SIDENEWS NO. 44 JUNE: Ms. El Mane Wong – Process Industries Digital Consultant, ABB

Digital transformation has been a priority for industry for years. From your perspective, where does the sector stand today and what are the main advances that have taken hold in recent years?

In our experience, three profiles coexist in industry today. A small group, around 10-15%, still views digitalization as a “hype”, maintains a wait-and-see stance and makes minimal investment in this regard. The majority, roughly 70-75%, have launched multiple pilots across different departments, but struggle to scale them. These initiatives tend to remain isolated technology silos, disconnected from each other and from the broader business strategy.

A third group, the leaders, has moved beyond pilots into plant-wide operational programs that generate measurable, repeatable value. What sets them apart is not access to superior technology, but organizational clarity: a shared vision tied to business outcomes, disciplined program governance, and a genuine investment in building internal capabilities and workforce readiness.

The advances that have taken hold and proven scalable are concentrated in three areas. First, advanced process control, which optimizes production parameters in real time. Second, energy monitoring and optimization, enabling companies to identify inefficiencies and reduce energy costs. Third, predictive maintenance, which uses process data and machine learning to anticipate equipment failures before they occur.

Technology efficiency has become a priority for economic, environmental and competitiveness reasons. Which technologies or practices do you consider fully consolidated, and which are beginning to generate real impact in industrial plants?

Advanced process control is one of the most consolidated technologies. For example, furnace optimization solution adjusts temperature setpoints and furnace pace in real time, calculating the thermal profile of each piece individually, and can be easily integrated with existing control systems without major hardware modifications. Deployed at several of the world’s leading producers, its results are proven and documented.

The next pillar combines process control with comprehensive energy demand forecasting, residual gas monitoring, and electricity procurement planning. These integrated capabilities improve both operational performance and cost structure, while enabling real-time visibility into sustainability tracking and thus reducing waste and optimizing resource use across the entire operation.

In parallel, predictive maintenance is maturing rapidly and beginning to generate real impact. Solutions like ABB Ability™ APM, recognized as a Leader in the 2025 Gartner Magic Quadrant for Global Industrial IoT Platforms, helps organizations to shift from reactive models to predictive and prescriptive approaches. The efficiency gains are measurable: higher asset availability, reduced unplanned downtime, and significantly lower maintenance costs.

In a context marked by rising energy costs and productivity pressure, what real impact are automation, advanced digitalization and AI having on the reduction of operational costs?

In most well planned and executed digitalization efforts, the impact is real and measurable. However, they are not always uniform and depend heavily on each organization’s starting point and its ability to incorporate the technology into daily operations.

In advanced process control, supervisory systems such as furnace optimization consistently demonstrate energy consumption reductions and productivity gains of up to 20%, with direct impact on cost per ton produced. For industries operating on thin margins, this translates to meaningful competitive advantage.

On the sustainability front, energy and resource demand forecasting is helping organizations improve electricity procurement accuracy by more than 10%, which is a significant lever in today’s volatile energy pricing market. Beyond cost, by-product waste management solutions help keep the plant’s carbon footprint under control, addressing both regulatory requirements and investor pressure.

Predictive maintenance is where the most dramatic cost impact occurs. The shift from reactive to predictive models generates tangible returns. Plant-wide APM solutions deliver more than 30% reduction in unplanned downtime and more than 15% reduction in total maintenance costs. In real deployments monitoring thousands of assets and processes, predictive alerts have prevented production losses valued at hundreds of thousands of euros.

The organizations achieving sustained returns share one common pattern: a structured data platform that integrates automation, data analytics, and process expertise into a coherent vision, rather than isolated technology investments.

Although digitalization is advancing ever faster, some companies still struggle to implement new technologies or adapt their internal processes. From your experience, what are the main barriers that still exist?

The barriers are not primarily technological, instead they are organizational and structural. Most plants operate with siloed data, heterogeneous systems and an unreliable data foundation that hampers any digital initiative from the outset. Nearly 70% of companies cite data quality as their main barrier to AI and analytics adoption.

Added to this is a lack of trust in results. If the operator or technician does not understand the recommendations generated by digital model, they will not act on it, and therefore the investment value disappears regardless of technology quality.

The third and most persistent barrier is scaling. The jump from pilot to plant-wide deployment remains the hardest step. Fragmented pilots create technology silos that are difficult to integrate, preventing organizations from realizing the full potential of their investments. What most companies lack are not better tools, but a clear strategic vision and the internal capabilities to execute it consistently.

This is where ABB’s approach differs. ABB focuses not just on project execution, but on long-term support, guiding customers along their digitalization roadmap and building confidence in the solutions deployed. We bring together process engineering expertise, data science capabilities, and a robust technology platform which enables seamless integration of existing data source and maximum value extraction. Rather than isolated technology bets, ABB helps customers build a structured, unified data foundation that scales from pilot to enterprise-wide deployment.

The sector is experiencing constant change driven by automation, digitalization and sustainability. What is ABB doing to adapt and support its clients?

At its foundation, ABB continuously evolves its portfolio on a consolidated industrial automation base by ensuring stability while innovation accelerates.

ABB Ability™ digital platform integrates heterogeneous data sources, advanced analytics, and AI/ML capabilities across the full operational value chain, addressing the data fragmentation that remains a primary barrier for most organizations.

Beyond the platform itself, ABB is embedding cutting-edge technology directly into industrial workflows. Through strategic alliances with Microsoft, ABB is integrating generative AI into daily operations. The embedded Copilot system particularly stands out as it enables any worker, from field technician to boardroom executive, to perform data analytics in natural language, without requiring data science expertise, and receive real-time, actionable recommendations. This democratizes advanced analytics and accelerates decision-making at all levels.

On client support, ABB takes a tailored, structured approach. Rather than proposing one-size-fits-all solutions, ABB assesses each organization’s digital maturity, installed base, and strategic priorities, then proposes a customized roadmap, from data consolidation through advanced analytics to operational execution. As a result, customers do not have to manage this evolution alone. ABB integrates new capabilities progressively into existing platforms, ensuring smooth transitions without operational disruption.

Our philosophy is to support clients as strategic partners through sustained, guided transformation, and not just as vendors delivering isolated tools.

Technological innovation is often cited as the engine of industrial transformation, but the greatest challenges are not always technological. What are the human, cultural and organizational challenges ABB considers most important?

From our point of view, three challenges stand above the rest.

We find resistance to change as the most persistent factor. Deploying a new digital tool is straightforward; changing how people work, making decisions and trusting data-driven recommendations is far more complex.

The skills gap is structural. Digital transformation requires profiles combining process knowledge with digital competency, which is scarce in both the market and within organizations.

The third is leadership commitment, which is ultimately the deciding factor. Without genuine buy-in from senior management, digital initiatives remain fragmented projects that never reach scale, regardless of their technical merit.

What sets ABB apart is our deep domain knowledge built over decades in industry. We speak the same language as the operational team because we understand their workflows, constraints, and priorities because we’ve worked within their environments. This shared language bridges the gap between technology and trust, accelerates adoption, and makes training and change management far more effective.

Rather than imposing solutions from outside, ABB works alongside different teams on the plant floor while maintaining sight of the broader digital vision required at the organizational level. We connect the realities of daily operations with the strategic objectives of management, translating between both worlds.

Generational change poses a double challenge for industry: preserving accumulated knowledge and incorporating new digital competencies. How is ABB addressing this challenge?

It is an undeniable truth that attracting young talent to our industrial environments is increasingly difficult, and so is preserving the accumulated knowledge of retiring professionals.

ABB Ability™ digital platform addresses both dimensions simultaneously. Our platform leverages generative AI to transform scattered technical manuals, procedures, and maintenance guides into structured workflows accessible to every field worker from day one. From the office, supervisors can schedule and track work orders in real time; while on the field, technicians can execute guided work orders from connected mobile devices, capturing feedback, photos, and observations directly into the platform. This approach reduces traditional knowledge silos and embeds domain expertise into the organization itself. As a result, knowledge is no longer held by individuals alone but preserved within processes and systems.

The key to attracting the next generation is not rebranding industrial work but fundamentally modernizing how it is done. Digital tools, connected workflows, and data-driven processes are no longer nice-to-have features but baseline expectations. Younger professionals expect their work environment to be as technologically advanced and intelligently structured as any other sector.

ABB helps industry evolve toward a working culture that is both efficient and competitive. By combining knowledge preservation with modern tools and workflows, organizations can attract digital-native talent while ensuring institutional experience is never lost. The result is a workforce that bridges generations, one where experience and innovation work together, not against each other.

Looking ahead five to ten years, what changes do you believe will most profoundly transform industry?

From ABB’s perspective, we see three main structural and cultural shift:

Autonomous Operation: The convergence of operation data and advanced data analytics will progressively move manual routine operational decisions to autonomous execution, with operators in a supervisory and exception-management role.

The end of calendar-based maintenance: Fixed maintenance schedules should be replaced wherever possible by data-driven actions. Sending personnels to fields for routine inspection or maintenance should only be necessary when the condition indicates a need to do so. This shift is gradual, but over the next decade it will become the uncontested industrial standard.

Democratization of industrial expertise: Generative AI and agentic systems will make expert-level knowledge, which today are concentrated in a small number of senior engineers or technicians, available to every worker at every level of the organization, in real time and in natural language.

ABB is uniquely positioned to guide industrial customers through this transformation. With deep industrial domain knowledge, ABB Ability™ as an integrated digital platform, and a commitment to long-term partnerships, ABB helps organizations move beyond pilots to sustained competitive advantage.