exists of new AI tools entering the market, but adopting technology for its own sake rarely leads to meaningful results.

The most successful organizations take a different approach. They start by identifying specific, achievable use cases tied to business outcomes. Only then can you determine where AI can help deliver measurable ROI:

• Automating the right tasks.
• Improving accuracy and speed.
• Freeing people to focus on higher-value work.

What to do next

For leaders, the next step with AI is not selecting a tool or launching a pilot. It’s gaining clarity.

Before investing further, organizations should be able to answer a small set of foundational questions:

• Where is AI already influencing decisions, workflows or risk?
• Which outcomes matter most right now: efficiency, risk reduction or growth?
• What constraints exist around data quality, governance and change readiness?
• Which capabilities must mature together for AI to scale responsibly?

Organizations that take the time to establish this clarity move forward with confidence. Those that don’t often accumulate disconnected pilots, underused tools and unmanaged risk.

Construction: Productivity, margin protection and risk visibility 

Construction leaders face persistent challenges around labor shortages, project delays, cost overruns and risk exposure. AI increasingly is embedded in project management, scheduling and financial systems, but value depends on how intentionally it’s applied.

Practically, AI can help construction firms:

• Reduce manual administrative work tied to job costing, billing and reporting.
• Improve forecasting accuracy by analyzing historical project data.
• Identify risk earlier through automated monitoring of budgets, timelines and compliance requirements.

Healthcare: Efficiency without compromising trust or compliance 

Healthcare organizations operate under intense regulatory scrutiny while facing ongoing pressure to do more with less. Often, AI is introduced to improve efficiency, reduce administrative burden and enhance decision-making — but the stakes are uniquely high. Nearly half of hospital executives have implemented AI, but many feel unprepared for the changes these tools require.

When applied thoughtfully, AI can:

• Streamline revenue cycle and back-office processes.
• Improve data accessibility for faster, more informed decisions.
• Reduce manual errors and repetitive work.

Manufacturing: Scaling efficiency and insight across the operation 

Manufacturers have long relied on automation and data to drive efficiency. AI builds on that foundation by:

• Enhancing demand forecasting and inventory planning.
• Improving operational visibility across systems and locations.
• Supporting faster analysis of performance, quality and cost drivers.

Why the right partner matters

Organizations that succeed with AI understand not just what they want to achieve, but also where they are today and what capabilities must mature together to move forward responsibly.

For one manufacturing client, quoting was slow, manual and error prone, creating friction for sales teams and limiting scale. By rethinking the process and embedding AI into existing workflows, the organization reduced manual inputs, improved accuracy and created a more scalable quoting model.

At Eide Bailly, we understand AI touches every part of the organization, so success depends on integrating technology with business processes — not treating it as a stand-alone initiative. Let us help you harness AI responsibly, aligning technology with strategy, data integrity and human oversight.