AI In the Middle Market
At this year’s SXSW Conference in Austin, a recurring theme echoed across panels and keynotes: by 2030 there will be two types of companies - those that have embraced artificial intelligence (AI) and those that have not. AI is no longer a futuristic concept; on the contrary, it’s actively reshaping the landscape of financial operations across industries through automation, predictive analytics, fraud detection, and enhanced decision-making capabilities. For finance leaders, the message is clear – adapt or fall behind.
AI has been transforming how teams operate. With current tools, finance can now process and analyze massive volumes of data in real-time, identify patterns that human analysts may overlook, and respond swiftly to dynamic market conditions. The result is more accurate forecasting, streamlined reporting, improved compliance tracking, and sharper risk management. Just as importantly, by automating repetitive, rule-based tasks such as invoice processing, reconciliation, and expense reporting, AI frees up valuable time for finance roles to focus on high-impact, strategic initiatives.
Turning Point’s own investment in AI over the last 18 months has validated a key insight that companies resisting this evolution are missing out on significant efficiency gains, cost savings, and strategic advantages. Organizations clinging to manual processes and outdated workflows will find themselves hampered by slower decision-making, increased risk of error, and a loss of competitive positioning. In today’s environment, where speed, accuracy, and foresight are critical, failure to embrace AI is no longer just a missed opportunity; it’s a liability.
Despite its potential, AI adoption is not without challenges, especially for middle market companies. A key limitation is data quality and availability. AI systems rely heavily on clean, structured, and consistently formatted data to generate dependable insights. Public companies benefit from EDGARized data that is easy for AI tools to access and analyze continuously. Middle market businesses, by contrast, often rely on fragmented systems and outdated data practices, making it difficult to feed reliable information into AI models. Without a solid data foundation, the output from AI tools may be inaccurate or irrelevant, which can further complicate adoption.
Additionally, businesses in the middle market tend to operate with tighter budgets, leaner teams, and limited access to advanced IT infrastructure compared to their enterprise-level counterparts. As a result, implementing AI tools can be seen as too costly or too complex. Many AI solutions on the market are designed with large enterprises in mind, featuring steep implementation requirements and customization needs that can overwhelm smaller finance teams. At Turning Point, we approached this incrementally, starting with ChatGPT and building small agents to support daily workflows. Having quickly outgrown this capability, we continued to iterate and today have built a robust agent that houses up to a million documents using multiple systems. Its power has exceeded our expectations, proving that strategic scaling is possible for ourselves and our clients.
Through the development of our tool, we have been open and transparent with our staff, training them on the technology and providing specific use cases to help them understand the value. Without this, the fear and misconception that AI will replace human jobs or reduce the need for their expertise can foster resistance within the organization. The truth is that AI should be seen as a tool for augmentation, not replacement. It enables finance professionals to do their jobs better, faster, and more strategically. Building a culture that is comfortable with AI means that CEOs and organizational leaders must communicate consistently and educate faithfully. This is the best path to company-wide adoption.
The “black box” nature of AI can also hinder adoption, making it feel like something people can’t trust. In finance, a field defined by accountability, auditability, and regulatory compliance, a lack of transparency around how decisions or recommendations are generated tends to fuel skepticism. Until AI systems become more transparent and align with financial governance standards, some degree of concern is likely to remain. But the trajectory is clear: explainability will improve, and emerging tools will give finance teams better visibility into how outputs are derived.
Despite these challenges to embrace AI, the future of it in finance, particularly for the middle market, is incredibly promising. As technologies mature, we are likely to see a wave of innovation tailored specifically to the needs of mid-sized businesses. Cloud-based platforms with built-in AI, pre-configured use cases, and simplified integrations will lower the barrier to entry and allow more companies to gain the benefits of AI without massive upfront investments.
By 2030, AI will likely be a fundamental part of all financial operations, just as spreadsheets and accounting software are today. Tasks that currently require human oversight will become fully automated, and finance professionals will transition into more analytical and strategic roles. Real-time financial modeling, instant scenario planning, continuous auditing, and proactive fraud prevention will become routine capabilities, giving businesses unprecedented agility and resilience.
The companies that start investing in AI now – experimenting, learning from early use cases, building internal expertise, and laying the groundwork for data readiness - will be best positioned to thrive in the years ahead. They will outpace competitors in speed, accuracy, innovation, and strategic foresight. Ultimately, the choice is stark: embrace the AI revolution or risk being left behind.
