Why Mid-Market Companies Should Focus on Practical Solutions Before AI
It's easy for us to get caught in the hype surrounding the latest and greatest tech advancements. Currently, AI is at the forefront of this phenomenon (what Gartner calls "peak of inflated expectations"). While AI certainly holds transformative potential, companies must approach its adoption with a measured perspective, especially in the mid-market sector (think $100MM-$1B in revenues).
We've seen this play out with the Data Science phenomenon about a decade ago when companies invested a ton of time, money, and energy while seeking the holy grail of supply chain, marketing, pricing, and sales optimization - many times all at once. Data Science and Digital Transformation were heavily intertwined during this period, with roughly a 20% success rate across companies.
Similar to the Data Science hype, there's an increasing trend of seeking AI solutions even when the problem doesn't require a highly sophisticated approach ("solutions in search of problems"). This approach is further complicated by organizational readiness to deploy AI solutions effectively:
Disparate data/accounting systems
Data quality issues
Lack of relevant human capital
Ingrained sales practices
Take, for example, a hypothetical Private equity-owned $1B company that has grown through acquisitions over the past decade. It's not uncommon for a company like that to find itself juggling multiple accounting systems across subsidiaries, leading to tedious manual processes and financial (or customer) data consolidations that take days or weeks each month. Thousands of person-hours each year to get basic reporting done.
Before leaping into "AI," there's substantial value in addressing foundational tech and analytics needs, particularly commercial analytics.
Data automation (to eliminate manual data processes), standing up a purpose-built data warehouse, and implementing dynamic reporting and performance insights (with basic predictive analytics) are not just incremental steps for many companies. They are transformative changes that many mid-market companies can realistically achieve and significantly benefit from.
It's essential to recognize that making the leap to AI-driven or AI-assisted solutions is not always the immediate answer. For most B2B companies, this is akin to teaching calculus to a 3rd grader.
While there are specific narrow use cases where AI can be beneficial (e.g., content generation, outbound marketing automation, personalized outreach, chatbots, etc.), the majority of business challenges can and should be effectively tackled with simpler, more straightforward solutions.
In the coming decade, it's unlikely that this fundamental need for foundational technological and analytics improvements will diminish. As business leaders and thought leaders, we must steer our companies (or clients) through the fog of tech hype and focus on practical, impactful solutions that address current challenges while preparing those ready for the coming AI revolution.