Before evaluating AI software, business leaders should identify the operational problem they want to solve, establish measurable goals, and create governance that supports responsible AI adoption.
Businesses have never had more options when it comes to artificial intelligence (AI).
Almost daily, there’s another product announcement, software update, or webinar promising to change how organizations work. Established business applications are introducing AI capabilities, while new AI platforms, assistants, and industry-specific solutions continue to enter the market. For business leaders, it’s an exciting time, but it can also be an overwhelming one.
With so many options available, it’s easy to believe the biggest challenge is choosing the right technology.
In reality, the organizations seeing meaningful results from AI are making a different set of decisions. They’re focusing less on which platform to buy and more on where AI can create measurable value, how it fits into existing business processes, and what needs to be in place before implementation begins.
That challenge isn’t unique. McKinsey’s 2025 State of AI survey found that while AI adoption continues to grow, many organizations are still focused on moving beyond experimentation to generate measurable business value. The technology is advancing quickly, but realizing meaningful outcomes depends on how organizations approach implementation.
What Is the Biggest Challenge Businesses Face When Implementing AI?
The biggest challenge in AI implementation isn’t choosing the right tool. Organizations can achieve better results when they first define the business problem they want to solve, establish measurable success metrics, create governance, and introduce AI through focused pilot projects before scaling across the business.
Organizations that achieve meaningful business value from AI tend to approach implementation differently. Rather than focusing first on technology, they begin by asking a series of practical business questions that guide strategy, measurement, governance, and adoption. The following five questions provide a practical framework for getting started.
1. Why Should Businesses Define the Problem Before Choosing AI Software?
The explosion of AI solutions has shifted many conversations in the wrong direction.
Leadership teams are often comparing products before they’ve identified the operational challenge they’re trying to solve. Vendor demonstrations are compelling, feature lists are impressive, and every platform appears capable of improving productivity. Without a clearly defined business objective, however, every solution begins to look like the right solution.
Instead of asking, Which AI platform should we choose?, organizations should begin by asking, What business problem are we trying to solve?
Is the goal to reduce the time required to onboard new clients? Improve the month-end close? Streamline proposal development? Eliminate repetitive administrative work? Improve response times?
When the business problem is clearly defined, evaluating technology becomes much easier. The conversation shifts from comparing features to determining which solution best supports the organization’s objectives.
Technology should support business strategy, not define it.
2. How Should Businesses Measure AI Success?
Organizations naturally want to measure AI initiatives. The challenge is deciding what should be measured.
Usage statistics, software licenses, prompts submitted, or the number of employees using AI tools can provide useful information, but they don’t answer the most important question: Is the business performing better because of AI?
One of the most common goals we hear is, “We want to be more efficient.” While that’s a reasonable objective, efficiency itself isn’t a measurement. To understand whether AI is making a difference, organizations need to identify the business metrics they expect to improve and establish a baseline before implementation begins.
Depending on the process, meaningful measures might include:
- Time required to onboard a client
- Month-end close timelines
- Proposal turnaround time
- Customer response times
- Hours spent on repetitive administrative work
- Client satisfaction or service metrics
Without understanding where you started, it’s difficult to determine whether AI is creating real value or simply introducing another technology into the organization.
For finance leaders, this approach should feel familiar. Every major investment requires measurable objectives and AI should be no different.
3. What’s the Difference Between AI and Traditional Automation?
Automation isn’t new. Most businesses already rely on technology to automate routine tasks. QuickBooks automatically categorizes transactions, HubSpot routes leads and triggers follow-up emails, payroll platforms process recurring processes, and countless business applications move information from one step to the next without manual intervention.
These systems are incredibly valuable, but they generally follow predefined rules. If one event occurs, another action follows.
AI expands what’s possible because it introduces capabilities that traditional automation cannot easily provide. Rather than simply following rules, AI can interpret unstructured information, understand context, generate content, summarize conversations, identify patterns, and make recommendations. It allows organizations to automate work that previously required human judgment.
That doesn’t mean AI replaces people. It means people spend less time completing routine work and more time reviewing, refining, and deciding with better information.
Understanding this distinction helps organizations identify opportunities where AI can complement existing automation rather than simply duplicate it.
4. When Should Organizations Establish AI Governance?
If your employees are already experimenting with AI, you’re not behind. You’re probably in the same position as many organizations today.
Employees are exploring new tools because they’re looking for ways to work more efficiently. That’s a positive sign. The challenge for leadership is creating an environment where innovation can continue without introducing unnecessary risk.
Governance is often viewed as something that slows adoption, but the opposite is true. It establishes clear expectations around approved tools, responsible data handling, privacy, human oversight, and acceptable use. Rather than restricting AI, governance creates the structure that allows organizations to scale its use responsibly.
Organizations don’t need to have every answer before employees begin experimenting, but they do need a plan for guiding that experimentation as adoption grows.
5. Should You Start AI With a Pilot Project?
The phrase “AI transformation” can create unrealistic expectations.
Organizations sometimes assume they need a company-wide AI strategy before they can begin realizing value.
In practice, the most successful initiatives start smaller: one workflow, one department, one measurable business problem, one pilot project, one opportunity to learn.
Early successes build confidence, reveal opportunities for improvement, and create practical experience that can inform broader implementation efforts. They also help leadership teams understand where AI creates value before expanding into more complex initiatives.
Like any meaningful business initiative, successful AI adoption is rarely the result of one large decision. It’s the outcome of many thoughtful decisions made over time.
The Pattern Behind Every Successful AI Rollout
The pace of AI innovation isn’t slowing down. New tools, features, and capabilities will continue to emerge, and organizations will continue to face difficult decisions about where to invest their time and resources.
The organizations creating lasting value won’t necessarily be the ones adopting every new platform first. They’ll be the ones who define the business problem clearly, measure results, put governance in place early, and build confidence one successful implementation at a time.”
Organizations that treat AI as a business capability, not just another software purchase, tend to see the difference show up over time, in how work gets done, how decisions get made, and how the operation holds up as it grows.
Where Does Your Organization Go From Here?
Every organization will approach AI differently. Adopting every new tool matters less than understanding where AI can create real value for your business.
If you’re ready to take the next step, Duffy Kruspodin can help you evaluate opportunities, establish practical governance, and develop a roadmap that fits your organization.
Learn more about our AI Advisory Services.
Sources Referenced
- IBM Institute for Business Value
https://www.ibm.com/thought-leadership/institute-business-value/en-us/report/2026-ceo - McKinsey State of AI
https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai - Thomson Reuters Future of Professionals Report
https://www.thomsonreuters.com/content/dam/ewp-m/documents/thomsonreuters/en/pdf/reports/future-of-professionals-report-2025.pdf - Microsoft Work Trend Index
https://www.microsoft.com/en-us/worklab/work-trend-index
