What’s missing from your company’s A.I. transformation is a transformer.

When the Commonwealth Bank of Australia (CBA) needed help developing artificial intelligence technology to improve its banking operations, ranging from cyber-threat detection to cash optimisation, it auditioned AI startup H20.ai and its open-source machine learning platform. After starting as an experimental pilot partnership, the bank, Australia’s largest, empowered the startup to customise and scale its tech across the bank’s business. But H20.ai didn’t only create tech solutions tailored for the bank, it provided holistic support, scaling talent—training more than 1,000 bank workers—and driving change management with a team of experts dedicated to the bank.

Incumbents like CBA are increasingly looking to AI technology to solve their business problems and are eyeing external tech partners to source those AI solutions. However, these traditional companies have faced challenges nurturing meaningful collaborations that maximise the support they get from AI players. Only 1 in 5 incumbents found the right kind of AI player, like H20.ai is for CBA, that offers access to custom technology, as well as support for talent, training, and change management, prompting the incumbent to overhaul its processes. We call these AI players that provide such support transformers.

For industry incumbents who can identify and effectively collaborate with a transformer, the value is clear. When the BCG Henderson Institute surveyed 600 leading companies, we found that those incumbents that successfully fostered meaningful collaborations on customised AI solutions were three times as likely to derive a high (positive) financial impact from AI as those that did not.

Incumbents currently adopting AI should aim to find their transformers to maximise their chances of deriving value from the technology. However, there are numerous barriers to a meaningful incumbent-transformer partnership. To overcome these hurdles, incumbents need to recognise and change preconceived notions and ingrained behaviours that are holding them back.

What type of support should incumbents seek?

Incumbents should target AI players that help them eliminate the three key barriers that usually inhibit them from adopting AI: technology, talent, and change management.

• Technology: Bridging legacy gaps to customised AI three-quarters of incumbents we surveyed said they were challenged by a lack of tools necessary to build their own AI solutions. When they looked externally for partners, 80% still had compatibility issues with their legacy IT systems. Transformers, in many instances, specialise in a particular vertical or function, allowing them to bridge an incumbent’s technology gaps by developing an AI solution tailored to the incumbent’s needs. Mature off-the-shelf products may be able to be more quickly adopted, but they might not fully solve the business problem, and when the technology becomes standardised, adjacent support on talent and change management also tends to fade.

• Talent: Overcoming the AI skills deficit. Incumbents, almost universally, report facing challenges in sourcing tech talents (83%) and providing the necessary AI training (85%) to their current employees. Transformers eliminate this talent deficit because they work on cutting-edge AI, which attracts top talent. Because transformer firms already specialise in the incumbents’ industry, these workers already speak the same language as the adopting incumbent workforce, facilitating the upskilling of the organisation’s non-AI workers.

• Change management: Reinventing ways of working. The complexity of upending existing processes with new tech was a challenge for 83% of incumbents, and 76% of incumbents were challenged by their employees’ lack of trust or understanding of AI technology. Transformers can act as a change agent, smoothing an incumbent’s transition by charting AI-specific strategies, reinventing existing processes to harness the value of AI, establishing AI governance to prevent risks from materialising, safeguarding responsible practices, and establishing trust for users and consumers. Meaningful transformer engagement can also help fight cultural resistance, by shifting employee mindsets from viewing AI as a threat to seeing it as an opportunity and supporting the redefinition of job descriptions with AI, as well as establishing trust in AI insights—all of which help the AI tools put down deep roots inside organisations.

  Yet the journey to finding—and engaging—with transformers can include pitfalls.

  Navigating the AI marketplace itself is a fundamental obstacle for all incumbents because AI falls outside incumbents’ experience and expertise; the navigation process slows down 83% of incumbents in their AI adoption journey. To find the right partner, incumbents need to devise a clear AI partnership strategy, something our survey found only a third of incumbents currently had. Additional challenges materialise depending on an incumbent’s experience with AI adoption, and at each stage, incumbents need to change organisational behaviours to overcome new hurdles to collaboration. 

At the beginning of your AI transformation:

When incumbents are at the early stages of their AI transformation (i.e., they have not yet adopted or are adopting AI in some processes), they have an intrinsic apprehension about working with AI startups or scale-ups, with half of the incumbents surveyed saying such apprehension hindered collaboration. Incumbents also demonstrate a preference for mature AI products rather than experimentation, as 43% of early-stage incumbents cite a lack of product readiness as a roadblock to engaging with AI partners.

To foster meaningful collaborations, incumbents need to change their organisational behaviour in two ways. They must see transformers as allies rather than adversaries and prioritise tailored solutions over mature products.

• From fearing competition to collaborating. Incumbents must radically shift their mindset recasting transformers as strategic allies instead of adversaries. “We viewed tech companies as owners of product solutions, not as true collaborators,” an executive at a European car manufacturer said of the company’s evolution from change-resistant to embracing A.I. collaboration. “We changed and decided to set up partnerships. The tech company invested a lot into the partnership—headcounts, training, discounts, and a lot of manpower. After two years, it became a win-win partnership. We used it not only for product consumption but also for building up a product.”

• From firewalls to open doors. This shift in mindset frees incumbents to be transparent with their data and their industry intelligence, which, in turn, enables transformers to better customise AI technology. When leading Norwegian drilling equipment and service provider MHWirth, now HMH, needed to conduct data-driven maintenance on its offshore drilling rigs, the incumbent gave its AI partner Cognite full access to its data via API key and free rein to deploy its solution. This approach helped HMH keep costs in check, extend the lifespan of equipment, and decrease downtime via customised predictive models.

• From ‘ready-made products only’ to welcomed experimentation. Instead of purchasing off-the-shelf solutions, incumbents need to embrace experimentation, particularly in industries or use cases where easily adopted mature products do not exist or don’t create a competitive advantage. Incumbents must instead place their bets on the transformers to provide customisation, which takes time and requires process—and cultural—changes. There weren’t any mature AI products, for instance, that met Brazilian aircraft manufacturer Embraer’s needs for autonomous flight. So, the incumbent sought a specialised player to develop new products, like electric vertical landing and take-off aircraft that require technologies incorporating visual traffic detection and camera navigation. That led the company to Daedalean, an AI startup that possessed a wealth of knowledge and experience in autonomous flight.

• From traditional pricing models to novel distribution of value. Incumbents also need to understand that experimentation and innovation might confound traditional pricing models. With a bespoke AI solution that creates a potentially novel distribution of value, incumbents will have to work closely with transformers to establish coherent pricing that takes into account the true value generated by AI tech company Bluecore did this by setting up a new monetisation model when pitching its Multi-Channel Marketing Platform analysing consumer behaviour and personalising retail marketing. The company established a pricing model based on success instead of volume—in this case, in the form of customer conversion rate or repeat purchases. That prompted incumbent retailers Foot Locker, Sephora, and Tommy Hilfiger to partner with Bluecore, embracing innovative pricing that rewarded experimentation.

In the advanced stages of A.I. transformation: 

When incumbents are in more advanced stages of their transformation (e.g., they are beginning to deploy AI at scale), new behavioural requirements emerge that are necessary for successfully building meaningful collaborations. At this stage, incumbents will need to push beyond product scaling and embrace the organisational reinvention. In the late stages of adoption, one-third of incumbents still expressed concerns about the pricing of AI products, underscoring the pervasive incumbent concern over how value is distributed in the partnership.

• From product scaling to organisational change. Incumbents at advanced stages of AI adoption need to shift their attention to structuring collaborations to accompany the scaling of AI. That shift means involving transformers in identifying and prioritising use cases to diffuse across the business. It also requires setting up an appropriate IT infrastructure to deploy these use cases— all as part of defining AI strategy at an organisational level. When Shell, for example, enlisted C3.ai to set up predictive maintenance programs for 10,000 pieces of its gas equipment, the oil incumbent empowered the startup not just to scale its insights across its business units, but explore additional use cases in production optimisation, system optimisation, and safety, as well as to expand into new business units such as Shell’s renewables vertical. The partnership demonstrates how incumbents can embrace a new way of thinking in their collaboration with transformers.

• From legacy distribution of value to continuous redefinition. When deploying AI at scale with a transformer, the data, and knowledge required increases—as do the potential incumbent benefits generated by AI this process can create data asymmetries and uneven financial benefits across AI use cases that can spark conflict in the partnership. To avoid this, incumbents should redefine, with their transformer partners, new ways of sharing and monetising the value generated by AI at scale. An incumbent and its AI partners can create a new value pool by commercialising the solution originally developed for internal use, as was the case with the collaboration between C3.ai and Shell (i.e., OA.I.).

For industry incumbents to get more out of AI, they need to change their mindset and their behaviours to allow for meaningful collaboration with transformers.Incumbents need to find their transformer to maximise the AI transformation through customised AI support—leaning into experimentation, changing organisational mindsets, overcoming cultural resistance, and opening to the uncertainty and potential of creating tailored solutions. The hurdles, as we show, are surmountable and the payoff in value is clear.

Read the study: What’s Missing from Your AI Transformation Is a Transformer | BCG

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