What’s Missing from Your AI Transformation Is a Transformer

By François CandelonRémi Lanne, and Clément Dumas
Article 3 MIN read
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Are incumbent companies getting the most out of the AI revolution? Not yet. In a recent analysis, we found that the AI adoption experience among incumbents has been uneven. Now more than ever, organizations are looking to AI technology to solve their unique business challenges and are seeking external tech partners to source AI solutions. But it is no simple matter for incumbents to go beyond off-the-shelf products and foster meaningful collaborations that create tailored solutions to maximize what AI players have to offer.

To better understand incumbents’ AI adoption experiences, BCG and the BCG Henderson Institute surveyed 600 business leaders at the heart of decision making on technology adoption across six markets: China, France, Germany, India, the UK, and the US. The survey spanned 20 industries, excluding the tech sector, where familiarity with AI among incumbents is already high.

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The Benefits of Collaboration

Meaningful collaborations can help incumbents resolve key issues that slow their AI adoption:

What Are Transformers?

Transformers are AI players, typically AI startups and scale-ups, that collaborate—rather than compete—with incumbents to eliminate the above barriers. They offer holistic AI support such as access to custom technology, suitable talent, training, and change management, and they prompt the incumbent to overhaul its processes.

Forging collaborations isn’t easy for incumbents. Navigating an unfamiliar AI marketplace poses challenges to new entrants—and once incumbents have selected an AI partner, they must overcome numerous roadblocks, depending on their AI maturity level.

In the early stages of their AI transformation, incumbents tend to be apprehensive about working with AI startups or scale-ups. Although that uneasiness typically fades as they gain AI experience, other obstacles become more significant. In particular, more experienced incumbents often develop a preference for mature AI products over experimentation and tailored solutions. And when incumbents reach the more advanced stages of their transformation and begin to deploy AI at scale , they face new issues related to the pricing of AI products and the distribution of value in the partnership.

To overcome these challenges, incumbents must recognize and change preconceived notions and ingrained behaviors. In part, this effort will require shifts in mindset. Incumbents may initially view AI startups and their methods with fear, mistrust, and resistance, but they need to embrace collaboration and experimentation. Incumbents must also negotiate several key tradeoffs in areas such as proprietary data, IP ownership, and partnerships.

The Case for Finding Your Transformer

Our study of top incumbent decision makers reveals the enormous benefits that incumbents can reap when they collaborate with transformers to unlock the power of AI technology. Yet fostering such collaborations require sustained attention to emerging challenges. Although these change as incumbents advance through each stage of adoption, the hurdles they pose are surmountable—and the payoff in value is clear.

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The BCG Henderson Institute is Boston Consulting Group’s strategy think tank, dedicated to exploring and developing valuable new insights from business, technology, and science by embracing the powerful technology of ideas. The Institute engages leaders in provocative discussion and experimentation to expand the boundaries of business theory and practice and to translate innovative ideas from within and beyond business. For more ideas and inspiration from the Institute, please visit our website and follow us on LinkedIn and X (formerly Twitter).

Authors

Alumnus

François Candelon

Alumnus

Project Leader; BCG Henderson Institute Ambassador

Rémi Lanne

Project Leader; BCG Henderson Institute Ambassador
Paris

External Consultant; BCG Henderson Institute Ambassador

Clément Dumas

External Consultant; BCG Henderson Institute Ambassador
Paris

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