AI in EPC – how to generate value

By Daniel Rohrhirsch, Sven Siepen, Stefan Krieger, Peter Odenwälder and Christian Vinck

Many EPC companies are struggling to achieve value from their AI projects, let alone scale them. Others are even further behind and still grappling with the data requirements. All face the same challenge – turning intent into enterprise-wide impact and value. In this article, we look at the factors that distinguish AI leaders from the pack, and offer use cases, a roadmap and other tools to set any EPC company on the path to successful AI adoption.

Working at the cutting edge of energy, chemicals, mining, infrastructure and heavy industrials, EPC (engineering, procurement & construction) companies have often adopted new technologies where these clearly help to de-risk delivery, improve productivity or meet client requirements. At the same time, the industry as a whole has typically been more cautious and selective in scaling new technologies, given thin margins, complex contractual obligations and unpredictable project dynamics. However, the emergence of artificial intelligence has left many EPC companies uncertain how to proceed.

AI is the first technology since digital scheduling and building information modeling (BIM) to materially shift EPC productivity, predictability and commercial outcomes. It’s a big deal, and companies are already exploring the possibilities.

But most have hit a wall: Some are yet to commit to AI, held back by the belief that their data is not good enough to begin; others have launched pilots but find themselves frustrated by fragmented results, low adoption and an inability to scale what worked in isolation. Both groups share a common challenge – the absence of a coherent, enterprise-wide approach that links AI technology investments, organizational capabilities and commercial outcomes.

This article offers solutions for both groups. It argues that already available data – no matter how imperfect or unstructured – is sufficient to begin, and that those who have already begun should move beyond pilots to industrialization.

To support this, we outline five tried-and-tested AI use cases in EPC that consistently deliver scalable, repeatable value and tangible productivity gains – that is, real impact. We also present a roadmap and key considerations for the successful rollout of AI at scale, as well as our exclusive AI benchmarking tool.

Why EPC companies are struggling to scale AI

So where are EPC companies going wrong with AI? In many cases, AI initiatives are treated primarily as IT experiments rather than a change to how engineers, planners, commercial managers, and site teams actually work. As a result, productivity gains remain local, adoption is inconsistent and economic impact is difficult to sustain.

Companies also often overlook the fact that AI fundamentally changes skill requirements and ways of working in an organization. Effective adoption requires capabilities such as prompt‑driven engineering interaction, AI‑supported quality assurance and human‑in‑the‑loop decision making – it’s not just a case of deploying tools and tweaking governance structures.

Our survey of R&D and engineering decision-makers bears out these findings. It showed that:

• 65% of respondents are uncertain about their AI capabilities and resources for transformation
• 84% lack a dedicated R&D or engineering AI strategy
• 56% are willing to adopt AI but are not yet ready
• 48% have not implemented any AI in their core engineering processes.

In terms of challenges respondents faced, the most frequently cited obstacles included a lack of AI ownership and strategy, resistance to change, insufficient AI expertise and cybersecurity concerns. These are all organizational challenges, not technology barriers, reinforcing why implementation approach matters more than tool selection.

This also underlines why successful AI adoption cannot be left to fragmented initiatives across individual functions or departments. While experimentation at the functional level is important, scaling AI requires clear direction from top management: where AI should create value, which use cases matter most, what level of standardization is required and how adoption will be embedded in the company’s operating model.

In short, the challenge of turning AI potential into actual impact is more organizational than technological. It lies in employee skill sets, leadership expectations, willingness to trust AI-supported outputs and the ability to embed AI into daily routines and processes, from standard operating procedures to quality gates and performance expectations. Whether an organization is taking its first steps in AI or attempting to scale it, successful adoption therefore goes beyond a technology rollout toward a workforce and operating model transformation.

Where to start: Roland Berger’s AI Maturity Assessment tool

Before deciding where to act – or what to do next – EPC leadership teams need a clear and unbiased view of their organization's current AI position. This transparency is a prerequisite for defining the structured execution roadmap outlined below. Without it, organizations risk either misjudging their ability to absorb change and overinvesting, or dispersing effort across initiatives without meaningful impact.

Roland Berger's 360-Degree AI Maturity Assessment is designed to support this step. Tailored to complex, project-based industries, it evaluates readiness across five dimensions — and is equally relevant whether an organization is taking its first steps or attempting to scale from an early base. The assessment provides the following core insights:

1. Maturity baseline: An objective view of where the organization stands relative to EPC peers
2. Priority use case map: Identification of two or three AI applications most likely to generate the fastest return given the current data and organizational state
3. 90-day action plan: Concrete, executable next steps calibrated to the organization's starting point.

For organizations already running pilots, the assessment frequently reveals an additional insight: which existing initiatives deliver value when scaled, and which are consuming resources without a credible path to enterprise value. Establishing these facts allows leadership teams to make deliberate choices on where to focus their efforts.

Value pools: The best target areas for AI applications in EPC

While numerous AI applications are technically feasible in EPC, some are more value-generating than others. Our review across EPC industries – energy, chemicals, heavy industrials, mining and infrastructure – showed that the largest and most reliable value pools for AI are concentrated in certain areas along the EPC lifecycle. These share common characteristics, comprising:

• Work that is document-heavy, highly repetitive and risk-sensitive (proposals, engineering checks, contracts)
• Processes with high variance and poor visibility (site progress, schedule risk)
• Areas where poor data leads to high commercial exposure (claims, notices, entitlement)

Focusing on the characteristics above consistently narrows the field to five use cases that can be industrialized across projects and geographies:

1. Proposal automation & bid intelligence;
2. design co-pilot and engineering QA automation;
3. contract intelligence and claims management;
4. computer vision for progress tracking and quality control; and
5. Schedule risk AI (SRA+) & predictive project controls

These use cases capture the largest and most reliable value pools across the EPC lifecycle. They are scalable and repeatable, require limited operational disruption and reinforce compliance.

Sign up now to access the full study. You will also receive regular news and updates, delivered straight to your inbox.