Despite significant advances in generative artificial intelligence in recent years, adoption of the technology remains largely limited to the same large corporations that have historically led the way in implementing new technologies. But GenAI is evolving, and so is the company’s profile best suited to extracting value from it. Increasingly, it is mid-sized companies that possess the right balance of resources and agility to accelerate adoption, achieve meaningful results and reap the benefits of GenAI as the technology matures.
Overall, although such companies are still lagging behind, they could recover. A survey by Oxford Economics found that only a quarter of mid-sized businesses surveyed had adopted AI in 2023, but 51% planned to adopt AI in 2024; adopters expected it to improve their prospects, especially in new products and services (43%) and marketing and sales (48%).
Until recently, it was (very) large companies that benefited most from GenAI, as the benefits of scale outweighed the challenges of organizational complexity that come with size. However, as technology evolves, large companies are slow to adapt. Extensive management layers, entrenched processes and siled operations can slow the adoption of rapidly evolving technologies such as GenAI.
In large corporations, GenAI implementations can suffer from the “death of a thousand pilots,” where individual teams or functions develop proof-of-concept products and tools but fail to scale due to enterprise complexity and a lack of clear governance. As a result, large companies often struggle to realize the full potential of new tools despite heavy investments in digital transformation.
In contrast, medium-sized companies can benefit from simpler structures that enable faster decision-making and implementation, with appropriate leadership and management. Their agility, combined with the right strategy, allows them to adapt more quickly to new technology developments and more easily operationalize GenAI. (Mid-sized companies here refer to companies with revenues between $50 million and $1 billion, and while the precise definition will vary from country to country, it generally refers to companies that are still small enough to have relatively simple operations and remain agile.)
While the advantages of size and scale provide once decisive advantages in access to specialized talent and capital-intensive infrastructure, the evolution of GenAI as a technology—particularly the development of GenAI as a service, the emergence of modernized platforms, and the growth of adaptive models—is creating a more level playing field between medium and large companies.
GenAI service providers, for example, significantly reduce the need for upfront investment and extensive IT capabilities by offering models and infrastructure as a service. Simplified platform solutions like Google Vertex AI and Snowflake also simplify the AI ecosystem, providing integrated tools for data management, model adaptation and deployment, all of which reduce technical barriers and accelerate time to value.
Advances in adaptive models through technologies such as augmented retrieval generation (RAG), meanwhile, are enabling midsize companies to leverage their proprietary data effectively without an army of in-house data scientists. Much of the coding required to build traditional AI has been replaced by rapid natural language engineering to create GenAI-powered tools tailored to a company’s content, expertise and workflows.
In addition, updates to existing software platforms, including ERPs and CRMs, include AI features, providing easy access to AI functionality on the existing technology stack. Mid-sized companies are well-positioned to adopt them quickly, as they generally have less complex and less customized instances of the software, so integrating new releases is easier and faster than for larger companies.
Beyond adoption, medium-sized companies are well positioned create value from GenAI, as it could help them address the operational constraints that often hold them back. Mid-sized companies often struggle to attract specialized talent, such as data scientists, and lack the scale to make full-time employment economically viable. GenAI tools can extend the capabilities of existing staff, as demonstrated by a a recent BCG experiment where management consultants each was asked to perform three core data science tasks beyond their core consulting capabilities: data cleansing, predictive analytics, and statistical understanding.
Using GenAI to perform tasks immediately expanded the capabilities of consultants beyond their current capabilities. These augmented participants showed an improvement of 13 to 49 percentage points over those working without GenAI and came within 12 to 17 percentage points of the benchmark for data scientists. Function- or role-specific tools are now entering the market and allowing companies to further expand the capabilities of existing employees. Sisensefor example, it allows companies to build semantic data models without coding that users can then query using natural language queries, allowing managers to incorporate data-driven insights into their decision-making without the need for data analysts or data scientists.
Another limitation often found in smaller companies is the lack of sufficient proprietary data to create differentiation. A recent study by LBS, IoD and Evolution doo. found that only 56% of smaller companies with annual revenues of £10m to £50m said they believed proprietary knowledge was somewhat or extremely important to their business, compared to 72% of medium-sized companies with revenues of more than £50m. Large companies, on the other hand, are already using traditional AI to extract value from proprietary data, having invested in cleaning and managing data sets.
However, mid-sized companies often have a wealth of unstructured data—from which they have struggled to extract value. A medium-sized company, for example, might have manuals for customer service agents that describe product details and troubleshooting tips, along with transcripts of actual customer service calls. With GenAI, such a company can now unlock those insights without having to hire a team of data scientists, using the company’s data to make new connections and create and disseminate highly customized organizational knowledge in real time. The result is improved customer service at a reduced cost—something these companies previously didn’t have the resources, capabilities, or infrastructure to do.
Mid-sized companies backed by private equity firms have additional operational advantages—strategic alignment, financial and human capital, and focused implementation—that make them prime candidates for GenAI adoption. Clear objectives for PE firms and timelines for their portfolio companies, with a focus on value creation within specific investment horizons (typically five years), enable decisive action to prioritize and implement GenAI applications. PE-backed companies can also access the necessary financial and human capital for GenAI projects, giving these companies the capacity to make large investments in leadership and advisory teams in anticipation of growth. As a result, they are often more willing to take calculated risks based on the potential for high returns.
Mid-sized companies may now have some structural advantages for GenAI adoption compared to larger players, but this does not guarantee success. Here are five strategic steps you can take right now to increase your chances of successfully adopting GenAI along the path to value creation.
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Build a scalable and flexible GenAI stack: Invest in scalable AI-as-a-service platforms that can grow with the business without significant additional investment.
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Go to ‘reshape’ and ‘invent’: Go beyond deploying GenAI for incremental improvements to current processes and rethink your business model and how you can re-engineer entire functions. AND recent research by BCG found that companies at the forefront of AI adoption realize nearly two-thirds (62%) of the value they gain from introducing AI and GenAI into core business functions, while the remaining third (38%) comes from more peripheral support functions. The bottom line is clear: Go for deep apps that reengineer core functions and prioritize those that use unique, proprietary data to create moats.
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See what complements GenAI, not just technology: Like recently White Paper by Evolution Ltd suggests, a key reason for disillusionment with GenAI is an overemphasis on the technology itself with too little attention paid to what lies beneath upstream-data engineering and proprietary data—i downstream-integrating GenAI into strategic decision-making and creating loops of learning and experimentation.
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Establish clear governance and leadership: Success with GenAI requires a strong commitment from company leadership to implement governance structures that facilitate effective decision-making and prioritize medium-term investment, not just immediate returns.
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Improve workforce capabilities: Use GenAI to upskill employees, enabling them to perform tasks beyond their current abilities.
Mid-sized companies, once considered too small, could be “just right” to get the most out of today’s GenAI. However, to do this, they need a clear strategy and a firm focus on where GenAI can make a difference—not just cost reduction, but also revenue and value creation. Those who are able to stay laser-focused on effective implementation will realize that the AI revolution isn’t just for industry movers or nimble startups—it can be an inclusive wave for which mid-sized companies are ideal.
read else Wealth column by François Candelon.
François Candelon is a partner in private equity firm Seven2 and former global director of the BCG Henderson Institute.
Michael G. Jacobides is Sir Donald Gordon Professor of Entrepreneurship and Innovation at the London Business School, Academic Advisor at the BCG Henderson Institute and Lead Advisor Evolution doo.
Meenal Pore is the principal at Boston Consulting Group and ambassador at the BCG Henderson Institute.
Leonid Zhukov is the director of the BCG Global AI Institute and vice president of AI & Data Science at BCG.X.
Some of the companies mentioned in this column are former or current clients of the author’s employers.
This story was originally published on Fortune.com
