artificial intelligence<\/a>, only 1% consider themselves mature in how they have integrated the technology into their operations, according to a 2025 McKinsey report.\n<\/p>\nBusiness advisors say successful AI integration isn\u2019t about moving faster but about building the right foundation before deploying the technology.<\/p>\n
\u201cThe first step isn\u2019t selecting a tool or a technology. It\u2019s truly identifying the problem you\u2019re trying to solve,\u201d said John Loury, president of Rochester, New York-based data analytics and AI consulting firm Cause + Effect Strategy.\n<\/p>\n
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.\n<\/p>\n
\u201cThe companies that are pulling ahead aren\u2019t asking, \u2018Where can we use AI?\u2019 They\u2019re asking, \u2018Where can AI help us make better decisions and create measurable value?’\u201d he said.<\/p>\n
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.\n<\/p>\n
Successful AI initiatives also depend on accurate, accessible data, Loury said.\n<\/p>\n
\u201cAI is only as good as the information that it has available to it,\u201d he said, noting that companies with fragmented or outdated data are unlikely to realize the technology\u2019s full potential, regardless of which platform they choose.\n<\/p>\n
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.\n<\/p>\n
\u201cWe don\u2019t need to do flashy things to be effective,\u201d Loury said. \u201cThe real goal is people solving practical business problems.\u201d\n<\/p>\n
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.\n<\/p>\n
\u201cDon\u2019t buy a tool first,\u201d Orson said. \u201cTake stock of what you actually do, walk through your real processes and talk to people that are actually doing the work, day to day.\u201d\n<\/p>\n
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.\n<\/p>\n
She also encourages organizations to evaluate the quality of their data before implementing AI.\n<\/p>\n
\u201cEvery single audit client we\u2019ve done, businesses discover that they have their information scattered in different systems, different spreadsheets, different inboxes,\u201d Orson said. Without accessible, reliable information, she said, AI cannot produce meaningful results.\n<\/p>\n
Once those foundational pieces are in place, Orson recommends choosing an initial AI project with a clear return on investment and limited risk.\n<\/p>\n
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.\n<\/p>\n
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.\n<\/p>\n
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.\n<\/p>\n
\u201cIt\u2019s never set it and forget it,\u201d Orson said. \u201cYou have to make sure that it\u2019s still working as you intend.\u201d\n<\/p>\n
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.\n<\/p>\n
\u201cBusiness owners should set basic rules early,\u201d Elise said. \u201cEmployees 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.\u201d\n<\/p>\n
Elise said one of the biggest mistakes companies make is allowing the technology to drive decisions instead of business needs<\/a>.\n<\/p>\n\u201cA lot of companies start with, \u2018We need to use AI,\u2019 instead of asking, \u2018What problem are we trying to solve?’\u201d she said. \u201cThat usually leads to scattered tool use, unclear results and employees experimenting in ways leadership cannot see or manage.\u201d\n<\/p>\n
According to Elise, organizations should also recognize that AI implementation extends well beyond technology.\n<\/p>\n
\u201cIt is also an operations issue, a training issue, a policy issue and a leadership issue,\u201d 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.\n<\/p>\n
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.\n<\/p>\n
\u201cAI should help a business operate better, not just move faster,\u201d Elise said. \u201cThat means leaders need to slow down long enough to understand the work, set the rules, train their people and decide where AI truly belongs.\u201d\n<\/p>\n
Caurie Putnam is a freelance writer for BridgeTower Media.<\/p>\n","protected":false},"excerpt":{"rendered":"The Blueprint: Only 1% of companies mature in AI integration per McKinsey report John Loury of Cause +…\n","protected":false},"author":2,"featured_media":126515,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[24,3639,25,63357,63358,5328,685,63359],"class_list":["post-126514","post","type-post","status-publish","format-standard","has-post-thumbnail","category-ai","tag-ai","tag-ai-integration","tag-artificial-intelligence","tag-atlas-works","tag-business-needs","tag-decision-making","tag-leadership","tag-sorvex"],"_links":{"self":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/126514","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/comments?post=126514"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/126514\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media\/126515"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media?parent=126514"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/categories?post=126514"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/tags?post=126514"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}