Successful AI integration should start with identifying business needs

While nearly every company is investing in artificial intelligence, only 1% consider themselves mature in how they have integrated the technology into their operations, according to a 2025 McKinsey report. 

Business advisors say successful AI integration isn’t about moving faster but about building the right foundation before deploying the technology. 

“The first step isn’t selecting a tool or a technology. It’s truly identifying the problem you’re trying to solve,” said John Loury, president of Rochester, New York-based data analytics and AI consulting firm Cause + Effect Strategy. 

Loury said too many organizations begin with the technology instead of the desired outcome. Before evaluating AI platforms, leaders should identify where inefficiencies exist, determine which business decisions they want to improve, and define what success will look like. 

“The companies that are pulling ahead aren’t asking, ‘Where can we use AI?’ They’re asking, ‘Where can AI help us make better decisions and create measurable value?'” he said. 

He said businesses are seeing AI create immediate value by automating repetitive administrative work, accelerating customer service, improving forecasting and summarizing large volumes of information. Those efficiencies free employees to spend less time on manual tasks and more time applying judgment, creativity and relationship-building skills. 

Successful AI initiatives also depend on accurate, accessible data, Loury said. 

“AI is only as good as the information that it has available to it,” he said, noting that companies with fragmented or outdated data are unlikely to realize the technology’s full potential, regardless of which platform they choose. 

Loury said successful organizations should focus less on where AI can be used and more on where it can improve decision-making or eliminate inefficiencies. 

“We don’t need to do flashy things to be effective,” Loury said. “The real goal is people solving practical business problems.” 

Amanda Orson, managing partner of Pennsylvania-based AI advisory and implementation firm Atlas Works, said businesses should first understand how work is actually being done before

introducing AI into their operations. 

“Don’t buy a tool first,” Orson said. “Take stock of what you actually do, walk through your real processes and talk to people that are actually doing the work, day to day.” 

For many organizations, the best place to start is with discrete, repetitive workflows, Orson said, particularly in back-office operations where AI can deliver measurable returns with relatively low risk. Customer support, software development and quality assurance are among the areas where organizations are realizing immediate productivity gains. 

She also encourages organizations to evaluate the quality of their data before implementing AI. 

“Every single audit client we’ve done, businesses discover that they have their information scattered in different systems, different spreadsheets, different inboxes,” Orson said. Without accessible, reliable information, she said, AI cannot produce meaningful results. 

Once those foundational pieces are in place, Orson recommends choosing an initial AI project with a clear return on investment and limited risk. 

Rather than attempting to transform an entire organization at once, she encourages companies to start with a single back-office process, demonstrate measurable results and build employee confidence before expanding AI into other areas. 

Beyond understanding workflows and data, Orson said companies should resist the temptation to rush into AI because of growing marketplace pressure. Buying a tool before mapping the problem remains one of the most common mistakes she sees. 

Organizations also often underestimate the importance of testing, she said. AI systems should be continuously monitored and refined rather than treated as a one-time technology purchase. 

“It’s never set it and forget it,” Orson said. “You have to make sure that it’s still working as you intend.” 

Building a solid foundation and implementing AI thoughtfully are only part of the equation. Sharon Elise, founder of Maryland-based AI business strategy and operations consulting firm Sorvex, said long-term success depends on leadership establishing clear expectations for how AI will be used across the organization. 

“Business owners should set basic rules early,” Elise said. “Employees need to know what information can and cannot be put into AI tools, when human review is required, and who is responsible for final decisions.” 

Elise said one of the biggest mistakes companies make is allowing the technology to drive decisions instead of business needs. 

“A lot of companies start with, ‘We need to use AI,’ instead of asking, ‘What problem are we trying to solve?'” she said. “That usually leads to scattered tool use, unclear results and employees experimenting in ways leadership cannot see or manage.” 

According to Elise, organizations should also recognize that AI implementation extends well beyond technology. 

“It is also an operations issue, a training issue, a policy issue and a leadership issue,” she said, explaining that companies that skip governance and fail to establish expectations around privacy, accuracy and accountability create unnecessary risks, particularly in customer communications, hiring, finance and legal operations. 

For many businesses, AI adoption has already begun whether leaders recognize it or not, she said, as employees increasingly use AI tools independently. The challenge now is turning that informal use into a coordinated business capability supported by policies, training and oversight. 

“AI should help a business operate better, not just move faster,” Elise said. “That means leaders need to slow down long enough to understand the work, set the rules, train their people and decide where AI truly belongs.” 

Caurie Putnam is a freelance writer for BridgeTower Media.