Today’s businesses are experiencing a dynamic shift as they eagerly strive to embrace generative AI to stay competitive in an ever-evolving market

Generative AI (Gen AI) is the subset of AI that focuses on the creation of new content, such as text, images, video, audio, and software code autonomously using sophisticated machine learning models called deep learning models. Though traditional AI works through machine learning to complete tasks and learn or make decisions independently, it cannot create new information.

The future of supply chains, fueled by generative AI, will transcend traditional optimization and enter a realm of unprecedented adaptability and resilience. Gen AI’s unique ability to create, simulate, and predict will revolutionize areas like demand forecasting, where it can generate nuanced scenarios based on vast datasets and real-time signals.

Gen AI will be instrumental in achieving self-healing supply chains. While traditional AI can analyze data and identify patterns to predict potential disruptions, Gen AI takes it a step further. It can create and simulate a wide range of scenarios, enabling the supply chain to proactively identify vulnerabilities and generate innovative solutions to mitigate risks before they materialize. Gen AI’s ability to create novel solutions and adapt to changing circumstances is crucial for achieving true self-healing capabilities in supply chains.

Where Gen AI departs from traditional AI

Imagine a supply chain where traditional AI acts like a vigilant guard, constantly scanning for potential threats. It might identify a potential delay in a shipment due to weather conditions, raising an alarm. Gen AI, on the other hand, is a resourceful problem solver. It takes that alarm and springs into action, not just identifying the problem but also brainstorming and generating a multitude of potential solutions. It might propose rerouting the shipment, reallocating resources to expedite other deliveries, or even proactively contacting alternative suppliers to ensure seamless fulfillment. In essence, Gen AI transforms the supply chain from a reactive system to a proactive one, where problems are not just identified but also actively and creatively solved before they disrupt operations.

Currently, 30% of supply chain leaders actively plan to deploy generative AI for supply chain in the next six months.^[i] The projection for increased adoption is driven by Gen AI’s ability to significantly reduce costs, improve efficiency, harness complex data, and increase revenue within business units that deploy the technology. Moreover, a recent Gartner survey of 127 supply chain leaders found that ‘Chief Supply Chain Officers (CSCOs) are dedicating 5.8% of their budget to Gen AI in 2024.’ The mass adoption of this emerging technology is further validated in the same report, with the finding that only ‘2% of respondents say they have no plans to leverage Gen AI.’^[ii]

Capgemini Invent recognizes the vast potential of Gen AI to revolutionize supply chains. While numerous opportunities exist, this perspective focuses on two key areas: scenario modeling and demand planning. In future communications, we will explore additional applications of this transformative technology. Additionally, we will highlight how with the right approach, businesses can unlock and scale the benefits of Gen AI.

“In the world of generative AI, supply chains will evolve from reactive systems to proactive networks, anticipating needs, optimizing resources, and seamlessly adapting to changes in real time.”

Phil Davies – Global Supply Chain leader, Capgemini Invent

Supply chain scenario modeling: a strategic perspective

In an era of unprecedented disruptions, supply chains are under immense pressure. Natural disasters, geopolitical events, pandemics, and even minor internal operational hiccups can trigger cascading effects that lead to production delays, shortages, and financial losses. In the urgent search for solutions, many business leaders are beginning to explore Gen AI’s capabilities as a means to tackle this issue.

The cost of not managing such disruptive supply chain risks effectively is immense, as evidenced by the billions of dollars lost in recent years. A recent Gartner study revealed that ‘75% of supply chain leaders expect an increase in high-impact disruptions compared to the rate of disruptions over the past 5 years.’^[iii] This highlights the urgent need for innovative solutions. For many organizations, Gen AI has proven to be invaluable in this regard.

Gen AI doesn’t just predict the future; it creates it!

Gen AI: the game-changer in scenario modeling

Scenario modeling is a crucial capability of the supply chain, enabling companies to simulate various risk scenarios and prepare appropriate responses. There is a potential for Gen AI to start leveraging models to simulate potential disruptions, companies can gain valuable insights and develop effective contingency plans.

Generative AI in supply chains can revolutionize scenario modeling by creating diverse, novel scenarios beyond historical data, analyzing unstructured sources, such as news and social media. By synergistically combining Gen AI’s generative capabilities with existing AI/ML techniques, supply chain scenario modeling can achieve a new level of sophistication. This powerful combination enables organizations to anticipate disruptions, explore innovative strategies, and make more informed, data-driven decisions, ultimately leading to improved efficiency, resilience, and adaptability in the face of ever-changing market conditions.

Gen AI will exclusively build upon the existing AI and machine learning (ML) capabilities in supply chain scenario modeling to create a more powerful and comprehensive approach. While reinforcement learning (RL), simulation modeling, and agent-based learning provide the foundation for optimizing decision-making, Gen AI is capable of so much more. Unlike traditional AI, Gen AI’s ability to process vast complex data and generate insights facilitates more comprehensive and proactive risk management in today’s supply chains. By modeling the impact of everything from equipment failures and labor shortages to geopolitical events, natural disasters, and cyberattacks, businesses can gain a deeper understanding of their vulnerabilities and develop proactive mitigation strategies.

Gen AI will democratize scenario modeling, enabling leadership to run simulations directly through large language models (LLMs), reducing the need for specialized modelers and tech developers.

The future is what we make it

Despite being its most prominent capability, this transformative technology can unlock benefits far beyond scenario modeling. In fact, Gen AI does not just predict the future; it creates it. Gen AI enables companies to stress-test their supply chains in a virtual environment, experimenting with different scenarios and responses to find the most effective solutions. This ability to “play out” potential disruptions before they occur provides a level of preparedness and resilience that was previously unattainable.

Companies that embrace Gen AI-powered scenario modeling can gain a significant competitive edge. They can achieve the following outcomes:

• Anticipate disruptions: By simulating potential risks, companies can identify weaknesses in their supply chains and take preemptive action to mitigate them.
• Optimize operations: Scenario modeling can help companies optimize inventory levels, allocate resources more effectively, and design more resilient transportation networks.
• Respond faster to crises: When disruptions do occur, companies with Gen AI models can quickly assess the situation, generate alternative scenarios, and choose the best course of action.
• Innovate and adapt: By continuously learning and adapting to new data, Gen AI models can help companies stay ahead of the curve and respond to changing market conditions.

Clearly, there are great potential benefits to integrating Gen AI for scenario and risk modelling!

Demand planning: Bridging the Gaps

Demand planning is a critical component of supply chain management that involves forecasting to ensure a company can meet future customer demand. The future of demand planning is not just about better predictions – it’s about unlocking a new level of understanding. Gen AI is not replacing current AI and ML models; it’s supercharging them. Imagine a demand planning system that does not just crunch numbers, but grasps the nuances of market shifts, consumer behavior, and global events. This is the promise of Gen AI. By synthesizing vast amounts of structured and unstructured data, it creates a dynamic, real-time picture of demand. This is not just about accuracy; it is about adaptability. Supply chains become agile, responding to disruptions with foresight, not hindsight. It is about empowering planners with insights that go beyond numbers, enabling them to make strategic decisions that drive growth and resilience.

These qualities set Gen AI apart from traditional AI capabilities that cannot generate new data or content and are restricted to analyzing existing datasets only. This enables planners to experiment with different scenarios to stress-test their supply chains and formulate optimal strategies for a variety of market conditions.

In today’s market, we are seeing Gen AI-powered demand forecasting systems improve forecast accuracy by up to 10% already by using Gen AI to predict and optimize the forecast. It incorporates market data and signals in addition to historical order and shipment information, employing a library of probabilistic and deep learning models to identify accuracy and reduce bias across product, geography, and time hierarchies.^[iv]

Gen AI presents a powerful opportunity for supply chains leaders to empower their demand planners, who can in turn guard against uncertainty and transform their supply chains in ways that will ensure they thrive despite uncertainty.

Scaling and readiness for Gen AI

As part of the Gen AI revolution, a multitude of use cases have been identified where the technology can potentially drive or unlock significant business value. However, recent research highlights that more than 85% of Proof-of-Concepts (PoCs) for Gen AI have failed to move to production. Therefore, it is crucial to thoroughly assess their feasibility, ascertain their true value, and evaluate their scalability within an organization to fully realize the associated benefits.^[v]

To support this, businesses should look to establish processes and create a shift in mindsets whereby use cases can move beyond ideation to be fully assessed, tested, deployed, and adopted to deliver value. Leaders must move away from traditional, reactive risk management approaches and embrace a proactive, data-driven mindset. Based on our experience, the following guidelines are provided as the core pillars to enable successful Gen AI in supply chain deployment.

GenAI business readiness

• Gen AI Strategizing: organizing dedicated workshops to clearly define your Gen AI strategy and identify use cases that will provide the most benefits.
• Build the right toolkit: Leverage frameworks and toolkits to develop Gen AI solutions from proof of concept to enterprise ready.
• Benefits at scale: Implement the right operating model, people, processes, technology, risk management, and controls to safely scale across the organization.

Finally, leaders in organizations will play a pivotal role in driving the adoption and implementation of Gen AI in supply chain scenario modelling. To succeed, leaders must prioritize the following actions: Championing change, defining a clear vision, and facilitating continuous improvement.

Final thoughts: The future of supply chain management

Whilst addressing two of the many supply chain opportunities in this report, it is clear that Gen AI will make an impact across the End-to End (E2E) chain. With that in mind, here are some parting considerations:

• The future of supply chain management belongs to those who are willing to embrace change and harness the power of AI and Gen AI.
• This is not just a technological challenge; it’s a leadership challenge. The companies that succeed will be those that have the vision, courage, and agility to transform their supply chains for the emerging age of AI.
• This is not just another tech trend; it’s a paradigm shift. Generative AI is democratizing access to sophisticated risk modeling capabilities, enabling even smaller companies to compete on a global scale with unprecedented resilience.

The future of supply chain management is here, and it is powered by AI and Gen AI. Leaders must seize this opportunity to transform their supply chains, ensuring they are not only resilient but also capable of thriving in an increasingly unpredictable world.

Capgemini has multiple frameworks and project blueprints to support and accelerate the development, deployment, and operation of Gen AI within supply chain. Contact our experts to learn more:

World leaders meet in Paris this week to discuss the future of AI. We need a shared vision for responsible use: both to maximize the benefits for society, and reduce AI’s potential for online harms.

While the emergence of advanced and highly capable artificial intelligence (AI) models and systems is likely to lead to huge benefits for society, it is also likely they will lead to large increases in online crime through the malicious misuse of AI tools, products and services, as well as potentially accidental harms (mistakes or unintended consequences from non-malicious actors). Of course, many different technologies can be exploited to cause harms as well as for their positive benefits, but often these harms are limited in their impact by requiring a high level of expertise, access to the technology, and money. In the case of generative AI however, these factors are much less constraining. We are now seeing the development of generative AI tools which are free or very cheap, very widely available online, and which have the potential to catalyse several forms of harm, including financial crime, cyber-attacks, and online targeting of individuals or groups (cyberstalking, harassment, or political disinformation). The barrier to entry for committing such crimes is now merely access to a laptop and wifi.

AI’s specific strengths in handling unstructured and variable datasets, data pattern recognition, replication at scale, tailoring content to the individual level, and predictive analytics, which are already being used by businesses to predict demand, anticipate user trends and identify gaps in offerings, make it uniquely valuable for all sorts of tasks, and also uniquely capable at being used as a tool to commit online crime. AI can facilitate the development of off-the-shelf “crime-as-a-service” products which vastly reduce the “barriers to entry”, for those so inclined. AI software can be used to conceal the perpetrator’s identity and location, making it hard to investigate. AI models can also themselves be targets for crimes such as hacking, through prompt injection for instance, and data manipulation or poisoning, causing them to make mistakes, or to react in specific ways given the right trigger. These types of manipulation would be of particular concern in areas of critical national infrastructure and autonomous weapons systems.

New potential for abuses of trust

Some of the most concerning types of harm which generative AI tools, such as social “bots”, may facilitate are crimes of persuasion and influence: exploiting an individual’s psychology or personal circumstances or actually affecting someone’s mental state to convince them to act in a way they may not have done otherwise, possibly using deepfakes (audio and visual media purporting to show a person or event that in reality never occurred), misinformation and disinformation. These effects could be exploited for deception, phishing, radicalisation and encouragement of social unrest. Early experiences show that our natural tendency to anthropomorphize means that people can become emotionally attached to AI-generated bots, divulge personal information to them, and that they may create an echo-chamber effect which normalizes harmful behaviours such as sex crimes. Even more subtly, the development and awareness of AI products generally creates an environment where people may expect or imply AI-related crimes, even if no AI was involved – for example threatening to use specific tools, making someone believe a certain effect was possible using AI even if it is not feasible, or making people believe a genuine video or photo was faked. It is also important to note that this type of criminality need not only be targeted at an individual. The capabilities of generative AI can be implemented just as easily for a large target audience, while still being personalized to the individual user to encourage their engagement.

There is also the potential for emergent criminal behaviours, as AI agents become more sophisticated and interact in increasingly complex ways. They may autonomously commit crimes going beyond the user’s initial expectations or moral compass. An AI system has no innate understanding of ethics, pain, truth, or compassion, and is without human limitations of strength, tiredness and pace. It may propose or take actions which are unacceptable, too complex to comprehend, or too fast to prevent. As AI agents start to be entrusted with acting autonomously on our behalf, we will need to incorporate increasing levels of safeguards to prevent them from over-reaching; but because deploying bots is cheaper and easier than implementing effective governance to control their actions, it is likely to be very difficult to monitor and mitigate all risks and impacts.

How can governments and business leaders take action?

As senior leaders gather in Paris to debate AI safety standards – amid a complex multi-national arms-race of AI development – they need urgently to discuss and agree measures to prevent AI-enabled harms occurring. These measures could be defined and coordinated across state boundaries, implementing governance in a similar way to global civil aviation, which is effectively governed by the International Civil Aviation Organization (ICAO), with state-level measures that ensure adherence to these global standards. The United Nations is well-positioned and prepared to coordinate the required oversight. Such measures need to be thought of in three layers:

1. Technical measures to prevent harm: removing any datasets from training data which contain harmful content; vetting datasets; fine-tuning models using reinforcement learning techniques to avoid harmful outputs; adversarial testing and evaluations; stress-testing to identify potential vulnerabilities such as prompt injection; developing explainable AI models; and guardrails to prevent certain types of output generation.
2. Organizational approaches to deter harm: minimum safety standards; terms and conditions; user verification; content moderation and screening (including AI tools to automate content identification and removal); watermarking, labelling and tracking metadata; tagging verifiable data; correcting or flagging fake news; user behavior analysis; blocking, alerts and reporting mechanisms; education, training and awareness; restricted access; governance policies including ethics; and developing appropriate and proportionate law, guidance and regulation.
3. Law enforcement responses to harm: monitoring and intelligence-gathering; detection tools (including AI tools to automate detection); investigatory processes; agreements with technology companies to access evidence; identifying high-risk individuals and communities; accessing technical skillsets; increasing capacity to address the growth in online harms; deploying counter-influence AI tools to mitigate the effects (for example redirecting to counter-radicalization resources); and working with technology companies to respond to emerging criminal behaviors.

In conclusion, generative AI tools can provide great benefits; but could also lead to exponential increases in online harms. Technology companies, governments, and law enforcement agencies are working together to anticipate, understand and prevent such harms occurring, but ultimately everyone will need to be conscious and responsible in their use of AI.

As we approach 2025, organizations face a pivotal moment—navigating uncertainty while leveraging Intelligent Industry to turn volatility into opportunities for growth and innovation.

Success lies in resilience, sustainability, and technology-driven transformation, where the convergence of digital and physical systems enables businesses to thrive in an evolving global economy.

The key to long-term success lies in compound thinking—a strategic approach integrating digital transformation, physical engineering, and sustainability. By designing intelligent, efficient, and adaptive systems, businesses can unlock sustainable value and drive innovation. Transformation extends beyond the physical and digital; it requires an attitudinal shift—embracing software-centric models and force-multiplying solutions that create a lasting impact.

From Smart to Intelligent Products, Operations, and Services

Industrial companies are under mounting pressure to develop and deliver increasingly complex products at unprecedented speed and efficiency.  Manufacturing is shifting from traditional, linear processes—where humans direct machines—to dynamic, multi-directional models.   In this new paradigm, consumers demand directly influences production triggering automated manufacturing systems powered by Industrial IoT and AI-driven orchestration. This transformation extends beyond factory floors, shaping fully integrated supply chain ecosystems that optimize logistics, enhance responsiveness, and enable autonomous, robotic warehousing.

Imagine a factory that seamlessly adapts to market shifts in real time. The shop floor becomes a collaborative hub, where suppliers, engineers, and AI-powered systems work in sync to optimize efficiency. Technologies like Digital Twins, the Industrial Metaverse, and Software-Defined Factories bridge the digital and physical, fostering continuous innovation and agility across the entire manufacturing process.

To scale digital strategies effectively, companies must move beyond isolated pilots and siloed implementations. Seamless integration across product development, manufacturing, and operations is the key to greater flexibility, resilience, and sustainability. As industries accelerate toward software-defined products and services, operational agility will be the key to thriving in an era of increasing complexity, speed and sustainability demands.

Breaking Down Silos: The Convergence of Digital and Physical Realms

To drive cross-disciplinary collaboration and unlock new efficiencies, industrial leaders must break down traditional silos and merge the digital and physical worlds.  This convergence fosters creativity, sets new industry benchmarks, and accelerates innovation.  AI, edge computing, and software-defined architectures are rapidly redefining operational excellence. These technologies will power the next generation of Intelligent Industry, enabling smart factories that are more flexible, cost-effective, and sustainable.

Data First: The Foundation for Scalable Solutions

In today’s fast-moving digital economy, data is more than an asset—it is the foundation for intelligent, scalable solutions.  Organizations must adopt a data-first approach, leveraging virtual models of physical systems to enhance efficiency, drive innovation, and support long-term sustainability. Data-driven frameworks improve collaboration, agility, and real-time decision-making, enabling businesses to proactively shape the future rather than simply react to change.

The rise of Artificial Intelligence (AI) and agentic AI presents both challenges and opportunities. While these technologies streamline operations and optimize human-machine interactions, they require a fundamental shift in organizational processes and decision-making. Companies that integrate AI-powered analytics and automation effectively will gain a distinct competitive advantage, improving responsiveness, efficiency, and scalability.

To stay ahead in an increasingly complex landscape, enterprises must embrace agile, digital-first solutions that optimize workflows, accelerate time-to-market, and improve cross-functional collaboration. Model-driven methodologies, such as Digital Twins and Product Passports, enable organizations to simulate, test, and refine solutions before physical implementation—ensuring efficiency while aligning with ESG and sustainability goals. By harnessing these advanced technologies, businesses can build intelligent, connected ecosystems that foster innovation and long-term value.

Driving Efficiency Through Legacy Modernization

Digital transformation is the key to smarter, greener manufacturing. While investment levels vary, digital manufacturing initiatives typically account for 15-25% of total asset bases, with a significant portion focused on modernizing brownfield sites. Despite their challenges, these sites hold immense potential for transformation.

According to the Capgemini Research Institute’s report, The Resurgence of Manufacturing: Reindustrialization Strategies in Europe and the US, 60% of reindustrialization strategies in these regions focus on brownfield approaches. Although brownfield factories face unique hurdles, digital transformation is turning them into smarter, greener, and more resilient operations.

Cutting-edge technologies—AI, IoT, robotics, and digital twins—are revolutionizing legacy manufacturing by enhancing efficiency, reducing waste, and improving supply chain transparency. While new factories seamlessly integrate these innovations, brownfield sites must undergo legacy system modernization to remain competitive.

The impact of digital manufacturing is profound:

• AI-driven analytics optimize production processes.
• Predictive maintenance minimizes downtime.
• Automated energy management reduces emissions.
• AI-powered monitoring ensures consistency and quality.
• Robotics and AR solutions enhance productivity and safety.

A Roadmap for the Future

By adopting compound thinking and continuous innovation, organizations can transform today’s challenges into opportunities for growth. A commitment to sustainability, intelligent products, and adaptive operations lays the foundation for a resilient future—one where businesses drive progress and shape a thriving global economy.

Software-driven processes and products are key to increasing flexibility and efficiency while seamlessly integrating IT and OT. As industries continue their journey toward digital transformation, those who embrace agility, collaboration, and innovation will lead the way in defining the next industrial revolution.

The explosive advancements in Generative AI (Gen AI) are awe-inspiring and daunting.

The world has never seen technologies with such transformative potential. They promise to reshape our reality, companies, and world to the core. In the Gen AI race, whichever software or platform company can provide Gen AI-driven experiences into their products first and best will win. Software engineering leaders have a pivotal role in this wave of disruption; keeping pace is essential and challenging, especially with the speed of innovation, tightening budgets, and a talent shortage.

Challenges of software engineering leaders

In Gartner’s 2021 Software Engineering Leader survey, “hiring, developing, and retaining talent” was one of the top three challenges for a whopping 38% of software engineering leaders. The other top challenges were “reducing time to market” and “constant disruptions due to unplanned work.”

Further, a separate Gartner study indicates that organizations with high-quality developer experience are 33% more likely to attain their target business outcomes, and developers with good developer experience are 20% more likely to have higher job satisfaction and engagement. Finally, a good developer experience improves developer productivity by 31%.

Software Engineering leaders who overlook developer experience risk losing their top talent, hurting software delivery velocity, and compromising quality. To meet core business goals—high-quality product innovation, time to market, and product growth and adoption—engineering leaders must prioritize and optimize developer experience.

What is Developer Experience?

Let’s understand what developer experience means. Per Gartner, developer experience refers to “all aspects of interaction between developers and the tools, platforms, and people they work with to develop and deliver software products and services.” A superior developer experience requires an environment where developers can do their best with minimum friction and maximum flow.

Today, software development teams navigate an increasingly complex environment with various tools, technologies, architectures, and processes across the software delivery life cycle (SDLC). This complexity often increases developers’ cognitive load, limiting their ability to deliver value. Investing in developer experience enables focus on high-value work with minimal distractions and empowers developers to be in the “flow of value” and “flow state.”

Gen AI-Driven productivity across software development life cycle (SDLC)

Augmented software engineering, and Gen AI in particular, can assist software engineers throughout the SDLC to drive productivity across the “inner and outer loop” of software engineering. Gen AI-augmented software engineering promises to improve developer productivity and operational efficiency by augmenting every software development life cycle phase.

A recent Capgemini Research Institute report shows that 69% of senior software professionals report high levels of satisfaction from using generative AI in software.

To drive the most significant gains in developer productivity, software engineering leaders must see developer productivity as more than just time savings and increased value delivered. The developer productivity goes beyond tasks like coding or testing. It also shapes developer satisfaction, well-being, effective communication and collaboration, and the ability to maintain an efficient flow state . This more profound understanding of developer productivity led GitHub researchers to develop the SPACE (Satisfaction and well-being, Performance, Activity, Communication & Collaboration, and Efficiency & Flow) framework, categorizing the key elements influencing developer productivity.

Linking Empowering Developers to the SPACE Framework

Software engineering leaders have a unique opportunity to harness the potential of Generative AI tools to drive meaningful improvements in developer empowerment and productivity. By focusing on critical aspects outlined in the SPACE framework—satisfaction &well-being, Performance and Activity communication and collaboration, and efficiency and flow—leaders can significantly enhance the developer experience. These improvements can compound the benefits of Gen AI, ultimately leading to greater productivity and innovation across engineering teams.

To do this, we believe Software Development leaders can group the opportunities of Gen AI into three groups: 1) Productivity 2) Developer Thriving, and 3) Valuable Outcomes.  The diagram below depicts our perspective on mapping the SPACE framework to the three opportunities

1. Productivity
– Activity & Performance

According to the Capgemini Research Institute, organizations with active generative AI initiatives have seen an average 7% to 18% improvement in productivity across the software development lifecycle, and a study from MIT showed an improvement of up to 40%.

Gen AI capabilities can be integrated into every phase of the SDLC, from business requirement analysis and user stories to software design, coding (including retro documentation), packaging, deployment, testing, and monitoring. All of these integrations have the potential for Time Saving. These benefits can be realized across all of the roles in the SDLC, including data analysts, business analysts, platform/software designers, and software engineers/developers/testers.

While Gen AI can be infused across all the stages of the software lifecycle to drive time savings, organizations must prioritize use cases that offer the highest benefits to fully harness AI’s potential in software engineering. This focus ensures that resources are directed toward initiatives that boost productivity. By targeting high-impact applications, organizations can maximize their return on investment in Gen AI and stay competitive.

Source File : Turbocharging software with Gen AI

2. Developer Thriving
– Satisfaction and Well Being, Communication and Colloboration, Efficiency & Flow

Gen AI tools, such as GitHub Copilot, have already demonstrated their ability to enhance developer satisfaction. According to a GitHub survey , more than 60% of developers who used Copilot reported improved levels of satisfaction and well-being. While these tools do not directly create well-being, they reduce the burden of repetitive and mundane tasks, such as writing boilerplate code or generating routine documentation. By automating these tedious tasks, developers can focus on more engaging and creative work, which increases overall satisfaction.

Effective communication is crucial for software engineering teams, especially as global and remote work grows. Gen AI tools enhance collaboration by refining written communication and automating tasks for smoother interactions. Tools like GrammarlyGO and GPT-4 can convert conversations into text, summarize discussions, and manage real-time updates.

Gen AI also helps teams write better user stories, generate documentation from source code, and improve translations for international collaboration. These enhancements reduce effort and sharpen communication, helping developers understand user requirements and deliver more valuable software. For instance, Gen AI aids developers in conveying complex technical concepts clearly, reducing misinterpretation. Gen AI further boosts efficiency by reducing cognitive fatigue and context switching. Context switching occurs when developers are forced to switch between tasks or tools, disrupting their concentration and reducing productivity. Tools like GitHub Copilot and CodeWhisperer keep developers in their flow, providing in-line assistance and quick access to information within their workspace. This seamless integration minimizes disruptions, enabling focused, efficient work and higher productivity.

3. Valuable Outcomes
– Innovation

The third dimension of Value Outcomes or Innovation measures how developers utilize their productivity gains. The impact of Gen AI on productivity gain is not just tracking specific metrics or the number of hours reduced for given tasks but also creating space for creativity and innovation, enabling developers to dedicate their talents to high-value, strategic tasks that pave the way for innovation. Freed from repetitive tasks, developers can pivot toward high-value work, focusing on architecting complex systems, developing novel features, and tackling ambitious projects to create solutions that directly impact the business and customer satisfaction. This shift enables a richer use of developer expertise and fosters an environment where meaningful, creative work takes precedence. Developers can experiment with groundbreaking ideas and innovative designs that may have seemed unattainable before. For instance, Gen AI allows developers to quickly prototype ideas, receive instant feedback, and iterate on complex features. The rapid feedback loop made possible by Gen AI fuels a culture of experimentation and innovation, enabling engineers to test new concepts and technologies with minimal risk or cost.

Ultimately, Gen AI is laying the groundwork for the next wave of software innovation—one that will shape the future of technology in ways we can only begin to imagine today.

Conclusion

Integrating Gen AI into software development offers the extraordinary potential to drive a more positive developer experience for productivity and innovation. As the technology evolves, so will its application within software engineering. Leaders who strategically invest in developer experience and harness Gen AI for satisfaction, performance, activity, collaboration, and efficiency are set to drive the next wave of product innovation, positioning their teams—and organizations—at the forefront of innovation. 

Part of the Empowering Developers with Gen AI Series

Being in San Francisco, this vibrant and living city, full of ideas and willingness to move onward

I could not help myself getting into a thought or two on the sociology of trends and commercial interests. In our current society we often stick to verification and confirmation to shape our ideas and opinions. Social media, news, and opinion platforms tend to reinforce our beliefs, while even academic resources like Arxiv.org can unintentionally contribute to this echo chamber by not being peer-reviewed. This trend is definitely visible in the AI industry, where big ideas, big money, and authoritative messaging can overshadow the importance of critical evaluation and scientific inquiry. Instead of acknowledging and solving observed problems, issues with AI are made undiscussable through arguments like: “why are you so negative? The issue will be solved if we only have more (more data, more time, more compute, more money, more whatever).”

As a seasoned AI scientist, I am concerned that our collective focus on confirmation may hinder progress in a field full of (real) potential. And that feeling gets stronger when walking in San Francisco, between richness, beauty, tents, people sleeping in cars while they have no housing, super sport cars and fentanyl junks besides those. The glass of the Tech Scene here always seems to be half-full, but the issues you get when really applying the tech seem to get marginalised (just as the problems you see around you on the streets) . Our eagerness to show that a method works can lead us to overlook underlying issues that need addressing. When we become too attached to our hypotheses, we may fail to consider alternative solutions or improve upon our existing models.

The consequences of such a confirmation bias are significant. We may set unrealistic expectations for people around us, we may start to believe in our own biases, all this leading to disillusionment when those expectations are not met. This pattern is all too familiar in the world of AI, and I fear we are on the brink of another wave of disappointment with generative AI if we do not count up all warnings we currently get.

To break free from this cycle, we must adopt a more scientific approach. Only if we start to acknowledge issues and begin to document our findings, clearly identifying where things go wrong instead of waiting for the “next round” of deliveries in the form of new “black boxes”, we might start to really implement value. By doing so, we can ensure that our AI solutions are grounded in evidence, reproducible, and open to falsification. This approach encourages us to challenge our assumptions, explore alternative solutions, and continuously improve upon our work.

By implementing the ways of work of scientific methods, we can build AI solutions that are not only innovative and efficient but also robust, reliable, and responsible when applied in practice. As we navigate the ever-evolving landscape of AI, let us commit a culture of documented inquiry and evidence-based decision-making. But what´s more: it will force more openness in development of new AI technology. Instead of beginning with large concepts and substantial investments to block competition, which might lead to developments following a likely unfavorable path, we should more openly explore, implement, and test new directions through open and proactive involvement early on. Doing so will lead you to the right use cases to tackle with the AI technology currently around you, instead of chasing the gold pot at the end of the rainbow, creating overinflated explanations and hinder positive sides of AI to be implemented.

The future is agentic! Check my article on AI agents and agentic workflows.

Experts are highlighting the immense potential of these advancements, which could even surpass the impact of the next generation of foundation models.

Key takeaways from the article:

• Agentic workflows empower AI agents to engage in a more dynamic, iterative, and self-reflective process, unlike traditional “non-agentic” approaches.
• AI Agents can leverage powerful design patterns like Reflection, Tool Use, Planning, and Multi-Agent Collaboration to drastically improve their performance and capabilities.
• Adopting Agentic systems holds significant implications for software development, business strategy, and the overall trajectory of AI.

Why Google Search is End of Life?

I think the biggest change that Google is a more holistic one that most people missed because of the focus on AI.

Namely Google is shifting from Search Engine to “Content Creation Engine”…. this is a massive change, I think this will become more and more visible in the next 18 months as they begin adding more and more features to their Google products…

If you look at the 100 new things that have been announced during Google I/O 2024 and look closely to the tools, you see how Google is going to make the change in the next years. The tools will enable you to create more faster and easier in an engine that you are already have been using for years.

Stay curious. And have fun exploring these new 100 tools.

All debates around Gen AI made me realized that the fact that it is creativity that debate circles around (picture generation, texts, sounds, music), i.e. all that we regarded as “human” traits, and that would be the last ones to be automated.

Before that we would replace all “boring jobs”, all routine, etc. Now those jobs seem, in many cases, to be quite difficult actually.

Because they require context, common sense reasoning and often planning. Like cleaning your room. Like driving a car in Lima, Peru or Oslo. Like performing bureaucratic tasks which involves or have to adhere to laws, regulations, etc.

All this made AI to go from “technical” (give me your data and we build a model and run it, update it. full control in all processes) to something that involves language, human communication, creativity and thus sociology started to play a role. And psychology. And even geo-politics become part of the discussion.

Today I had the chance to discuss these topics at the core of Dutch politics, with Barbara Kathmann and Ufuk Esmer in the building of the “Tweede Kamer” (House of Representatives) in Den Haag. One thing we definitely agree on is that it is long overdue that there is a public debate on Artificial Intelligence, benefits, but also its place, its needs and its risks in society.

We will have a more in-depth debate tomorrow at our Capgemini Leidsche Rijn offices on this. Looking forward to it!

(And the state of the public digital services, ref picture from an information stand, really shows the need for a good vision and strategy on digital services).

The current spike of Gen AI is prompting more and more people to wonder when AI will surpass human intelligence (and in many disciplines, the bar is already passed).

Interestingly, this is a debate which is far from new. Actually, as early as 1950, one of history’s most brilliant minds published a scientific paper in which he posed the question ‘’Can machines think?’’. This was Alan Turing, who would later become famous for the ‘’Turing Test’’ to determine a machine’s ability to exhibit intelligence. Turing also invented one of the first computing devices in history (the Turing machine) by the age of 24.

But it was during World War II that Turing made some of his greatest contributions to humanity. Working at the Allied codebreaking center of Bletchley Park, Turing and his team managed to crack the Nazi Enigma code, which significantly contributed to the Allied victory.

Even after the war, he made lasting contributions to the fields of mathematics, biology and what would eventually be called Artificial Intelligence.

But despite all of this, Turing was persecuted and even put on trial for homosexuality. He ended up committing suicide in June 1954 (although there is still some debate whether his death might be an accident).

Alan Turing’s legacy is vast and multifaceted. His life story is also a powerful reminder of the consequences of prejudice. As Pride Month comes to a close, it reminds all of us of our responsibility to build, protect and defend an inclusive work culture in our organizations, empowering talented minds like Turing to thrive regardless of our respective gender identities and sexual orientations.