In brief

In brief

  • What do industries as diverse as Energy, Industrial Equipment and Automotive have in common? They are all capital intensive and struggle to scale AI.
  • Many companies achieve value in pockets. To realize AI's exponential value, leaders need to outline a clear strategy based on an integrated picture.
  • Leaders will prioritize AI investments based on relative value, complexity, time to market and potential synergies across company areas.
  • An AI control tower will further help maximize value through optimization of AI as it’s operationalized.

A billion dollars is on the table

Despite the financial potential, few companies in capital-intensive industries—such as Automotive, Industrial Equipment, and Oil & Gas—have conquered the full value of AI. Many companies in these industries are experimenting with AI and realizing pockets of value. But nearly three out of four C-suite executives (71%) acknowledge they struggle to scale it across their business.

Yet, scaling AI is key to success. A full 85% of executives in capital-intensive industries say they won’t achieve their growth objectives unless they scale AI. Getting it right requires an integrated approach for AI that closely involves the workforce. Getting it wrong leaves US$1 billion on the table.


of capital-intensive industry executives say they won’t achieve their growth objectives unless they scale AI.


acknowledge they struggle to scale AI across their business.

Demystifying AI value

Companies often begin with testing AI for solving the most complex business problem. However, AI is known to bring quick and large value to support functions.

An integrated approach is needed for AI that strikes the right balance between long-term business impact and short-term success. An approach also that takes into consideration that only a portion of AI value lies in automation—and much AI value resides in enhanced judgment, interaction and trust.

Companies that win with AI work from a deliberate, holistic AI strategy rather than disparate projects. And they laser focus on the workforce aspects of AI, knowing their people are essential to realizing AI’s full potential.

Rather than pursuing value in pockets, leaders will engineer value that builds upon a complete and integrated picture.

Zooming in on value

While the “size of the prize” is a compelling factor in prioritizing AI investments within a company, time to value and ease of capturing value are equally as important. Value potential is just one aspect of prioritizing AI use cases within a holistic strategy.

Conducting the overall value verification for AI requires a structured approach. Through an integrated view, leaders can work with their teams to identify which use cases are the most attractive and where implicit synergies arise. Using these synergies as a guide allows leaders to then map how they prioritize and operationalize use cases to amplify their return on investment.

Integrated value mapping allows companies to prioritize AI use cases.

Here is what this value mapping may look like for a typical Automotive company.

Integrated value mapping allows companies to prioritize AI use cases. Here is what this look like for a typical Automotive company.

Using AI for the “big picture” on distribution costs

A large Oil & Gas company’s transportation costs were rising at a rate that put a portion of its operations in the red. The organization was focused heavily on “first-mile” cost, rather than on final mile or total landed cost. In addition, an ERP implementation was making cost comparisons and analyzing trends difficult. To better manage its distribution across its products, company leaders decided to harness AI for better insights. Integrating data from multiple disparate sources, the company used AI analysis for information on well sights; trucking, rail and ocean lanes; warehouses; suppliers; and high-traffic stock-keeping units (SKUs). Using a focus on total landed cost, it was able to flag over US$30 million in savings.

Maintaining a bird’s-eye view

Setting up a clear strategy for AI across all functions is equivalent to creating an AI “control tower.” Such a control tower approach can help establish value measurement and management, involving a blend of business stakeholders, data science and IT professionals. This step is necessary, and our data shows it’s lacking at many companies. Only 45% of companies have deployed a sustainable AI program and only one in ten have started to systematically exploit the value of AI across different departments.

An AI control tower can help maximize value in many ways, from ensuring the right stakeholders are involved to delivery governance.

All eyes on the prize

To achieve desired growth, most companies will leverage increasingly sophisticated models that maximize the value they glean from their AI efforts. As they do so, analytics becomes a core part of their operating model as AI is democratized throughout all levels of the organization, driving business decisions.

Forward-thinking leaders keep a few actions top of mind:

1. Evaluate the value and complexity of use cases

Leaders manage the AI journey methodically, with a structured roadmap that gives a view of all possible use cases across the value chain.

2. Anticipate the impact of AI on the workforce

Leaders embed AI ownership and accountability into teams and ensure employees fully understand AI and how it relates to their roles.

3. Outline a clear, integrated strategy for AI

Leaders build an AI value office to assess and track opportunities for top-line and bottom-line impact of AI use cases on both business and corporate functions.

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Joey Lanius

Managing Director–Accenture Strategy, Supply Chain & Operations Strategy

Manas Satapathy​


Cedric Vatier

Senior Managing Director – Accenture Strategy, Industrial & Travel, France and Benelux


Bensalem Albaroudi

Analyst – Accenture Strategy

Ranjana Combs

Senior Manager – Accenture Strategy, Industrial, Mobility & Travel

Hélène Lassaux

Manager – Accenture Strategy

Lisa Nait

Consultant – Accenture Strategy


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