Middle Market Firms Face Unique AI Challenges

Middle-market companies are pouring billions into artificial intelligence, but challenges abound.

U.S. middle market companies (so named because their revenues fall into the middle of their industry ranges) haven’t earned much buzz on the artificial intelligence front.

But that scenario is changing fast for middle market firms, which account for 30% of all U.S. gross domestic product and earn between $10 million and $1 billion annually.

Exhibit “A” is a new survey from Chicago-based RSM US LLP, a middle market assurance, tax, and consulting services provider.

The report notes that “most middle market organizations” are adopting AI (78%) and generative AI (77%). However, 54% of those using generative AI find its deployment more challenging than anticipated. Meanwhile, “top Gen AI goals” include better quality control (58%), more robust customer service (51%), automation of repetitive tasks (45%), and higher employee productivity and creativity (45%).

There’s more to the report for middle management executives, especially on the “priorities” front. This from the study.

• 85% completely agree/somewhat agree that generative AI has impacted their organization more positively than expected.
• 77% report using generative AI solutions such as ChatGPT (82%), Microsoft Copilot (30%) and Adobe Firefly (24%).
• 74% have a budget dedicated to generative AI investments and 89% plan to increase their budget next fiscal year.
• 41% report being in the partial implementation phase for AI and 34% among those using generative AI.

Five Big Problems

RSM also lists the specific challenges that middle market companies are experiencing with their Gen AI Implementations. Companies just starting out can expect to deal with some or all of the following rollout issues.

Data quality. “Organizations must prioritize data quality management, ensuring accuracy and completeness through processes such as data cleansing and enrichment, supplemented by external data when required,” the study noted.

Skills gap: A lack of overall AI expertise within an organization can hinder the adoption process. “Consequently, investing in training is crucial to empower employees, accelerate implementation, and drive business innovation,” the report stated.

Integration with existing systems: “Incorporating generative AI solutions into existing processes and systems, like CRM or ERP, can pose complexity and requires detailed planning to guarantee seamless integration with the current infrastructure,” RSM reported.

Ethical and legal considerations: “Organizations must tackle AI-related issues by creating standards to ensure system transparency and accountability, and perform routine audits to prevent ethical, legal, regulatory or compliance risks,” the firm added.

Cost: The investment in hardware, software and personnel can be notable for the middle market. “(That’s why organizations need to consider the benefits, such as increased efficiency, improved decision making, and cost savings, against the expenses involved in its adoption,” RSM stated.

Going External

AI implementation challenges are so deep that middle-market companies may need to hire outside Gen AI expertise to streamline budgets and boost operational efficiencies.

“AI and generative AI are making significant impacts on our industry — perhaps more than any previous technology,” said Sergio de le Fe, enterprise digital leader and partner with RSM US LLP. “Our survey underscores the necessity for middle market organizations to develop a comprehensive AI strategy that encompasses the entire value chain.”

“Considering the complexity of AI technologies, it’s no surprise that roughly two-thirds (67%) of middle market leaders surveyed recognize the need for external assistance to fully capitalize on the advantages of their selected AI solutions,” he added.

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