CIO Influence Interview with Kendra DeKeyrel, Vice President of ESG and Asset Management at IBM

Kendra DeKeyrel, Vice President of ESG and Asset Management at IBM, talks about the latest trends in asset management, factors to consider when developing and launching a new software product in this Q&A: 

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Hello Kendra, welcome to our CIO Influence Interview Series. Please walk us through your SaaS and tech leadership journey as VP ESG & Asset Management Product Leader at IBM.

Hi, happy to be here! I’ve been at IBM for over 20 years and held multiple roles during this time. Currently, I’m the VP of our ESG and Asset Management portfolio, where I get to collaborate with diverse teams to develop innovative software solutions that drive efficiency, reduce environmental impact, and create lasting value for organizations around the globe. One thing I’m passionate about – and that I get to help support in this role – is finding new ways in which organizations can leverage technology to build a more resilient and environmentally conscious future.

Previously, I managed and shaped the Director of TRIRIGA, Watson Assistant and Digital Business Assistant. Prior, I led the IBM Digital Business Assistant, served as Chief of Staff for Marie Wieck (GM for IBM Hybrid Cloud) and many more.

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What are some of the latest trends in asset management that you believe will significantly impact the IT sector in the next few years, and how is IBM positioning itself to lead in this space?

Organizations in asset intensive industries are facing new obstacles such as: the cost of unplanned downtime is increasing, skills shortages are hurting productivity, and traditional maintenance practices are becoming obsolete in the current business landscape, among others. This is leading to an industry-wide shift from enterprise asset management (EAM) to asset lifecycle management (ALM). Adopting ALM involves combining a range of strategies designed to extend the lifespan of assets and increase their efficiency for a more comprehensive approach to strategic asset management.

As a result, organizations that implement ALM systems will go well beyond traditional planning, tracking and execution of maintenance. The tailwinds behind ALM suggest this trend will continue for the foreseeable future, as further advances in technology and AI make ALM more accessible and valuable for organizations of all sizes.

At IBM, we already offer technology within our portfolio that can help clients extend the lifespan of their assets and improve productivity and reliability, while reducing costs and advancing decarbonization. For instance, Transport for London (TfL) was able to optimize the city’s public transportation vehicles by leveraging IBM Maximo Application Suite to proactively mitigate issues and extend the lifecycle of existing assets; reducing the need for replacement parts and limiting the risk of catastrophic failures. TfL estimates net savings of GBP 21 million over the next decade solely for its London Underground responsibilities.

Another trend that will impact the IT sector is the rise of generative AI. As it becomes more prevalent and easily available, businesses will want to explore gen AI and find ways to leverage it to streamline their operations and lead to better results in their bottom line.

This trend stems from AI’s ability to help us automate processes, optimize resource management, and provide predictive analytics that drive greater efficiency. Now, generative AI will bring this one step further and allow organizations to tackle new specific use cases.

We are working with our clients to deploy various applications of this technology, such as in asset management and strategic planning. By doing so, they aim to reduce operational costs, minimize errors, and make data-driven decisions with greater precision to support their sustainability efforts. Overall, the adoption of generative AI is expected to transform business practices and deliver substantial competitive advantages in a rapidly evolving market.

Can you talk about the latest initiatives at IBM that have successfully combined technology with sustainable practices?

Sustainability is good for the environment and also for business. According to our recent industry research, organizations that embed sustainability more deeply into their operations perform better financially and on progress towards sustainability. For these reasons and more, at IBM we are exploring ways to leverage technology like AI help supercharge their efforts to make the progress that we need.

For example, earlier this year we released version 9.0 of Maximo® Application Suite with new innovations that help to optimize asset lifecycles for more sustainable business practices. One of these new features is Maximo Emissions Management which helps businesses balance operational efficiency with environmental responsibility by allowing them to monitor continuous and fugitive emissions in near real time and manage compliance programs. The more information businesses can gather about individual assets, including emissions, the more they will be able to make informed decisions to operationalize sustainability. We are also working towards interconnecting different solutions in our portfolio to provide clients with all-encompassing solutions for their rising ALM needs. Emissions management is one example, as it supports operational emissions reporting and strengthens corporate sustainability tracking through an integration with IBM® Envizi™ ESG Suite.

We are also developing even more new technology that help businesses build climate resilience. Environmental Intelligence is our new cloud-based data platform that empowers data scientists and application developers with environmental data and insights that can help them build solutions to mitigate the environmental impact on their assets and build more climate-resilient operations. This powerful combination of technology can unlock a more efficient, responsible and sustainable approach to business operations.

Most recently, we announced our collaboration with JLL to bring a new ESG Reporting and Data Management Solution to the commercial real estate sector. This new solution utilizing Envizi technology will help companies across the commercial real estate sector capture and manage data across multiple portfolios for easier decision-making and public reporting to support their decarbonization efforts.

These are some of many instances in which at IBM we continue to develop new and innovative technology supercharged by AI and insights to supercharge our clients’ sustainability journey.

What role do AI and machine learning play in enhancing IBM’s asset management and sustainability solutions?

AI is already helping businesses unlock opportunities to minimize maintenance costs, optimize physical assets, and contribute to sustainability and energy cost goals. However, generative AI has the potential to become a game changer for driving an organization’s asset management efforts and overall sustainability journey. For context, “traditional” AI focuses more on analysis of data, whereas generative AI produces brand-new content such as text, images, etc. that are often indistinguishable from human-generated content.

At IBM, we are exploring ways to embed gen AI in our offerings, allowing our clients to begin reaping the benefits. Maximo work order intelligence, for example, is currently available and offers businesses the ability to apply IBM watsonx™generative AI capabilities for specific use cases within asset management. This feature can result fewer maintenance errors, avoiding unnecessary operations, lowering material costs and saving maintenance personnel time to help businesses better prioritize their resources.

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Additionally, we are developing new use cases for generative AI in sustainability through our geospatial foundation model. This model can be fine-tuned to track deforestation, detect GHGs, or predict crop yields for example. This type of model can help by identifying and analysing data, surfacing trends such as where and why populations are moving, provide insight on how to serve them with renewable energy, and also estimate where carbon is stored, how long it will take to degrade, and more.

Ultimately, by bringing together generative AI, Internet of Things, environmental insights and our existing platforms, IBM is “leading the pack” by helping co-create a more automated and sustainable future for business.

In your extensive experience with enterprise software product management, what are the most critical factors to consider when developing and launching a new software product?

There are several critical factors that should be considered when developing and launching a new software product. Firstly, understanding and identifying market gaps is crucial for uncovering unmet needs and emerging trends.

For us, it is essential to co-create with our entire innovation ecosystem — clients, partners, industry leaders, and others— to design solutions with greater impact. Equally important is ensuring that the product aligns with our strategic vision and leverages our existing technological and consulting strengths, as well as our experience and industry knowledge —both IBM’s and our partners. This means integrating cutting-edge technologies like AI and generative AI to enhance the product’s capabilities and ensure it delivers tangible value to our clients.

Another key factor is maintaining a strong focus on user experience and usability. Our solutions must not only be technically robust but also intuitive and easy for users to adopt and integrate into their workflows. The IBM Cloud Carbon Calculator is a great example of this, as it can help enterprises track greenhouse gas (GHG) emissions across cloud services and advance their sustainability performance throughout their hybrid, multicloud journeys. It also complements our existing portfolio of sustainability solutions and consulting expertise that help organizations set, operationalize, and achieve their environmental sustainability goals.

Effective collaboration across teams is also essential. IBM boasts large and experienced research and product development teams, as well as professionals specialized in various technologies and industries. These groups, together with sales and support personnel, work closely with clients to ensure that the product is well-supported throughout its lifecycle and meets customer expectations.

By carefully balancing these factors and implementing a robust go-to-market strategy, we can develop and launch products that meet the evolving needs of our clients and drive significant business outcomes.

Before we wrap up, what is your take on the future of AI-driven asset management and sustainability solutions in 2024?

Organizations need to take concrete actions to strengthen their sustainability journeys and operational excellence, but they face various challenges that must be resolved quickly and effectively. At the same time, we are in a dynamic and exciting period where AI-powered solutions are scaling rapidly and achieving impressive results, with use cases that have proven highly successful.

To address these challenges, companies must leverage technology that can provide intelligent and comprehensive asset management while also embedding sustainable practices. In order to get started on this process, I encourage organizations to assess the data they currently have, clean up what they don’t need, and start the journey of using AI as a valuable ally in their sustainability journey. The positive results of this combination are tangible: an IBM Institute for Business Value study found that organizations that embed sustainability are 52% more likely to outperform their peers on profitability.

At IBM, we are already helping our clients find the right solutions for their asset management needs while simultaneously exploring the future of ALM. One way in which I foresee ALM evolving is by finding more opportunities to integrate AI. Data is what makes the sustainability journey possible, and AI will supercharge that journey by helping companies make better decisions about their assets, faster.

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Additionally, the rise of generative AI will be key for organizations to transform how they work and operate their businesses. For context, “traditional” AI focuses primarily on data analysis, whereas generative AI is producing brand-new outputs that are expected to further enhance efficiency and productivity.

IBM is active in both spaces. For instance, we are currently using AI to auto classify scope 3 emissions categories with IBM Envizi Supply Chain Intelligence. Using AI to automatically classify work orders, as mentioned above, is another straightforward example that can save hundreds of hours of work. When it comes to the future of AI, we are exploring new applications of climate risk data, which provides customers with the ability to forecast climate related risk associated with physical assets and to inform investment decisions.

Within IBM portfolio, our AI-infused technology helps organizations extend the lifespan of their assets and improve productivity and reliability, while reducing costs and advancing decarbonization.

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