Small Wins: The Key to AI Success for Midmarket Firms

Techaisle’s recent survey of over 2100 businesses shows that 53% of midmarket firms have shifted their focus to smaller AI wins as they result in reduced risk, faster ROI, enable flexibility, build trust and capability, and target specific immediate pain points. These early wins can serve as a springboard for more significant, more ambitious AI initiatives, ultimately driving long-term growth and success. This trend was first highlighted on July 31st during a podcast recording, where I was asked about the specific AI trends Techaisle and I were watching. My response was clear: small wins. This insight was grounded in our data-driven research, and the evidence presented in this article further supports this conclusion.

Pursuing smaller, more manageable AI projects is increasingly becoming the preferred strategy for midmarket firms. This shift is primarily driven by a series of significant roadblocks hindering the widespread adoption of AI.

A staggering 82% of midmarket companies cite cost and a lack of sufficient investment as primary obstacles. The substantial financial commitment often required for large-scale AI initiatives burdens these organizations considerably. Additionally, 63% of midmarket firms grapple with insufficient technology infrastructure, highlighting the need for robust IT systems to support AI applications.

Uncertainty also plays a significant role. 59% of midmarket companies express a lack of clarity on AI implementation, underscoring the complexity and challenges associated with integrating AI into existing business operations. Furthermore, trust and security concerns, cited by 51% of respondents, pose substantial barriers to AI adoption. The sensitive nature of data and the potential risks associated with AI systems have led to a cautious approach among many organizations. Finally, data quality and accessibility remain critical challenges. 38% of midmarket firms struggle with a lack of curated data and the inability to ingest quality data, hindering AI model development and performance. These collective challenges have compelled midmarket organizations to adopt a more pragmatic approach to AI. By focusing on smaller, more attainable projects, these firms can mitigate risks, accelerate time-to-value, and build momentum while addressing the limitations imposed by these roadblocks.

Techaisle data shows that while the preference for small wins is consistent, there are notable differences in the intensity of this preference across vertical industries.

Different industries prioritize small AI wins over large investments at varying rates, with Personal Services (75%) and Information & Professional Services (65%) leading. Manufacturing (51%) and Healthcare (47%) show moderate prioritization, indicating a balanced approach. Resources (36%) and Banking/Financial Services (42%) are less focused on small AI wins due to different strategic needs.

However, one thing is clear: almost all midmarket firms have prioritized AI adoption, and 78% say that not doing so may lead to a significant opportunity cost. These firms believe that AI adoption will lead to 10% of cost savings to the business over the next five years.

The Techaisle survey data also shows that IT (55%), Sales and marketing (44%), and Employee productivity (38%) are the top three functional areas using Active AI pilots. This suggests that companies are focusing on using AI to improve efficiency in core business functions like IT operations and sales, as well as employee productivity. Overall, Active AI adoption appears to be scattered across different departments, with no single department dominating the use of AI pilots. This suggests that companies are still experimenting with AI in different areas to find the most effective use cases.

Organizations can test the waters without committing substantial resources by investing in bite-sized AI initiatives. This cautious approach allows for experimentation and learning without fear of catastrophic losses. Furthermore, these projects boast faster time-to-market, enabling businesses to gauge the potential of AI quickly and make informed decisions about future investments.

The beauty of small AI projects lies in their ability to deliver quick wins. These early successes are potent catalysts, generating momentum to propel organizations forward. Businesses can more easily secure additional funding for more significant AI initiatives by demonstrating tangible results. Moreover, these initial triumphs foster a culture of innovation and experimentation, encouraging employees to embrace new technologies and explore their potential.

In the ever-evolving landscape of AI, flexibility is critical. Small AI projects are inherently more agile than their larger counterparts. This nimbleness allows organizations to adapt swiftly to technological changes, market dynamics, and customer needs. By embracing a modular approach, businesses can easily modify or pivot their AI strategies without disrupting their core operations.

Early successes in AI are instrumental in establishing trust and credibility with customers, partners, and stakeholders. By delivering tangible value through small-scale projects, organizations can demonstrate their AI expertise and commitment to customer satisfaction. This reputation for innovation and reliability can be a powerful asset when competing for business and attracting top talent.

For 44% of firms, deploying AI solutions for improving customer experience is a priority. Small AI projects often target specific pain points that directly impact customers. By addressing these challenges with innovative solutions, businesses can deliver immediate value and enhance customer satisfaction. This focus on customer-centric outcomes fosters stronger relationships and builds loyalty.

Successful small AI projects can have ripple effects far beyond the initial investment. By demonstrating AI's value, organizations can unlock new revenue streams and explore opportunities for strategic partnerships.

The shift towards incremental AI adoption is a strategic approach that empowers midmarket firms to achieve significant results. By prioritizing small wins, organizations can unlock AI's potential while mitigating risks and maximizing return on investment.