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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.
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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.
Frequently asked questions about practical solutions before AI
What does focusing on practical solutions before AI mean?
It means fixing foundational technology and commercial analytics needs before adopting AI: automating manual data processes, standing up a purpose-built data warehouse, and implementing dynamic reporting and performance insights with basic predictive analytics. For many mid-market companies, roughly $100MM to $1B in revenue, these steps are transformative changes they can realistically achieve.
Is AI the right first step for mid-market companies?
Usually not. AI holds transformative potential, but it currently sits at what Gartner calls the peak of inflated expectations, and companies should adopt it with a measured perspective. For most B2B companies, jumping straight to AI-driven or AI-assisted solutions is akin to teaching calculus to a third grader.
What lessons does the data science hype offer for AI?
About a decade ago, companies poured time, money, and energy into data science, often chasing supply chain, marketing, pricing, and sales optimization all at once, and data science and digital transformation efforts had roughly a 20% success rate. AI shows a similar trend: companies seek highly sophisticated solutions for problems that do not need them, creating solutions in search of problems.
What stops companies from deploying AI effectively?
Organizational readiness. Common obstacles are disparate data and accounting systems, data quality issues, a lack of relevant human capital, and ingrained sales practices. A hypothetical $1B private equity-owned company that grew through acquisitions, for example, may juggle several accounting systems, so data consolidations take days or weeks each month and basic reporting takes thousands of person-hours a year.
Where can AI help mid-market companies today?
In specific, narrow use cases such as content generation, outbound marketing automation, personalized outreach, and chatbots. Most business challenges, however, can and should be tackled with simpler, more straightforward solutions, and the need for foundational technology and analytics improvements is unlikely to diminish in the coming decade.
How should business leaders respond to AI hype?
Steer companies and clients through the fog of tech hype instead of chasing the latest and greatest advancements. Focus on practical, impactful solutions that address today’s challenges, and prepare the organizations that are ready for the coming AI revolution.
Related Reading
- Tariff Shockwaves & Margin Erosion: Why Mid-Market Industrial Firms Need Revenue Management as a Service Now
- Dynamic Pricing: Balancing Profit and Customer Satisfaction
For broader industry perspective on pricing analytics and revenue growth management, see McKinsey’s Growth, Marketing & Sales insights.
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