Despite generative AI’s remarkable advances in recent times, adoption of the technology stays largely confined to the identical large corporations which have historically led the way in which in deploying emerging technologies. But GenAI is evolving and so, too, is the corporate profile best suited to extract value from it. Increasingly, it’s mid-sized corporations that possess the appropriate balance of resources and agility to speed up adoption, drive meaningful outcomes, and reap the advantages of GenAI because the technology matures.
On the entire, while such firms are still behind, they might be poised to rebound. Research by Oxford Economics found that only 1 / 4 of mid-sized corporations surveyed had adopted AI in 2023 but 51% were planning to adopt AI in 2024; the adopters were expecting it to enhance their outlook, specifically in latest services and products (43%) and marketing and sales (48%).
Until recently, it was (very) large corporations that benefited most from GenAI, because the benefits of scale outweighed the challenges of organizational complexity that accompany size. Yet as technology evolves, large firms find themselves slow to regulate. Extensive layers of management, entrenched processes, and siloed operations can decelerate the adoption of fast-evolving technologies like GenAI.
In large corporations, GenAI implementations can suffer from “death by a thousand pilots,” wherein individual teams or functions develop proof-of-concept products and tools yet don’t manage to scale them as a result of the enterprise complexity and lack of clear governance. In consequence, large corporations often struggle to totally realize the potential of latest tools despite extensive investment in digital transformation efforts.
Mid-sized firms, against this, can profit from leaner structures that allow for quicker decision-making and implementation, given the appropriate leadership and governance. Their agility, when combined with the appropriate strategy, enables them to adapt more quickly to latest developments within the technology and more easily operationalize GenAI. (Mid-sized firms here refers to corporations with revenues between $50 million to $1 billion, and although the precise definition will vary from country to country, this broadly refers to corporations which are still sufficiently small to have relatively easy operations and remain agile.)
While the advantages of size and scale provide once-decisive benefits in access to specialized talent and capital-intensive infrastructure, the evolution of GenAI as a technology—particularly the event of GenAI as a service, the emergence of streamlined platforms, and growth of customizable models—is making a more level playing field between mid-sized and huge firms.
GenAI providers, as an example, are significantly reducing the necessity for up-front investment and extensive IT capabilities, by offering models and infrastructure as a service. Streamlined platform solutions like Google Vertex AI and Snowflake also simplify the AI ecosystem, providing integrated tools for data management, model customization and deployment, all of which lower technical barriers and speed up time-to-value.
The advance of customizable models through technologies like retrieval-augmented generation (RAG), meanwhile, allows mid-sized firms to leverage their proprietary data effectively without a military of in-house data scientists. Much of the coding needed to construct traditional AI has been replaced with natural language prompt engineering to create GenAI-powered tools tailored to the corporate’s content, expertise, and workflows.
As well as, updates to existing software platforms including ERPs and CRMs are incorporating AI features, giving easy accessibility to AI functionality on the present tech stack. Mid-sized corporations are well placed to adopt these rapidly, given they typically have less complex and fewer customized instances of software, so integrating latest releases is easier and faster than for larger corporations.
Beyond adoption, mid-sized corporations are well positioned to create value from GenAI, as it could help them tackle the operational constraints that always hold them back. Mid-sized firms often struggle to draw specialized talent, similar to data scientists, and should not have the dimensions to make it economically viable to rent a full-time position. GenAI tools can expand the capabilities of existing staff, as demonstrated by a recent BCG experiment where management consultants were each asked to finish three basic data-science tasks outside their core consulting capabilities: data cleansing, predictive analytics, and statistical understanding.
Using GenAI to perform the tasks immediately expanded the consultants’ aptitude beyond their current abilities. These augmented participants showed a 13- to 49-percentage-point improvement over those working without GenAI and got here inside 12 to 17 percentage points of the benchmark for data scientists. Function- or role-specific tools at the moment are entering the market and enabling corporations to further expand the capabilities of existing employees. Sisense, for instance, enables corporations to construct semantic data models without coding that users can then query through natural language queries, enabling managers to include data-driven insights into their decision making without the necessity for data analysts or data scientists.
One other constraint often found at smaller corporations is a scarcity of sufficient proprietary data to create differentiation. The recent study by LBS, IoD and Evolution Ltd. found just 56% of smaller firms with annual revenues of £10 million to £50 million stated they consider that proprietary knowledge is somewhat or extremely vital to their business, compared with 72% of mid-sized corporations with revenues over £50 million. Large corporations, alternatively, are already using traditional AI to extract value from proprietary data, having invested in cleansing and curating datasets.
Mid-sized firms, nevertheless, often have a wealth of unstructured data—from which they’ve struggled to extract value. A mid-sized company, for instance, could have handbooks for customer support agents outlining product details and troubleshooting suggestions, together with transcripts of real customer support calls. With GenAI, such a firm could now unlock those insights while not having to rent a team of knowledge scientists, using company data to make latest connections, and creating and disseminating highly tailored organizational knowledge in real time. The result’s improved customer support at a reduced cost—something that these corporations would previously not have had the resources, capabilities or infrastructure to do.
Mid-sized corporations backed by private equity firms have additional operational strengths—strategic alignment, financial and human capital, and focused implementation—that make them prime candidates for GenAI adoption. PE firms’ clear objectives and timelines for his or her portfolio corporations, specializing in value creation inside specific investment horizons (normally five years), enable decisive motion to prioritize and implement GenAI applications. Firms backed by PE may access the essential financial and human capital for GenAI projects, giving these corporations the capability to take a position heavily in leadership and advisory teams in anticipation of growth. In consequence, they are sometimes more willing to take calculated risks based on potential for prime returns.
Mid-sized corporations may now have some structural benefits for GenAI adoption in comparison with larger players, but that doesn’t guarantee success. Listed below are five strategic steps they’ll take right away to extend their possibilities of successful GenAI adoption on the road to value creation.
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Construct a scalable and versatile GenAI stack: Put money into scalable AI-as-a-service platforms that may grow with the corporate without significant additional investment.
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Move to ‘reshape’ and ‘invent’: Move beyond deploying GenAI for incremental improvements to current processes, and rethink your small business model and the way you’ll be able to reengineer entire functions. A recent BCG survey found that the businesses on the forefront of AI adoption derive nearly two-thirds (62%) of the worth they get deploying AI and GenAI in core business functions, with the remaining third (38%) coming from more peripheral support functions. The takeaway is obvious: Go for deep applications that reengineer core functions and prioritize those who leverage unique, proprietary data to create a moat.
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Have a look at what complements GenAI, not only the technology: As a recent Evolution Ltd white paper suggests, a key reason for disappointment with GenAI is an overemphasis on the technology itself with too little attention paid to what lies upstream—data engineering and proprietary data—and downstream—integrating GenAI into strategic decision-making and creating learning and experimentation loops.
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Establish clear governance and leadership: Success with GenAI requires a robust commitment from an organization’s leadership to implement governance structures that facilitate efficient decision-making and prioritize investment for the mid-term, not only immediate returns.
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Enhance workforce capabilities: Use GenAI to enhance worker skills, enabling them to perform tasks beyond their current capabilities.
Mid-scale corporations, once considered too small, could also be “good” to make essentially the most out of today’s GenAI. To accomplish that, nevertheless, they need a transparent strategy and a good concentrate on where GenAI could make a difference—not only reducing costs, but generating revenue and value. Those which are in a position to stay laser-focused on effective implementation will find the AI revolution will not be only for the industry incumbents or nimble startups—it could be an inclusive wave that mid-sized corporations are ideally suited to ride.
Read other Fortune columns by François Candelon.
François Candelon is a partner at private equity firm Seven2 and the previous global director of the BCG Henderson Institute.
Michael G. Jacobides is the Sir Donald Gordon Professor of Entrepreneurship and Innovation at London Business School, academic advisor on the BCG Henderson Institute, and the lead advisor of Evolution Ltd.
Meenal Pore is a principal on the Boston Consulting Group and an envoy on the BCG Henderson Institute.
Leonid Zhukov is the director of the BCG Global A.I. Institute and vice chairman of AI & Data Science at BCG.X.
A number of the corporations mentioned on this column are past or present clients of the authors’ employers.
This story was originally featured on Fortune.com