Energy company Repsol has extended its co-innovation partnership with Dublin-based professional services firm Accenture to accelerate the use of generative AI across the company, through the introduction and deployment of AI agent systems. This “agentification” will help to improve the efficiency of processes as they are scaled across all company businesses.

Introducing AI agents is part of the evolution of Repsol’s digital program, an extension of the work carried out for more than two years in the energy firm’s Generative AI competence Center, which has laid the foundations for analyzing and understanding the advantages of generative AI and defined a strategy to extend it throughout the company.

“With the extension of our collaboration with Accenture, we continue to drive our digitalization and AI push through the introduction of generative AI agents,” said Josu Jon Imaz, CEO of Repsol. “We aspire to be one of the pioneering companies in the energy sector in the use of these technologies. Since we launched our Digital Program more than six years ago, Accenture has been providing us with tools to improve our efficiency and competitiveness, in our effort to transform the company through technology.”

The deal means Accenture will help build and deploy customized, autonomous AI agents, powered by components of the Accenture AI Refinery platform and the Nvidia AI platform, including Nvidia accelerated computing and Nvidia AI enterprise software.

In a press release, Repsol says these agents will help “reinvent and streamline processes into more dynamic and less complex workflows to boost productivity, ranging from planning and forecasting to application maintenance and incident resolution,” enabling Repsol employees to work faster, simpler and more efficiently.

The two companies will also explore the use of AI agents and Nvidia Omniverse for digital twins and robotic solutions to perform maintenance and other activities in its industrial and logistics centers more efficiently.

“We are excited to help Repsol achieve a new level of performance by working together to create tailored AI agents with the Accenture AI Refinery and the NVIDIA AI platform. Accelerating the use of agentic AI will enhance efficiency and productivity at speed, better serve customers with personalized experiences, and ultimately help Repsol gain competitive advantage,” said Julie Sweet, chair and CEO, Accenture.

On the customer side, these technologies will deliver personalized offers with greater accuracy and speed.

As part of this agreement, Repsol will also expand training for its employees.

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AI is not going to replace your workers and make strategic executive decisions. But any manufacturer not carefully considering a well-thought-out AI strategy is leaving money on the table. And it doesn’t matter if you are an enterprise-scale manufacturer or regional company with less than 150 employees, there are plenty of ways to extract value and gain an edge from AI.

“One area where manufacturers can quickly find ROI from AI is real-time knowledge sharing and translation, where AI breaks down language barriers by instantly translating and summarizing documentation like standard operating procedures, repair logs, and shop floor communications,” says Qaiser Habib, Head of Canada Engineering at Snowflake, a cloud data platform developer based out of Bozeman, Montana.

Snowflake has developed a flexible cloud-based platform for modern data teams looking to store, process, and analyze data in a highly scalable and cost-effective manner while enabling collaboration across different departments and organizations. The company has offices in 50 cities around the world. Habib speaks to us from Snowflake’s Canadian engineering hub in Toronto.

“This enables global manufacturing teams [and supply chain partners] to collaborate seamlessly, share best practices across facilities, and access critical operational insights in their native language.”

This is one of several “low-hanging fruit” areas Habib says will drive successful adoption of AI amongst manufacturers.

Manufacturers can also find near-immediate AI ROI is in key areas such as predictive maintenance, where AI monitors equipment to prevent costly unplanned downtime. Additionally, AI guided maintenance solutions can analyze repair logs and equipment manuals to recommend troubleshooting steps faster for more effective repairs.

According to Habib, the key to creating AI ROI is using AI solutions that understand your company and industry-specific processes rather than generic solutions.

AI predictions for manufacturing

Habib made a few predictions about how manufacturers will approach and interact with AI for the next year or so. Here’s a look:

Shift from AI hype to ROI: 2025 will mark the transition from AI experimentation to measurable business impact. 2024 saw widespread hype and experimentation as organizations explored AI and solidified their data strategies. Organizations are now demanding concrete returns, and engineering teams must identify specific, high value problems where AI can deliver measurable results and move beyond proofs of concept to full production systems that emphasize accuracy and reliability. The focus will be on operationalizing large language models (LLMs) and taking evolved approaches to security, governance, and observability.

This shift is particularly evident in the manufacturing industry, where enterprise AI applications are delivering tangible results through computer vision quality control systems. These systems are transforming production by dramatically reducing inspection time compared to manual processes, catching subtle defects that human inspectors might miss, preventing costly downstream quality issues, and enabling 24/7 continuous inspection without fatigue.

Organizations will prioritize AI projects that can demonstrate clear financial impact within specific timeframes, moving away from speculative “AI for AI’s sake” projects toward targeted solutions for well-defined business problems.

Engineers will face the stress test: With attention shifting towards ROI, engineering teams are experiencing unprecedented pressure to validate AI investments while maintaining code quality, security, and team wellbeing. As engineers, we are accustomed to working in fast-paced environments with new technologies and innovations, but there is now added pressure to balance rapid AI implementation with sustainable team practices.

To foster a productive and engaged workforce, leaders must ensure their teams have a crystal clear understanding of the business case behind their AI initiatives and provide the necessary training needed. As organizations prioritize these efforts, upskilling through certification programs and professional development initiatives will remain a critical focus in the workplace.

The key to success in 2025 will be finding the sweet spot between aggressive AI adoption and sustainable engineering practices. Teams that can demonstrate ROI while maintaining team health and code quality will set the standard for the industry, making strategic upskilling and clear business alignment more critical than ever.

AI-as-a-Service will accelerate AI adoption: AI-as-a-Service (AlaaS), which is the delivery of AI tools, apps, and capabilities as a cloud-based service, will emerge as a game-changer in 2025, making AI implementation more accessible and cost-effective for organizations facing adoption hurdles. By removing the need for large upfront investments in hardware and development, AIaaS allows companies to bolt AI capabilities onto existing systems with built-in security and governance features.

Now, instead of requiring developers and engineering teams to build and maintain their own AI systems, they can focus on solving business problems rather than building AI infrastructure from scratch. This also means teams can scale their AI initiatives based on their needs and select AI tools or services tailored to specific use cases. For example, a healthcare provider could use AlaaS to quickly stand up a diagnostic tool that analyzes medical images for early disease detection, while a large retailer can rapidly deploy GenAI-powered chatbots that pull from warranty and return policies to enhance customer service.

The AIaaS revolution also creates new opportunities for engineers to develop and monetize their own AI applications within cloud-based platforms. Similar to how mobile app stores transformed software distribution, AIaaS platforms will enable a new generation of AI-powered startups and create additional revenue streams for developers.

Manufacturing’s AI sweet spot

What does a sweet spot for AI adoption and engineering sustainability look like for a small or medium sized manufacturer that doesn’t have the scale to dedicate an executive to managing this?

“For small and medium manufacturers, the AI adoption sweet spot lies in focusing on practical, high-impact projects with clear ROI and avoiding overly complex, experimental initiatives,” Habib says, citing examples such as using conversational AI interfaces to make ad hoc data and analytics more accessible, creating agents to automate tasks like quality inspections, or implementing retrieval-augmented generation (RAG) systems to search, summarize, and recommend actions from knowledge bases.

At a small manufacturer, RAG systems could be used to improve customer support, technical documentation, or internal knowledge management. For example, an AI-powered chatbot could retrieve relevant technical manuals or past troubleshooting cases to answer customer inquiries more accurately. Additionally, RAG systems could assist with automated reporting or data analysis by generating insights from production data or historical reports, helping the manufacturer make better decisions.

Habib says manufacturers will leverage AIaaS through readily available cloud solutions that integrate with their existing operations. For example, they could use AI-powered demand forecasting services to optimize inventory levels, implement digital twin simulations to test process changes before deployment, or utilize natural language AI to automatically generate technical documentation from production data.

He adds that these solutions can be implemented incrementally, with costs scaling based on actual usage, making AI adoption more manageable and cost-effective than building custom solutions from scratch.

The post Searching for ROI from AI in 2025 appeared first on Engineering.com.

• Working as an engineering assistant.
• Engaging in more wide-ranging problem-solving with less effort and elapsed time.
• Automating business workflows more quickly and at a lower cost.
• Developing software more rapidly and with less effort.

Many engineers are walking along their personal digital transformation path. Sensors and software have replaced printed log books for data acquisition. Online systems have replaced file folders for data management. Excel has replaced paper worksheets. Digital displays have replaced large corkboards or walls decorated with many sticky notes. Data analytics became feasible once companies transformed most of their data into digital datastores.

These examples of how engineers use generative AI to drive value from digital transformation should trigger specific ideas for you to pursue.

Generative AI as an engineering assistant

Increasingly capable computers and improved application software have digitally transformed the work of engineers. Engineers access a myriad of data from many sources. It needs to be integrated, cleansed and analyzed to create value from all this mostly digital data. That work can be tedious and error-prone.

Generative AI has opened up new possibilities for engineers to view AI as an automated engineering assistant. Now, engineers can look more widely for data and simulate more possible solutions while reducing effort and the risk of errors.

“Strive to treat AI as an assistant and not simply as a new tool,” said Mel Head, a retired chemical engineer with a computing background who worked at Honeywell. “We still need to review the AI output before using it exactly as we would with a human assistant.”

On-target search

Once upon a time, engineers relied on printed reference texts for much of their work. Engineers have relied on Google’s digital search results like everyone else for many years. Search has brought much of the information engineers need for their work to their fingertips. When did engineers last visit a reference library or buy an engineering textbook?

Generative AI offers the ability to avoid the effort of sifting through Google’s digital search results by summarizing the available information and focusing on what’s most important. That AI ability assures engineers that nothing significant has been missed while reducing their effort.

“Using GPT-4o or Perplexity for search has pretty much supplanted Google searches for me. Rather than Google something and get a million links and many ads to sort through, GPT-4o and Perplexity give me well-formed and cited answers, said Jeff Uhlich, Principal Consultant at Obleeq Solutions. “They’re not always 100% accurate, but they are more useful than Google results.”

Powerful data analytics

Engineers have always analyzed data. First, slide rules, then calculators and then Excel. Each represented a significant advance for engineers.

In digitally transformed businesses, generative AI can analyze and summarize vast volumes of data to produce meaningful tables and impressive charts. That AI functionality improves the quality of reports while reducing engineers’ effort.

“I use Microsoft Data Analysis Expressions (DAX) and Power Query M to analyze oil and natural gas production data. AI tools like OpenAI ChatGPT and Microsoft CoPilot provide code snippets and links to reference material to confirm syntax details,” said Mark Perrin, petroleum engineer and VP at TriAcc Group. “The bottom line is that these AI tools save me a lot of time when solving client data problems.”

More capable business workflows

Every business operates with many workflows. Engineers often play a significant role in adapting workflows to changing business conditions or capturing the value of new technology.

Digital transformation creates an opportunity to incorporate more digital data into automated workflows. Then, generative AI can add more sophisticated decision-making to these workflows.

“With platforms like Zapier and Make to automate my workflows, AirTable to quickly develop low-code apps, and Notion to integrate diverse office data, I’ve streamlined and automated a plethora of business operations,” says Alan Mourgues, consulting reservoir engineer and founder at CrowdField. “From generating content for blogs and websites to data analysis, our business processes are fluid, responsive to business changes and efficient.

Faster software development

Developers and engineers have handcrafted digital software since the invention of computers. This work has been expensive, tedious and error-prone.

More recently, sophisticated integrated software development environments (IDE) and the wide availability of function-rich open-source software libraries have greatly improved developer productivity.

The advent of generative AI has improved developer productivity further by generating significant amounts of source code based on a comparatively short prompt. These advances will help software developers meet the voracious software appetite of engineers and our society more generally.

“ChatGPT is a powerful tool for engineers to develop software rapidly,” says Damien Hocking, CTO at Madala Software. “AI tools don’t give you a perfect solution, but it’s fantastic as a flying start that saves hours of grunt work.”

Digital research organization

Engineers are constantly involved in improving production performance and designing new products. That work includes significant research into new materials, automation and process advances. Digitally organizing the results of this work has been difficult. Document management systems and apps like Microsoft Notes have helped. When project teams collaborate, apps like Slack and Microsoft Teams preserve the discussion well.

Generative AI adds the ability to summarize and prioritize research results digitally. That saves engineers vast amounts of reading time.

“I’ve started replacing my extensive paper notes with a digital workflow on a tablet using the Nebo app,” said Brad Henrie, quality and process engineer at Sealweld Corporation. “My notes combine sketches, flowcharts, text, and calculations, so I can’t achieve the same results with a word processor. Nebo performs simple arithmetic, converts my handwritten text to computer-readable text and is searchable.”

Generative AI increases engineers’ productivity in a digitally transformed business without compromising quality or adding risk.

The post Enhancing your personal digital transformation path appeared first on Engineering.com.

Many executives foresee AI revolutionizing Software-as-a-Service (SaaS) by transitioning from static tools to dynamic, personalized, and intelligent capabilities. AI-as-a-Service (AIaaS) offers businesses unprecedented opportunities to innovate and scale. The promise is a future powered by AI agents and Copilot-like systems that streamline infrastructure, connect enterprise data, and reduce reliance on traditional configuration and system integration.

In a recent BG2 podcast, Satya Nadella shared his vision for AI’s role in reshaping technology and business. He stated, “The opportunities far outweigh the risks, but success requires deliberate action.” These opportunities extend beyond industry giants to startups and mid-sized enterprises, enabling them to adopt AI and leapfrog traditional barriers. Smaller enterprises, in particular, stand to gain by avoiding the pitfalls of complex digital transformations, taking advantage of AI to innovate faster and scale effectively.

Revolutionizing Experiences and Integration

AI is (or will be) fundamentally changing how users interact with SaaS platforms. Traditional SaaS tools are often said to be rigid, offering one-size-fits-all interfaces that require users to adapt. In contrast, AI brings opportunities to disrupt this model by analyzing user behavior in real-time to offer personalized workflows, predictive suggestions, and proactive solutions. Nadella emphasized this transformation, saying, “The next 10x function of ChatGPT is having persistent memory combined with the ability to take action on our behalf.”

This aligns with the emergence of Copilot systems, where AI acts as a collaborative partner rather than a mere self-contained tool. Imagine a SaaS platform that not only remembers user preferences but actively anticipates needs, offering intelligent guidance and dynamic adjustments to workflows. Such personalization fosters deeper engagement and loyalty while transforming the management of business rules and system infrastructure.

Empowering Smaller Enterprises

The promise of AI extends not only to large enterprises but also to smaller businesses, particularly those in manufacturing and traditionally underserved sectors. For example, a small manufacturer could adopt AI-driven tools to optimize supply chain management, automate repetitive tasks, and deliver personalized customer experiences—all without the complexity of traditional ERP systems.

To ensure successful adoption, businesses must:

• Identify high-impact areas: Focus on processes that benefit most from automation and predictive analytics, such as customer service, supply chain management, or marketing optimization.
• Leverage scalable solutions: Choose AI platforms that align with current needs but can scale as the business grows.
• Build internal expertise: Invest in upskilling employees to work alongside AI tools, ensuring alignment between human and machine capabilities.
• Partner strategically: Collaborate with AI vendors that prioritize interoperability and ethical standards to avoid vendor lock-in and compliance risks.

Redefining Value: Pricing Models and Proactive Solutions

AI is not only transforming technical capabilities but also redefining pricing models for SaaS platforms. Traditional subscription fees are being replaced by real-time, usage-based pricing, powered by AI algorithms that align revenue with the value delivered. Nadella warned, “Do not bet against scaling laws,” underscoring AI’s potential to adapt and optimize at scale. For instance, AI can analyze customer usage patterns to calculate fair, dynamic pricing, ensuring customers pay for the outcomes that matter most.

This shift to value-based pricing can help SaaS companies differentiate themselves in competitive markets, reinforcing their commitment to customer success. Additionally, as AI drives data integration, traditional software vendors (ERP, CRM, PLM, MES, etc.) will need to adapt their business models. With AI, vendor lock-in could become obsolete, or at least redefined, as businesses migrate data seamlessly across platforms, fueled by open standards and interconnected data assets.

Overcoming Adoption Challenges

While the promise of AIaaS is immense, transitioning from traditional SaaS is not without its hurdles. Businesses must address:

• Cost barriers: AI solutions can require significant upfront investment, especially for smaller firms. Clear ROI metrics and phased implementation plans can mitigate this challenge.
• Technical expertise gaps: The lack of in-house AI expertise can slow adoption. Partnering with AI-savvy consultants or platforms can bridge this gap.
• Resistance to change: Shifting from static tools to dynamic AI-driven systems requires cultural change. Leadership must communicate the benefits clearly and provide training to ease transitions.

Responsible AI: Trust, Compliance, and the Road Ahead

The rise of AI-powered SaaS platforms presents both immense opportunity and significant responsibility. As these platforms analyze vast datasets, safeguarding user privacy and ensuring compliance with regulatory standards will be non-negotiable. Nadella’s remark that “Innovation must go hand in hand with ethical considerations” underscores the need to balance technological advancement with accountability.

To build trust and ensure accountability, businesses must prioritize:

• Transparent data policies: Clearly communicate how user data is collected, stored, and used.
• Robust security measures: Safeguards against data breaches are critical for maintaining trust.
• User-centric governance: Empower users with control over their data while ensuring compliance with global regulations.

Final Thoughts…

Looking ahead, adaptive AI systems and large language models will continue to redefine how SaaS platforms deliver value, addressing evolving customer needs with precision and speed. Nadella’s vision for AIaaS is inspiring, but businesses must remain grounded. To lead in this new era, organizations must tackle critical questions:

• How can they balance AI’s immense potential with the risks of misuse or ethical lapses?
• What steps are necessary to ensure AI enhances—not replaces—human decision-making?
• How can smaller enterprises leapfrog traditional barriers to scale with AI?
• Can persistent memory systems foster meaningful personalization without sacrificing user trust?
• What role will regulatory frameworks play in ensuring accountable innovation?

By addressing these questions and embracing the opportunities AI presents, SaaS providers can chart a path toward sustained success. The question is not whether AI will transform SaaS, but how organizations will adapt to lead in this new digital era

The post RIP SaaS, long live AI-as-a-service appeared first on Engineering.com.

McKinsey have predicted that generative artificial intelligence will increase workforce productivity with a global value of  $6.1 -$7.9 trillion annually. They expect this to be achieved at some point between 2030 and 2060 and be based on making existing workers more productive by complementing their work and through elimination of jobs by automation.

The impact of artificial intelligence on productivity will be added to impacts on other organisational elements for a combined benefit of $17.1 -$25.6 trillion – a massive boost for the organisations that are successful in their adoption of artificial intelligence. It is clear that in most industries, organisations’ AI adoption will determine competitive success – failure to adopt will be existential for many organisations and their employees.

On October 24^th 2024, the Boston Consulting Group released their “Where’s the Value in AI?” report which described the current state of AI adoption. Most companies are at an early stage and only 4 % have reached the point where value is being generated from it. The BCG provide guidance on how organisations should move forward – emphasising that 70 percent of the implementation focus should be on people and processes.

This guidance stresses the importance of employee participation in and support for artificial intelligence. Changes in working practices and skills as a result of AI driven changes in processes, for example with the introduction of work practice guidance based on AI enabled quality inspection, will only be successful if employees support the new practices and act on the guidance provided.

Employee attitudes towards artificial intelligence in most companies do not support this AI positive behaviour today. In fact, they are likely to make artificial intelligence adoption very difficult. While, according to the World Economic Forum, 60 percent of adults globally expect that AI will significantly change their lives in the https://www.weforum.org/stories/2022/01/artificial-intelligence-ai-technology-trust-survey/ next three to five years, 61 percent don’t trust them. A recent Accenture report notes 95 percent of employees see that there will be value for the organisation in working with generative AI but 60 percent worry that it will cause job loss, stress and burnout.

These statistics illustrate the importance of the centrality of employees in your artificial intelligence implementation.  Our work on AI for our Digital Transformation Certificate online program at Watspeed at the University of Waterloo includes a focus on the development of tools and techniques that will help organisations conduct analysis of, plan and manage successful implementation of technological change. We have developed a tool that focuses on your efforts to positively motivate employees for artificial intelligence. The tool structures your consideration of the key aspects of employees and AI. These are:

Adoption Design: The relationship between AI and people needs to be carefully considered. Employees are more likely to support adoption if the technology is aligned with their skills and capabilities, if its impact on employee wellness is taken into account, if it makes them personally more valuable and secure in their jobs and if careful consultations on AI adoption have taken place with employees.

Managing Change: The human side of any significant organisational change always has to be managed carefully – and that includes AI. Effective communication processes should be being used and strong consultation processes should be in place throughout the implementation. Where severance is necessary it should be handled fairly and sensitively and employees should be confident that their quality of working life will be improved by AI.

Training: We usually understand that AI skills training will be necessary for many employees. We don’t always recognize that the introduction of Ai requires employees to have a better basic understanding of, and comfort with, information technologies, the processes they are contributing to and how these impact organisational performance. These are necessary to enable them to maximise the impact of artificial intelligence and to give them confidence in their future value to the organisation.

Wellness: Our research shows that that employees are apprehensive about artificial intelligence, fearing that it will reduce the quality of their working life through increasing stress and burnout and reducing job security. The introduction of AI will often require significant job disruption and change that will challenge mental wellness. Having good employee wellness support systems in place will be essential.

Artificial Intelligence Impact: While our focus here is on engaging employees, we must also maintain our determination that AI will contribute to the strategic objectives of the organisation. This section is included to ensure that this emphasis on performance is the objective of the work with employees.

The following short assessment is intended to help you consider how you will achieve effective employee participation in artificial intelligence. Complete the assessment by rating each of the following statements on a 1 – 5 scale, with a response of 1 indicating strong disagreement with the statement and a response of 5 indicating strong agreement with it:

Adoption Design

Managing Change

Training

Wellness

Artificial Intelligence Impact

Once you have completed the assessment, total your score for each section. Sections with the lower scores indicate where work is required to strengthen your artificial intelligence implementation.

The post Achieving employee engagement for artificial intelligence appeared first on Engineering.com.

Join Patsnap and TRIZ Consulting Group in this exclusive webinar to learn how the TRIZ method paired with AI can be applied to accelerate innovation and enhance patentability.

We’ll cover:

• The Fundamentals of TRIZ: Learn how TRIZ helps identify and resolve contradictions in the innovation process to drive faster, smarter solutions.
• Turning Innovation into IP: Explore strategies for converting inventive breakthroughs into patentable assets, gaining a competitive edge.
• Real-World Impact: See TRIZ in action through real-world case studies illustrating its role in revolutionizing industries and fostering game-changing innovations.
• AI-Driven TRIZ – Tools of the Future: Discover how the integration of AI with TRIZ is reshaping problem-solving techniques and creating innovative tools that are redefining the landscape of invention.

This on-demand webinar is sponsored by Patsnap.

The post How AI & TRIZ Provide Better Answers for Faster R&D Solutions appeared first on Engineering.com.

Many engineers are investigating digital transformation initiatives using artificial intelligence (AI) features within their organizations.

They’re generally aware of various AI risks and are searching for ways to better categorize, understand, mitigate and communicate them.

A group of scientists from the Massachusetts Institute of Technology (MIT) and other universities are also concerned with AI risks, so they built an AI Risk Repository to serve as a common frame of reference for discussing and managing AI risks.

They classified AI risks into seven AI risk domains:

1. Discrimination & toxicity.
2. Privacy and security.
3. Misinformation.
4. Malicious actors and misuse.
5. Human-computer interaction.
6. Socioeconomic and environmental.
7. AI system safety, failures and limitations.

When engineers use these AI risk domains to conduct risk management for their digital transformation initiatives, they will gain a better understanding of how AI can help their digital transformation initiatives without causing irreparable harm.

Discrimination & toxicity

The discrimination and toxicity AI risk domain consists of the following subdomains:

1. Unfair discrimination and misrepresentation – Unequal treatment of individuals or groups by AI.
2. Exposure to toxic content – AI systems expose end-users to harmful, abusive, unsafe, or inappropriate content.
3. Unequal performance across groups – The accuracy and effectiveness of AI decisions and actions are directly related to group membership.

If this risk becomes a reality, the business impact includes loss of reputation and the distraction and cost of lawsuits. If this risk turns into reality, the societal implications include unfair outcomes for discriminated groups.

Privacy and security

The privacy and security AI risk domain consists of the following subdomains:

1. Compromise of privacy – AI systems obtain, leak, or correctly infer sensitive information.
2. Security attacks – AI systems exploit systems, software development tool chains and hardware vulnerabilities.

If this risk turns into reality, the business impact includes data and privacy breaches and loss of confidential intellectual property, leading to regulatory fines and the cost of lawsuits. If this risk becomes a reality, the societal implications include:

• Compromising end-user privacy expectations, assisting identity theft, or causing loss of confidential intellectual property.
• Breaches of personal data and privacy.
• System manipulation causing unsafe outputs or behavior.

Misinformation

The misinformation AI risk domain consists of the following subdomains:

1. False or misleading information – AI systems inadvertently generate or spread incorrect or deceptive information.
2. Pollution of the information ecosystem and loss of consensus reality – Highly personalized AI-generated misinformation creates “filter bubbles” where individuals only see what matches their existing beliefs.

If this risk turns into reality, the business impact includes misleading performance information, employee mistrust and potentially dangerous product designs. If this risk becomes a reality, the societal implications include inaccurate beliefs in end-users that weaken social cohesion and political processes.

Malicious actors and misuse

The malicious actors and misuse AI risk domain consists of the following subdomains:

1. Disinformation, surveillance and influence at scale – Using AI systems to conduct large-scale disinformation campaigns, malicious surveillance, or targeted and sophisticated automated censorship and propaganda.
2. Cyberattacks, weapon development or use and mass harm – Using AI systems to develop cyberweapons, develop new or enhance existing weapons.
3. Fraud, scams and targeted manipulation – Using AI systems to gain a personal advantage over others.

If this risk becomes a reality, the business impact includes loss of business continuity, cost to recover from attacks and bankruptcy. If this risk turns into reality, the societal implications include:

• Manipulating political processes, public opinion and behavior.
• Using weapons to cause mass harm.
• Enabling cheating, fraud, scams, or blackmail.

Human-computer interaction

The human-computer interaction AI risk domain consists of the following subdomains:

1. Overreliance and unsafe use – End-users anthropomorphizing, trusting, or relying on AI systems.
2. Loss of human agency and autonomy – End-users delegate critical decisions to AI systems, or AI systems make decisions that diminish human control and autonomy.

If this risk turns into reality, the business impact includes misleading and potentially dangerous system outputs. If this risk becomes a reality, the societal implications include compromising personal autonomy and weakening social ties.

Socioeconomic and environmental

The socioeconomic and environmental AI risk domain consists of the following subdomains:

1. Power centralization and unfair distribution of benefits – AI-driven concentration of power and resources within certain entities or groups.
2. Increased inequality and decline in employment quality – The widespread use of AI leads to social and economic disparities.
3. Economic and cultural devaluation of human effort – AI systems capable of creating economic or cultural value reproduce human innovation or creativity.
4. Competitive dynamics – Competition by AI developers or state-like actors perpetuates an AI “race” by rapidly developing, deploying and applying AI systems to maximize strategic or economic advantage.
5. Governance failure – Inadequate regulatory frameworks and oversight mechanisms fail to keep pace with AI development.
6. Environmental harm – The development and operation of AI systems cause environmental damage, partially through their enormous electricity consumption.

If this risk becomes a reality, many businesses will likely collapse due to economic collapse. If this risk turns into reality, the societal implications include:

• Inequitable distribution of benefits and increased societal inequality.
• Destabilizing economic and social systems that rely on human effort.
• Reduced appreciation for human skills.
• Release of unsafe and error-prone systems.
• Ineffective governance of AI systems.

AI system safety, failures and limitations

The AI system safety, failures, & limitations AI risk domain consists of the following subdomains:

1. AI pursuing its own goals in conflict with human goals or values – AI systems act in conflict with ethical standards or human goals or values.
2. AI possessing dangerous capabilities –  AI systems develop, access, or are provided with capabilities that increase their potential to cause mass harm through deception, weapons development and acquisition, persuasion and manipulation, political strategy,cyber-offense, AI development, situational awareness and self-proliferation.
3. Lack of capability or robustness – AI systems fail to perform reliably or effectively under varying conditions, exposing them to errors and failures that can have significant consequences.
4. Lack of transparency or interpretability – Challenges in understanding or explaining the decision-making processes of AI systems.
5. AI welfare and rights – Ethical considerations regarding the treatment of potentially sentient AI entities.

If this risk becomes a reality, the business impact includes loss of reputation and the distraction and cost of lawsuits. If this risk turns into reality, the societal implications include:

• AI using dangerous capabilities such as manipulation, deception, or situational awareness to seek power or self-proliferate.
• Mistrusting AI systems.
• Enforcing compliance standards becomes difficult.

Do not expect every digital transformation initiative to have risks in every subdomain. Nonetheless, there is value in explicitly considering every subdomain during the risk identification task.

When digital transformation teams identify and mitigate project risks using these AI risk domains, they will ensure their risk management processes are as comprehensive as possible.

The post Managing AI risks in digital transformation appeared first on Engineering.com.

The power of artificial intelligence (AI) to enhance digital transformation initiatives has become increasingly evident to engineers as they seek to improve operational efficiencies, scale innovation and gain a competitive edge.

While digital transformation is hardly new, AI and large language models (LLMs) have emerged as a formidable accelerator by changing business processes, reshaping products and services and sometimes upending entire industries.

AI’s ability to learn and improve over time, coupled with digital transformation, means that organizations can realize faster processes, reduced costs and more efficient operations.

These AI benefits contribute to an environment of continuous improvement and innovation that is often key to success in a competitive environment.

Enhancing data-driven decision-making

Engineers know that data-driven organizations use digital insights to shape strategies, optimize processes and respond rapidly to market changes. However, harnessing the potential of integrated digital data at scale for decision-making requires far more than traditional data analytics.

AI’s capability adds unprecedented speed and precision to decision-making by:

• Sifting through structured data, identifying patterns and generating predictive insights.
• Analyzing vast volumes of unstructured data more effectively than search engines, specialized databases or software developers to generate predictive insights.
• Avoiding the cost and elapsed time associated with custom data integration of diverse data sources using software developers.
• Autonomously detecting trends and forecasting outcomes.

Adding AI and LLM capability to data-driven decision-making helps engineers optimize operational and strategic decisions while reducing the need to base decisions on history, experience, in-vogue ideas or hunches.

Examples of adding AI capability to data-driven decision-making for engineering include:

• Monitoring large volumes of IIoT data from production equipment to identify performance anomalies to avoid unscheduled downtime.
• Sifting through the external media for general and industry audiences to identify competitor initiatives that may require a response.
• Summarizing patent data, trademark data and research journals maintained in multiple languages to identify potentially relevant technology developments.

Automating processes and workflows

Automation is a fundamental aspect of digital transformation. AI-powered tools like robotic process automation (RPA), machine learning and cognitive computing, a type of AI that simulates human thought processes, have taken digital transformation to new heights.

While valuable, previous generations of automation that engineers implemented were limited to well-defined, repetitive tasks and detailed, rule-based decisions. AI expands automation to more complex decision-making processes, pattern recognition and more generalized problem-solving.

Examples of adding AI capability to automating processes and workflows include:

• Adding more accuracy and sophistication to simulations. For example, engineers can refine and enhance their designs through successive simulations to reduce limitations, which leads to more innovative solutions.
• Enhancing supply chain management for better product demand forecasting, logistics optimization, order fulfillment and risk assessments for component shortages. Achieving these improvements requires the integration of disparate data sources maintained by partners.
• Adding more intelligence to RPA transaction workflows such as invoice and shipment receipt processing. Examples include identifying potentially duplicate invoices, assessing the materiality of discrepancies and identifying likely fraud.

Improving data quality

Engineers are painfully aware that insufficient data quality is the number one reason for the failure of digital transformation initiatives. Asking data analysts to identify and correct data quality issues is slow, tedious, expensive and subject to further errors.

AI can automate data quality improvement work using pattern recognition. AI can achieve more speed and consistency at a lower cost than human analysts.

Examples of using AI capability to automate data quality improvement include:

• Recognizing that existing equipment can’t manufacture the designs due to dimensions, lack of accessibility and unachievable tolerances.
• Identifying and correcting instances where numeric values are associated with different units of measure or measurement systems creates errors and confusion.
• Sharply reducing the number of duplicate and incomplete inventory master records.
• Generating synthetic data to augment existing datasets to improve AI models.

Persisting knowledge

Organizations lose surprising amounts of essential knowledge and intellectual property (IP). Too often, engineers reinvestigate problems or wrestle again with design refinements because of a lack of awareness of prior work. Loss of knowledge and expertise typically occurs due to:

• Staff turnover and transfers.
• Reluctance to share knowledge.
• Lack of management support for knowledge management.
• Lack of time to document work.
• No repository in which to store work products.
• No easy ability to search and retrieve documents.
• Organization restructuring, acquisitions and mergers.
• Confusion caused by inaccurate, outdated or redundant versions of information.

Addressing these issues without digital transformation is impossible. Including digital knowledge management to the scope of digital transformation initiatives can significantly increase the value organizations achieve from the knowledge they have accumulated, often at considerable effort and cost.

Adding AI agents to knowledge repositories can add another increment of value. AI agents are intelligent software that use an LLM to perform query tasks, make decisions and learn from their experiences like humans. AI agents are a significant advance on the more familiar chatbots.

Examples of using AI agents to enhance digital knowledge management include enabling engineers to:

• Query “tribal knowledge” to improve production performance.
• Discover best practices.
• Better troubleshoot production equipment problems based on records of historical incidents.
• Query IP such as patent records, test results, research reports, development studies and licensing agreements in support of current work.

Challenges and considerations

While AI and LLMs add potential to digital transformation, engineers must acknowledge the challenges and ethical considerations associated with its deployment. These include:

• Ensuring data privacy to maintain customer and employee confidence.
• Addressing biases in AI algorithms and training data to maintain trust and inclusivity.
• Recognizing that LLMs may be incomplete or misleading.
• Training a skilled workforce that can effectively manage AI-driven processes.
• Fostering a culture that embraces innovation to ensure the smooth integration of AI and LLM technologies.

Engineers can establish robust data governance, prioritize transparency in communication and continuously monitor AI systems to mitigate unintended consequences.

Artificial intelligence is a powerful accelerator of digital transformation. Its impact spans most industries and functions, enhancing efficiency, agility and resilience. By embracing AI’s transformative potential, businesses can achieve a sustainable competitive advantage and drive long-term growth.

The post Where AI can accelerate digital transformation appeared first on Engineering.com.

Danish cobot maker Universal Robots has unveiled its AI Accelerator, a ready-to-use hardware and software toolkit created to further enable the development of AI-powered cobot applications.

Designed for commercial and research applications, the UR AI Accelerator provides developers with an extensible platform to build applications, accelerate research and reduce time to market of AI products.

The toolkit brings AI acceleration to Universal Robots’ (UR) next-generation software platform PolyScope X and is powered by NVIDIA Isaac accelerated libraries and AI models, running on the NVIDIA Jetson AGX Orin system-on-module. Specifically, NVIDIA Isaac Manipulator gives developers the ability to bring accelerated performance and state-of-the-art AI technologies to their robotics solutions. The toolkit also includes the high-quality, newly developed Orbbec Gemini 335Lg 3D camera.

Through in-built demo programs, the AI Accelerator leverages UR’s platform to enable features like pose estimation, tracking, object detection, path planning, image classification, quality inspection, state detection and more. Enabled by PolyScope X, the UR AI Accelerator also gives developers the freedom to choose exactly what toolsets, programming languages and libraries they want to use and the flexibility to create their own programs.

UR says AI Accelerator is just the first to market of a series of AI-powered products and capabilities in UR’s pipeline with the goal of making robotics more accessible.

With a small hardware upgrade, the software is compatible with UR’s e-Series cobots and the new-generation cobots UR20 and UR30.

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With a foundation already set in AI and generative AI capabilities, Siemens is taking a strategic leap to deepen its offerings in areas such as Product Lifecycle Management (PLM) and Digital Twins.

Yet, the acquisition raises critical questions: Is Siemens advancing its strategic edge by embedding next-level AI and knowledge graph technologies, or is it scrambling to keep up in a landscape that is moving faster than ever?

Elevating AI-driven PLM and digital twins

Siemens’ integration of Altair’s powerful AI, simulation and high-performance computing tools into its PLM tech suite, particularly within Teamcenter and Simcenter, offers a potential transformation in how digital twins and simulations are used across engineering and manufacturing. Altair’s deep expertise in physics-based simulations, including mechanical and electromagnetic modeling, could allow Siemens to develop more sophisticated digital twins that not only represent physical products but also predict behaviors and outcomes with high fidelity.

With Altair’s technology, Siemens can push digital twin capabilities beyond basic visualization and monitoring, creating a system that incorporates real-time data, predictive analytics and adaptive simulations. This would enable manufacturers to make informed, AI-driven decisions at every stage of the product lifecycle, from design and development to production and maintenance.

However, despite Siemens’ existing portfolio, which includes substantial AI and generative AI tools, the acquisition raises a critical question—how effectively can Siemens embed these capabilities as a core, transformative feature within its PLM platform? Without a clear path to seamlessly integrate AI across its offerings, Altair’s capabilities risk being relegated to auxiliary add-on features, potentially limiting their business impact. For Siemens, this move is more than just adding tools; it’s about embedding intelligence deeply within the end-to-end PLM framework, making AI a central component of its digital transformation strategy.

Enhancing digital twins with HPC

Siemens is marketing itself as a leader in digital twin technology, primarily through its Xcelerator platform, which integrates real-time operational data to improve asset management, production efficiency and product quality. Altair’s HPC capabilities could significantly enhance Siemens’ digital twin offerings by allowing more complex, detailed, and faster simulations—an essential component of predictive maintenance and optimization for manufacturers.

The integration of HPC into Siemens’ digital twin ecosystem could be transformative, enabling simulation models that accommodate an unprecedented scale of data and complexity. For instance, manufacturers could simulate entire production lines or supply chain networks, gaining insights that help them optimize operations, reduce energy consumption, minimize downtime and predict implications from product changes. This is particularly relevant as industries move toward more sustainable and resilient operations.

However, leveraging Altair’s HPC across Siemens’ existing infrastructure poses some challenges. HPC solutions typically require specialized infrastructure, substantial processing power and technical expertise. Siemens will need to carefully consider how to bring HPC capabilities into mainstream use within its portfolio, including positioning within its maturing SaaS offering. The risk here is that without a robust integration plan Altair’s HPC tools may remain isolated and less affordable, providing limited impact and reducing the transformative potential of this acquisition.

Knowledge graph technology: connecting data with digital thread

Altair’s recent acquisition of Cambridge Semantics, a developer of knowledge graph and data fabric technologies, brings new dimensions to the integration of enterprise data across complex manufacturing ecosystems.

Knowledge graphs provide a framework for Siemens to unify and contextualize vast amounts of data from disparate systems—an essential step for effective AI-driven insights and accurate digital twin models. With knowledge graphs, Siemens could break down data silos, connecting information from PLM, digital twins, and other systems into a cohesive whole, creating a seamless digital thread across the lifecycle.

Incorporating Cambridge Semantics’ knowledge graph technology into Siemens’ portfolio could lead to a new era of “data-rich” digital twins, where structured and unstructured data come together to provide a more comprehensive, actionable view of products, assets and operations. By grounding generative AI models in real-world data, knowledge graphs could improve response quality and deliver contextual insights, allowing engineers and operators to make better, faster decisions.

Yet, the question remains: can Siemens adapt this advanced data integration technology effectively in an industrial setting? Cambridge Semantics’ data fabric has been proven in sectors like defense, life sciences, and government. Adapting it for manufacturing will require Siemens to navigate industry-specific complexities. Without careful implementation, the risk is that knowledge graph technology will be underutilized—merely another tool rather than a strategic game-changer in Siemens’ PLM and digital twin offerings.

Strategic opportunity or catch-up?

The acquisition of Altair could empower Siemens to lead in AI-driven PLM, high-fidelity simulations and data-enriched digital twins. But the road ahead demands more than technological additions; it requires Siemens to deeply integrate these capabilities within its core platforms and ensure they serve as transformative, essential components rather than optional add-ons.

For Siemens, the challenge lies in more than simply acquiring AI—it’s about operationalizing it. By embedding Altair’s and Cambridge Semantics’ technologies as central pillars in its software ecosystem, Siemens has the opportunity to redefine industrial intelligence in manufacturing. Can Siemens realize this vision to become a true leader in AI-driven industrial software, or will it struggle to fully leverage these assets, ending up as a late entrant in a rapidly advancing field?

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