How AI Can Lead to Operational Transformation in Smaller US Companies

Small and mid-market companies (SMBs) in the U.S., more so than any other type of company, face difficult challenges when it comes to adopting AI technologies. Although many SMB leaders view the concept of AI favorably, a lot of them struggle to build a strong digital foundation necessary for it to thrive. According to TechRepublic, SMBs often remain tied to outdated systems that prevent them from accessing accurate data.This lack of visibility restricts their ability to fully use AI for operational transformation. If SMBs can overcome these obstacles, it could unlock new efficiencies, improve decision-making and create long-term value. Technology partners can offer help in different aspects that help bridge this gap and reap the benefits of these new technologies.Let’s take a closer look at how AI can drive operational transformation for small and mid-market companies.

How to Lay the Groundwork for AI to be Integrated

Many SMBs have legacy systems that limit data transparency and hinder operational efficiency. According to the Woodard Report, 40% of CFOs do not trust their organization’s data accuracy, with a further 77% believing they should be leading business-wide operational transformation.

More than 76% of CFOs report that data inaccuracy and inconsistency are top pain points and 77% believe they should be leading business-wide operational transformation. However, without a solid data foundation, these goals are difficult to achieve. Poor data quality and complex manual processes often keep businesses stuck in a reactive mode.

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A digital innovation partner can offer data cleanup, product investigations and integration solutions that help identify the sources of poor quality, fix problems with systems integrations and even replace integration solutions with more modern, simpler approaches.

For example, Making Sense worked on a product initiative for a fintech loans company that helped the customer identify reasons why some of their data was inconsistent, allowing them to have informed, provable conversations with data providers and system vendors about the issues and how this impacts their business.

The reactive mode that companies can get stuck in is often exacerbated by layering new technologies on top of existing, outdated systems rather than implementing new solutions. Research shows that 41% of SMBs prefer an add-on approach, compared with 23% who seek single-purpose solutions with built-in AI and strong data infrastructure.

This impedes their ability to gather consistent insights, making it harder to automate processes or drive meaningful change. Digital transformation or modernization can replace old systems with newer ones, including simplifying workflows, upgrading technology and reducing operational costs.

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Democratizing Data and Being More Efficient with AI

AI can address the challenges of complex layering of technologies by improving data access and accuracy and creating better workflows. As is well established, it frees up employees from repetitive, manual tasks, allowing them to focus on higher-value work.  In the healthcare industry, we are seeing more and more automated entries of different-formatted forms thanks to optical character recognition (OCR) and generative AI LLMs to automate a workflow that requires lots of repetitive and tedious tasks.

Rather than relying solely on IT or data specialists, AI enables a more democratized approach to data use. Employees can interact with insights directly through user-friendly interfaces, enabling more strategic contributions across departments. This leads to greater transparency and operational cohesion so that businesses can make data-driven decisions that fuel growth.

One of our supply chain software clients worked with us to develop models and interfaces that simplified the management of huge complexities such as stocking inventory, logistics and a predictive approach to the supply-demand chain without requiring specialized training for users. This collaboration aided in modernizing the company’s legacy AS400 system with cloud-based solutions that better supported its evolving business needs. They were able to streamline operations through the use of a real-time web interface, reducing costs and driving growth beyond their long-standing relationship with their partner.

How AI Unlocks Innovation

AI-powered insights enable leaders to better understand the full spectrum of their workforce's skills and performance. For example, the executive vice president at a big healthcare company recently stated in an MIT publication: “To build a digital organization, you’ve got to take people’s amazing talents and create an 'and' strategy for technology. To be relevant and future-ready, you, for instance, need to have your commercial expertise and digital expertise.”

Within that digital expertise, AI can be used to retain talent and consider lots of metrics related to employee work experience. This could include projects they've worked on, the feedback they've received and areas for growth. This understanding not only improves workforce management but also enhances innovation by placing the right people in the right roles to drive transformation.

The Last Hurdle with AI Adoption Barriers

Finally, while AI clearly presents several great opportunities, SMBs need to jump through several hoops to fully integrate these technologies. Legacy systems and a lack of digital infrastructure are the most common barriers. To unlock AI's full potential, SMBs need to invest in modernizing their systems to create an environment that supports AI’s data requirements.

Leaders and decision-makers must now focus on how AI can supplement human capabilities, allowing employees to apply their critical thinking and creativity in other areas. After all, AI-driven tools are best used to complement human intuition, so that teams are empowered to make better decisions and innovate more effectively.