AI and digital

Drive AI and digital transformation in energy, chemicals, and industrials

AI and digital technologies are reshaping asset-intensive industries by improving how decisions are made, operations are executed, and value is created. From upstream oil & gas to refining, chemicals, power, and industrial manufacturing, companies are investing in data, analytics, and AI to improve performance and position for long-term competitiveness.

However, adoption remains uneven. Many organizations have invested in digital tools and AI capabilities but struggle to translate these into measurable business outcomes, scale beyond pilot programs, or align technology with real operational needs.

ADI Analytics helps operators, software and digital solution providers, and investors define strategies, prioritize opportunities, and translate AI and digital capabilities into tangible value across oil & gas, energy, chemicals, and industrial markets.

Digital and AI are converging to reshape industrial workflows

Digital technologies have created the foundation for transformation by enabling data capture, connectivity, and visibility across industrial systems. AI is now accelerating this shift by improving prediction, optimization, and decision support across these same workflows.

In oil & gas, energy, and chemicals, this convergence is reshaping:

• Operations and maintenance, where condition monitoring, predictive maintenance, and performance optimization improve uptime and reduce costs
• Planning and supply chain, where better forecasting and scenario analysis improve inventory, scheduling, and logistics decisions
• Commercial and trading functions, where data-driven pricing, customer targeting, and contract optimization enhance margins
• Technical functions, including subsurface analysis, process optimization, and product development

The result is a shift from data visibility (digital) to decision advantage (AI-enabled digital).

Value is concentrated in core industrial workflows

Across oil & gas, chemicals, and industrials, the largest value pools from AI and digital are tied to improving core workflows rather than deploying standalone tools. Examples include:

• Optimizing production and throughput in upstream, refining, and chemical plants
• Reducing unplanned downtime and improving asset reliability
• Enhancing supply chain planning and inventory management
• Improving commercial decisions such as pricing, contract strategy, and customer targeting

These applications deliver measurable gains in cost, margins, and capital efficiency. Realizing this value depends on selecting the right use cases and aligning them with business priorities.

Scaling from pilots to enterprise adoption remains the key challenge

Despite the potential, many companies face common challenges:

• Fragmented digital and AI initiatives across functions
• Unclear prioritization of use cases
• Weak linkage to P&L impact
• Difficulty scaling beyond pilots
• Misalignment between technology providers and operator needs

Leading companies are moving toward business-led transformation, where AI and digital initiatives are anchored in high-impact workflows, supported by clear governance, and scaled systematically across the organization.

Industry context shapes AI and digital strategy

AI and digital adoption is highly dependent on sector-specific realities:

• Oil & gas and energy: large, complex asset bases, volatile markets, and high capital intensity require better forecasting, optimization, and asset-level decision making
• Chemicals and process industries: margin pressure, complexity, and innovation cycles drive demand for improved planning, yield optimization, and product development efficiency
• Industrials and manufacturing: equipment reliability, customer requirements, and supply chain complexity drive adoption of data-driven operations and service models

These factors require domain-specific strategies grounded in real workflows, data availability, and operating constraints.

How ADI helps

ADI Analytics supports clients across the full AI and digital landscape with integrated advisory services that combine industry expertise, market insight, and execution-focused strategy:

Strategy, use cases, and value realization

AI and digital strategy & roadmap development
Define enterprise strategy, priority value pools, and phased roadmaps aligned with business objectives, operational constraints, and capital priorities.

Use case identification, screening & prioritization
Identify and prioritize high-impact opportunities across operations, maintenance, supply chain, commercial, and technical functions based on value, feasibility, and scalability.

Workflow redesign & value realization strategy
Assess how AI and digital capabilities reshape end-to-end workflows and define changes needed to improve decision making, efficiency, and performance.

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Market intelligence, commercialization & growth

Market sizing, segmentation & opportunity assessment
Evaluate market potential for AI-enabled and digital solutions across oil & gas, energy, chemicals, and industrial segments.

Customer insights & use case validation
Conduct voice-of-customer research, application mapping, and segmentation to identify real operator needs and high-value problems to solve.

Go-to-market strategy & offering positioning
Support software and digital solution providers in refining value propositions, targeting the right customers, and improving product-market fit.

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Competitive positioning & ecosystem strategy

Competitive benchmarking & capability assessment
Benchmark companies on AI and digital maturity, capabilities, and performance relative to peers and best-in-class operators.

Vendor landscape & partner evaluation
Map AI and digital solution providers, assess capabilities, and identify best-fit partners based on use case alignment and industry needs.

Ecosystem and partnership strategy
Define how companies should position within the broader AI and digital ecosystem, including partnerships across technology, data, and service providers.

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Adoption, scaling & operating model

AI and digital maturity assessment
Evaluate current capabilities across data, workflows, organization, and use case deployment, and define gaps relative to industry benchmarks.

Operating model, governance & best practices
Develop governance frameworks, decision rights, and operating models to support scalable and consistent adoption.

Scaling strategy & change enablement
Support transition from pilots to enterprise-wide adoption through structured rollout strategies, capability building, and alignment across functions.

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Investment and transaction support

Commercial due diligence for AI and digital assets
Assess technology providers, platforms, and digital business models for investors and corporate development teams.

Growth strategy & portfolio prioritization
Identify attractive investment areas across the AI and digital value chain and support portfolio strategy decisions.