Business to Planet Connect 2024 explored the roles of policy, investment, and tech innovation for bold action and future resilience

An urgent call echoed throughout New York last week: it’s time for climate action.

On September 25, 2024, hundreds of people gathered at The Glasshouse in Manhattan for Capgemini’s special event examining how organizations can join the clean-energy revolution by embracing sustainability as an enabler, rather than a blocker, of value.
Capgemini’s Business to Planet Connect was part of Climate Week NYC, a key international summit for business, government, and civil society. It was hosted by Climate Group and held alongside the United Nations General Assembly.
The general sessions at Business to Planet Connect set the tone for the specialized panels to come later in the day. The morning’s speakers hit upon the obligation to address climate change, the collaboration between policymakers and investors, the technologies that are revolutionizing sustainability efforts, and the opportunity to innovate.

Empowering nature’s voice: Companies must prioritize climate up to the board level

Two climate action leaders and sustainable business-strategy pioneers – ClimateVoice Founder and Former Executive Director Bill Weihl and Natural Logic Founder and CEO Gil Friend – delivered an impassioned keynote. They argued voluntary corporate action is admirable but insufficient, and that economy-wide policy changes are necessary. This will require supportive public policies that encourage effort at scale.

Weihl, who lost his voice to ALS, spoke to the crowd through an AI-powered voice clone developed by ElevenLabs. He used his personal experience with the condition as a metaphor for climate change: both are dire and progressively worsening. However, there is no cure for ALS, whereas solutions exist that could address up to 90 percent of the climate crisis, according to Weihl.
“[With ALS], I can personally hope for a miracle soon. But the big challenge is finding equanimity and acceptance,” Weihl said. “With climate, we can all hope, but hope is not a plan. Instead, we collectively can and must adopt and deploy the solutions we have much faster, while we continue R&D on the areas where we don’t have good solutions yet. That word ‘collectively’ is key.”
Friend called for businesses to shift their focus from maximizing shareholder returns to belonging to the living world. What would happen if companies expanded their sense of fiduciary responsibility, the obligation to act in a client or beneficiary’s best interests, to encompass nature and climate?
He argued that willful disregard of meaningful material information (e.g., environmental impacts, sustainable alternatives) result in business practices that “verge every day on violation of fiduciary duty to shareholders.”

Aligning investments, policies, and technology: Collaboration is crucial for future resilience

The first panel discussion explored the role of private and public partnerships in meeting evolving regulations and stakeholder expectations, as well as the intricacies of aligning investments, policies, and technologies.

Christophe Defert, Head of HSBC Asset Management’s climate tech venture; Adam Schafer, Head of Supply Chain Strategy at CHIPS for America in the US Department of Commerce; and Daniel Doimo, CEO of Solarlytics, agreed that success in sustainability requires collaboration between policymakers, investors, and industry players across the entire supply chain – upstream and downstream.
“Collaboration is the only way to actually [effect] any meaningful change there, whether it’s from a policy standpoint or an accounting standpoint,” Schafer said. “[This includes] every actor in that very complicated value chain, right through to the end consumer.”
Private investment is crucial for channeling funds to sustainable technologies but it’s still difficult to align long-term investments with short-term investor expectations. Under the current conditions, the investment industry needs innovation and stable policies to support long-term goals, while governments help identify and mitigate risks throughout complex, global supply chains. In short, everyone has a role to play.

Generative AI: Accelerating sustainability through responsible innovation

The second panel was dedicated to resolving the inherent tension of Gen AI for climate action. On one hand, its capabilities promise to streamline and optimize sustainability initiatives. But on the other, AI’s colossal energy and resource usage leaves a massive environmental footprint – exactly what climate champions are trying to avoid.

Rachel Delacour, Co-Founder and CEO of Sweep; Dan Versace, Senior Analyst for ESG Services at IDC; and Mohammed Abdelhadi, Director of Cloud for Sustainability at Microsoft, discussed how Gen AI is being used to address the global climate challenges outlined in the UN’s Sustainable Development Goals (SDGs), while emphasizing the importance of establishing guardrails against unintended consequences like bias and carbon impact.
There’s tremendous potential for AI to help governments and companies manage the effects of climate change with improved risk modeling; enable precision farming by optimizing planting schedules, fertilization, and water usage; and combat inequity by democratizing tech resources, particularly in underserved communities. But society will need to simultaneously develop new technologies and methods for reducing AI’s impact.
Abdelhadi explained that Microsoft, a key player in this technology, is working on several initiatives to reduce the impact of AI, such as developing solutions for optimizing energy and water usage for data centers.

Obligation or opportunity? The road ahead for the automotive industry

The final panel of the general sessions focused on two aspects of sustainability: the obligation to reduce environmental impacts and the opportunity to lead the charge toward a circular economy.

Bill Combs, Vice President of Sustainability at Penske; Nitin Tyagi, Vice President of Supply Chain at Our Next Energy (ONE); and Stephen Snyder, Chief Strategy Officer at Princeton NuEnergy, explained that the automotive industry must reduce carbon emissions and that various innovations (e.g., renewable energy, battery recycling, resilient supply chains) could create more sustainable business models for the future.
Acknowledging Penske’s responsibility stemming from its size and global influence across transportation, Combs outlined how the company is addressing sustainability, including renewable fuels and EVs for its fleets, circularity in vehicle maintenance, and solar energy to power facilities. He said it’s important for Penske to learn about sustainability in one business line and then “copy and paste” the implementation for other initiatives across the portfolio.
Tyagi was proud to share that ONE is shifting to phosphate-based batteries to avoid the ethical and environmental challenges associated with critical minerals like cobalt. By using batteries with more abundant and less controversial materials, ONE hopes to localize the battery supply chain, which would be more resilient to geopolitical risks in addition to reducing emissions.
Snyder described the concept of direct recycling, in which used battery materials are not melted down but instead cleaned and reused, much like washing dishes. This process lowers costs and waste, making the entire lifecycle of batteries – from production to disposal – more sustainable.
“I don’t know of any auto manufacturer that isn’t focused on per unit costs – batteries, tires, gauges, anything,” Snyder said.

The time is now: Let’s seize the chance to be part of the change

Several of Capgemini’s business and sustainability experts moderated the discussions: Sol Salinas, Sustainability Lead for Americas and Global Accounts; Vincent Charpiot, Head of Group Sustainability Business Accelerator; Christopher Scheefer, Group Data & AI Sustainability Lead; and Laurence Noël, Head of Global Automotive.

Each provided insights throughout and supported spirited, informative dialog. Each conversation promoted the adoption of a “Business to Planet” mindset, which sees beyond business-as-usual and charts a path toward a greener future.
Many organizations have taken significant steps toward climate action in recent years but, as we continue to grapple with sky-high greenhouse gas emissions and rising global temperatures, it’s clear that humankind is not moving quickly enough.
That’s why Climate Group settled on “It’s time” as the theme for this year’s Climate Week. That sentiment was heard within The Glasshouse and throughout the city. We need definite action. It’s time to phase out fossil fuels. It’s time to invest in clean energy. It’s time to embed sustainability into government policies.
It’s time.

Ever thought about the environmental impact of your IT infrastructure? At AWS re:Invent 2023, we explored the intersection of cloud and sustainability in our podcast episode.

Technology is well and truly opening doors to a more eco-friendly future, as highlighted in last year’s Cloud Realities podcast episode at AWS re:Invent in Las Vegas. Our Cloud Realities team hosted Rahul Sareen, Global Head of Sustainability Solutions at AWS. In this episode, he shared how AWS is using its massive infrastructure and technology to promote sustainability in the tech world. This blog is generated from the insightful podcast episode.

The AWS journey to Net Zero: Setting the scene

AWS is not a small player in the tech world. With 240 fully featured services and a presence in 33 geographic regions, AWS aims to achieve net zero carbon emissions by 2040, ten years ahead of the Paris Agreement. This goal covers all parts of Amazon’s business, including Prime, Amazon Web Services and devices. To reach this target, AWS is working on several key areas:

• More energy-efficient data centers; finding new ways to reduce the energy consumed by its servers and racks.
• Water positive by 2030; meaning they will return more water than they use, working closely with local agencies to achieve this goal.
• Energy-efficient chips, such as Inferentia for machine learning and Graviton for general computing. The Graviton4 processors reduce carbon intensity by 40-50%.

AWS aims to use 100% renewable energy for all of their operations by 2025.

Sustainability in the cloud: a team effort

In the podcast, Rahul Sareen highlights that AWS’s internal efforts are just one piece of the puzzle. He stresses that achieving sustainability is a team effort. Partnerships are crucial for scaling solutions, guiding customers, and navigating complex regulations. AWS works with a wide range of partners, including ISV providers who create specialized sustainability tools and global system integrators such as Capgemini, who offer strategic consulting and implementation expertise. This collaborative approach helps AWS reach more people and support organizations of all sizes in adopting sustainable practices.

The impact of generative AI on sustainable practices

Rahul Sareen talks about how AI, especially generative AI (Gen AI), can tackle sustainability challenges. Gen AI can help with complex regulations, make ESG reporting easier, and even predict natural disasters to help us prepare better.

• ESG reporting: Gen AI simplifies the process of following ESG regulations and ensures accurate reporting.
• Building efficiency: AI and ML models can adjust HVAC systems based on occupancy and weather, optimizing energy use in buildings.
• Climate risk management: Gen AI can predict climate-related risks, such as wildfires and floods.

Rahul emphasizes that sustainability is more than just reporting metrics; it’s about taking real actions to reduce environmental impact.

Capgemini at AWS re:Invent 2024 –
Scale, meet vision.

AWS’s commitment to sustainability, is a clear demonstration of how technology can drive significant environmental change. By leveraging the power of cloud computing, AI, and strategic partnerships, AWS is not only scaling its operations but also reaching a vision for a more sustainable future.

At AWS re:Invent 2024, join our line-up of experiences and networking opportunities, so together we can drive sustainable business success with innovation.

Create a sustainable future, powered by green technology

Climate Week NYC is an opportunity for corporate executives, thought leaders, and change makers to connect and reflect on ways to drive climate action forward to create a more sustainable future.

At our exclusive event, Business to Planet Connect, we discovered how organizations are fueling the green energy revolution by investing in sustainable innovation. We navigated the intricacies of environmental policy and how generative AI helps organizations address sustainability challenges.
We also heard unique perspectives from industry leaders across sectors, shining light on issues like transforming the food ecosystem, supporting electric vehicles and the hydrogen economy, and building sustainable supply chains.

In case you missed it, or want to learn more, we invite you to check out our collection of insights from the event. Explore complete coverage and more in-depth perspectives from the most impactful discussions throughout the day. We look forward to creating a more sustainable future and reinforcing our commitment to the journey from Business to Planet.

Seven ways the latest solutions in AI and machine learning revolutionize how auto dealers approach financing options

A customer walks into your car dealership. After a brief conversation about her preferences and budget, the sales representative helps her pick the right car model. Now it’s time to discuss financing options.

In the old days, this would have kicked off a drawn-out credit application process. Not anymore. Thanks to a newly installed, generative AI-powered system, she chats briefly with a virtual assistant on a tablet who asks questions in a conversational manner. It feels nothing like filling out a form.

The Gen AI system cross-references and verifies the information provided, instantly pre-approving the customer, and generates financing options based on her personal situation.

This sort of scenario isn’t far-fetched. It isn’t even that far into the future. We’re on the cusp of a big change.

On the road with Gen AI: Demystifying the latest innovation

Gen AI is a class of AI systems that can create new content based on patterns and information learned from existing training data. Though Gen AI has been a popular topic in the past year, few have a clear picture of all the ways it will impact their industries.

The potential use cases for automotive stretch from the earliest stages of product design and prototyping to the customer’s enjoyment of the vehicle, which could include personalized driving experiences and in-car entertainment.

But automotive leaders shouldn’t overlook a domain that’s critical for the market: captive finance companies, wholly owned subsidiaries that provide financial services such as insurance and loans that enable purchases from the parent company. These are heavily regulated organizations and stand to benefit from the quality control, risk management, compliance, and peace of mind Gen AI can facilitate.

Here are key areas where Gen AI can help automotive lending and portfolio management.

1. Credit applications.

When assessing a customer’s creditworthiness, companies traditionally looked at a credit agency’s score.

But organizations are expanding the data consumed to determine whether to approve a potential customer, including alternative financial data (like rental payment history, utility bills), employment information (job stability and history, income level), bank transactions (cash flow, savings), and social behavior.

Having additional evidence to confirm someone’s reliability helps secure approvals. Gen AI tools can easily gather these data from various sources for a more complete view of the customer, increasing approval rates while simultaneously reducing risk.

2. Customer support.

Automakers and car dealers can train Gen AI on their internal data in large language models (LLMs) to create intelligent agents that can support employees internally and assist customers through chatbots.

There’s a lot of volatility in the automotive industry, so it’s currently difficult for these kinds of bots to feature the latest information. If the annual percentage rates (APRs) on auto loans drop or a new insurance program hits the market, that poses a problem.

But Gen AI-powered solutions can account for this. The proprietary intelligence of the Gen AI agent can improve with ongoing training on additional datasets. Properly executed, the chatbot responses will be as accurate as the latest publicly available information.

3. Customer communications.

A Gen AI agent can quickly draft personalized messages for customers, whether to celebrate a milestone in their journey with the automobile, remind them of an upcoming responsibility (like returning a car at the end of a lease), or offer additional upgrades.  

To provide accurate and helpful information, the AI tool will need to be trained on internal knowledge pertaining to the customer as opposed to publicly available information.

4. Residual value.

Automobile manufacturers assess the residual value, similar to resale value, of a vehicle at the end of its lease term. It’s typically presented as a percentage of the manufacturer’s suggested retail price, after factoring in depreciation.

Before reaching out to the dealership, meticulous (or simply interested) customers can ballpark the value of their vehicles by consulting major valuation sources like Kelley Blue Book. You can plug in a few details about your vehicle – age, make, model, mileage, fuel efficiency, accident history, etc. – and get a rough estimate.

But the more information one has about a particular vehicle’s condition, the more accurate the estimate will be. Gen AI-based software can power solutions like “residual-value calculators” that go beyond the traditional metrics to assess all available vehicle information, including telemetric data, for a far-more precise estimate.

5. Fraud prevention.

Identifying potential fraud patterns can help mitigate losses. Gen AI tools can perform fast, thorough background checks that verify important documents, such as driver’s licenses, financial statements, and insurance papers.

Gen AI can also identify patterns associated with fraud attempts and set red flags (e.g., discrepancies, forgeries, suspicious behaviors) that will increase the likelihood of detection before a deal is closed.

Automotive News reports that Autobahn Forth Worth, a chain of car dealerships in Texas, helped police catch a suspect after two fraud attempts: one at its Land Rover dealership, the other at its BMW dealership.

According to the report, Autobahn’s insurance and ID verification system flagged the woman after she attempted to buy a used Cadillac Escalade with an approved loan in May 2024. The suspect left after a salesperson mentioned a problem with the insurance but attempted to buy a BMW M4 Competition one week later. The suspect was arrested and stands accused of trying to steal the vehicle using a synthetic identity, in exchange for $2,500 from a fraud syndicate.

Synthetic identities combine real information from various people to create a new fake identity and are just one of many forms of fraud that Gen AI can detect.

6. Compliance audits.

Many regulatory agencies monitor the automotive market for unethical behavior. Lawsuits are not uncommon in instances where companies skirt existing laws.

The cost for not complying with legal and regulatory policies for automotive companies is high. Administrative bodies can slap them with multimillion dollar fines, followed by a flurry of bad press. Similarly, vehicles that don’t conform to safety standards are a danger for everyone.

The oversights can be genuine errors. There are so many rules and regulations that these are almost bound to happen.

Fortunately, Gen AI tools trained on compliance protocols can aggregate and analyze all relevant information from various data sources regarding a company and its vehicles. Automated reporting can reduce the room for human error while real-time monitoring can flag potential violations for immediate remediation.

7. Mitigate delinquencies, avoid repossessions.

In Q1 2024, the percentage of motorists with auto loans that went into serious delinquency reportedly continued to rise across every age group.

The Federal Reserve Bank of New York data show that 7.9 percent of borrowers were a month or more late on their payments, and 2.8 percent were 90 days or more late, according to Automotive News.

Similar to its ability to detect fraud, Gen AI can analyze patterns to identify customer accounts at risk of defaulting on loan payments and proactively reach out with options for reducing the likelihood of default or repossession.

Of course, early in the process, company representatives can encourage responsible financial decisions: picking an affordable vehicle, taking a longer-term loan, setting up automatic payments, and so forth. But if customers show signs of potentially defaulting on a vehicle they already own, service representatives can point to professional financial help, refinancing options, and so forth.

This isn’t about reducing or eliminating someone’s obligation to fulfill the terms of a deal. It’s about helping people get through a difficult time and ultimately avoiding repossession or the cancellation of the contract.

Smooth road ahead: Embedding Gen AI into your business

Incorporating Gen AI into automotive finance is still very much a greenfield project. Despite having established financial tools and processes, companies mostly do not already have Gen AI infrastructure in place that needs to be updated or replaced.

To lay the foundation for innovation, automotive companies can team up with a business and technology transformation partner like Capgemini, as well as major players in the emerging AI and machine-learning market: OpenAI (ChatGPT), Meta (Llama 2), Google (Vertex AI), Microsoft (Copilot), and Amazon (SageMaker). Each of these technology firms offer LLMs that can be trained on data to perform Gen AI functions.

The Gen AI market is still new. As these tools continue to develop, the use cases in automotive finance will continue to deepen and multiply.

Please contact me if you would like to discuss how Gen AI can help your automotive business.

How cutting-edge tech will shape the future of environmental and social impact in banking

In finance, the goal is to go green – now more than ever.

At Capgemini’s Business to Planet Connect 2024, part of Climate Week NYC, a panel of experts discussed the challenges and opportunities that lie ahead for financial services companies.
They highlighted how data, artificial intelligence, and collaboration will be critical for setting and achieving environmental, social, and governance (ESG) goals in the financial sector. They touched upon reporting, risk management, growth, innovation, and much more.
Throughout the conversation, Satish Weber, Chief Sustainability Officer for Capgemini’s financial services business, cited a few illuminating statistics from the latest A world in balance report: more than 70 percent of executives now believe the benefits of sustainability outweigh the costs (a stark contrast to earlier attitudes that viewed it merely as a cost of doing business), and 67 percent  think the potential sustainability benefits of Gen AI outweigh the detriments.
Like other industries, the financial sector is reconsidering its impact on the environment and exploring how new tech and ways of working can help. What follows are key takeaways from the panel.

Changing business models

In recent years, it’s become clearer to banks and insurers that incorporating sustainability concerns into their business strategies is no longer an option, but an imperative.

The World Economic Forum’s research indicates that more than half of the planet’s total gross domestic product, about $44 trillion, relies on nature.
According to the United Nations Environment Programme’s latest State of Finance for Nature report, humankind will need to nearly triple today’s levels of investment in nature-based solutions to $542 billion by 2030 to limit climate change to 1.5 degree Celsius above pre-industrial levels.

Viewing the entire supply chain

Financial services companies will need to look at the entire supply chain – both upstream and downstream – when incorporating sustainability into their business models.
Major banks operate in a financial economy rather than a real economy (i.e., focusing on monetary activity rather than the flow of physical goods). So, although a bank can reduce its footprint with restrictions on hardware, paper, and electricity, most of its attention should go to its downstream portfolios: its clients across various sectors, including those with enormous carbon dioxide emissions.
Many major financial services companies say they are working with these clients to identify technology use cases that will reduce environmental impacts. Their existing environmental and social risk management (ESRM) policies should have established guidelines for due diligence when managing such risks.

Organizing ESG data with Gen AI

The panelists discussed how banks have struggled to incorporate ESG data into their decision making over the last few years because the datasets lack standardization, structure, and traceability.

They expressed hope that Gen AI solutions would be able to source, compare, validate, and interpret these data faster than humans could manually. But they also encouraged handing these insights over to actual analysts to protect against biases in the AI and ultimately make a sound financial decision.
“Gen AI has lots of benefits in the context of sustainability for the banks, but it needs to be managed,” one panelist said.

Evolving climate modeling for FS

Catastrophe modeling has helped insurance providers prepare for devastating storms for decades. But losses from non-catastrophic weather events have increased as global warming continues.

Many insurance companies are working with the National Oceanic and Atmospheric Administration (NOAA) to better understand how severe storms and climate change are affecting specific communities.
For instance, an insurer focused on military families may add new layers to extreme weather maps that provide better understanding of their likely needs, considering locations and resources.
Tej Vakta, Head of Sustainability Solutions for Capgemini’s financial services business, emphasized the collaboration with OS-Climate and detailed how Capgemini developed Business for Planet Modeling – built on Google Cloud. This advanced, integrated climate scenario analysis and assessment model facilitates strategy co-creation and validation, addressing both physical and transition risks for financial services institutions.

Combining physical risk and finance data

Banks are starting to learn about physical risk from insurers but are having difficulty incorporating new layers of geospatial storm data into their financial systems, which were designed for tracking profit and loss, credit, balance sheets, and so on.

Many banks are responding to the challenges of managing these large datasets, issues such as storage and computation, by going cloud native with frameworks like data mesh, which build a decentralized architecture and enable great flexibility. Richer data sets allow one to create scenarios on the fly.
Central banks and regulators often request risk-assessment scenarios. Soon, however, banks without the right architectural thinking won’t be able to respond to the fast turn-around times.
Harmful climate events are widespread and touch upon every aspect of a firm. Banks need to start looking at the wide array of risks, in terms of complex modeling, beyond the credit level.
Stress-testing exercises can help firms better understand market-risk factors within an overall portfolio or the operational risks of their own facilities, such as physical damage to a certain location or a server outage.

Collaborating on nature risk

Even for banks that have already incorporated climate risk into their enterprise risk management (ERM) frameworks, their understanding of nature risk may still be in its early stages.

The Taskforce on Nature-related Financial Disclosures (TNFD) is an initiative aimed at helping financial institutions understand and manage their risks and opportunities related to nature and biodiversity. Its 40 members represent financial institutions, corporations, and market service providers and hold more than $20 trillion in assets.
TNFD developed LEAP, an approach to identifying and assessing nature related issues for all organizations across all sectors and geographies. The four phases of LEAP are locate, evaluate, assess, and prepare.
This framework helps organizations ensure their disclosure statements align with TNFD recommendations but it’s also useful to better understand risks and opportunities.
Unfortunately, there is no single golden source of data on all the different elements of biodiversity a bank needs. This is where collaboration with various partners, data providers, and clients can help.
At least one panelist expects industry collaboration on physical risk and climate resilience will “go through the roof over the next two years.” He said few people collaborate across financial institutions when a problem is new, but over time our shared interests become clearer and the non-competitive spaces for working together often increase.

Creating business value in sustainability

A central part of the overall sustainability transition will be the energy sector’s scaling of green-energy infrastructure, which will require trillions of dollars. Facilitating such investments are growth opportunities.

Financial institutions have a lot of capital that can be invested in clean tech and climate tech, just as they’ve invested in Insurtech and fintech.
Many major banks have made the significant commitment of mobilizing large sums – some exceeding $1 trillion – toward sustainable and transition financing by the end of 2030.
“As banks, we have a critical role to play in driving the real transition of the economy,” one panelist said. “It’s not going to happen by itself.”
In addition to financing clients who need to invest in capital infrastructure, financial institutions might be able to play a major role in standardizing and accelerating carbon markets.
“This is the biggest evolution of the global economy that has ever been and hopefully ever will be,” another said. “But there is going to be a need for an immense amount of capital, a collaborative approach and the integration of data and technology to expedite the transition.”

What if safeguarding your data could also help save the planet? In a time when both cybersecurity and environmental responsibilities are top priorities, blending these goals is not just a smart move – it’s a game-changer. Effective cybersecurity can enhance your sustainability efforts, while a commitment to sustainability can fortify your security infrastructure.

Let’s delve into how strong cybersecurity practices can shape a greener future.

Emerging cybersecurity trends and their impact on sustainability

As recently highlighted by Information Week, enterprises are now increasingly aware of the link between cybersecurity and sustainability, facilitating significant investments in both. Combining strong cybersecurity with sustainability efforts improves efficiency and resilience, such as through cloud migration for energy savings and security.

AI is essential for managing sustainability data, though its environmental impact needs careful handling. Gartner predicts AI will reduce cybersecurity incidents by 40% by 2026 and that investment in combating misinformation will top $500 billion by 2028. As regulatory pressures increase and strategic integration becomes crucial, these trends highlight the combined impact of cybersecurity and sustainability on business resilience.

Despite these promising market trends, there are several challenges that an organization needs to address today:

• Awareness gap: Many organizations struggle to recognize the interplay between cybersecurity and sustainability. Without a clear understanding, integrating these practices effectively becomes difficult.
• Legacy systems integration: Merging outdated systems with modern, sustainable technologies can be complex and resource-intensive, requiring careful planning and execution.
• Financial constraints: Budget limitations can impede the adoption of advanced, sustainable cybersecurity technologies, posing a barrier to effective integration.

Key questions for organizations on the path to sustainable cybersecurity

• How can we share resources and use existing tools to have more sustainable outcomes?
• How can we architect data-intensive actions – such as log sources, log storage, metadata architecture, or processing – as well as third-party tools, to optimize efficiency?
• How can we be effective remotely in the event of a cybersecurity incident?
• Can a shift to managed security services help reduce costs and achieve sustainability goals?

Capgemini’s approach to sustainable cybersecurity

At Capgemini, we are dedicated to integrating sustainable practices into every facet of cybersecurity. Our approach centers around the following key principles:

• Impact assessment: Baseline evaluation
  Assess your organization’s carbon footprint using our specialized methodology. Identify opportunities to modify cybersecurity practices and reduce environmental impact, aligning security measures with sustainability goals.
• Energy-efficient solutions: Seamless cloud transitionSecurely transition from on-premises systems to energy-efficient cloud solutions with our expert guidance. Enhance cybersecurity while supporting sustainability by minimizing energy consumption and operational costs.
• Enhanced remote support: Efficient incident response
  Optimize incident response strategies to support remote operations. Reduce the need for on-site interventions, minimize travel emissions, and promote environmental efficiency.
• Managed security services: Reducing carbon footprintUtilize managed security services, including security operation centers (SOCs) and managed detection and response. Optimize resources through shared infrastructure, lower energy consumption, and enhance efficiency via automation.

You can read the full report “How strong cybersecurity drives sustainable outcomes,” which served as key reference material for this blog.

Charting a sustainable and secure future

Ready to experience how Capgemini blends cybersecurity with sustainability? Visit Sustainable Technology page

Partnering across the food value chain to minimize waste through people, policy, and technology

Food waste is a global problem that has environmental, financial, and societal impacts. A recent panel discussion at Capgemini’s 2024 Business to Planet Connect event got to the root of how to build a sustainable future, from farm to table. The session, led by Gina Kirby, a director in Microsoft’s Cloud for Sustainability team, explored how technology and collaboration are transforming the food ecosystem.

Alexandria Coari, a Vice-President at ReFED, a non-profit which fights food loss, noted that one-third of all food produced globally is wasted every year. That amounts to $1 trillion in global economic value and eight percent of greenhouse gas emissions, caused by methane gas released from food in landfills.
While the problem is significant, it is solvable with the help of technology.

Using data to get a true picture of waste

Food waste occurs at every stage of the food value chain, from the farm, to transport of food, to retailers, to the consumer. As food moves through the value chain, it is damaged, lost, or cannot be used due to changes in temperature. Data analysis can help highlight and address these problems.

However, it is difficult to track food waste data in places like homes, restaurants, and hotels. Chefs have long used pen and paper or spreadsheets to manually track food waste, but that information is often inaccurate and the process is time consuming.
David Jackson, Director of Marketing & Public Affairs for waste-analysis firm Winnow, explained its technology automates data collection across the value chain. When restaurants and other businesses understand precisely where they are wasting food and the associated cost, they can make operational changes to reduce it.
The company has captured data from approximately 2,700 locations all over the world and uses computer vision to see food as it’s thrown away, assess the weight of that material, and give it an associated cost.
“We can pinpoint somewhere between five and 15 percent of the food that comes into the kitchen as it ends up in the bin,” Jackson said. When teams in a kitchen have those insights, they can understand where they are making too much food, which is the primary reason food is wasted in commercial kitchens. Equipped with the right data, these teams can cut food waste by about half in one year, he said.
Data can also help supermarkets make sales decisions based on when food is nearing expiration, and restaurants can better assess when food must be cooked or what temperatures it should be stored at to keep it from perishing.
Data sets become more comprehensive when they include collective inputs from across the value chain. The more comprehensive the data, the greater the opportunity to make changes across multiple touchpoints.

Turning problems into opportunities

Any business operating in the food space can look at this monumental problem and see it as an obstacle. Yet sustainability leaders view it as an opportunity. Reducing food waste could provide a revenue source for businesses. It could also help feed the one in eight Americans who are food insecure today.
Some organizations are banding together to collect and share data to inform next-step actions. The Pacific Coast Food Waste Commitment, a public-private partnership on the West Coast of the US and Canada, connects retailers, manufacturers, growers, distributors, and local jurisdictions. Each entity reports data, which is anonymized and reported back so that organizations can track their progress and benchmark against peers.
Coari at ReFED noted that the coalition found a large amount of food was unsold and going to landfill. By using data to track these trends and take measures to reduce waste, in four years, US grocery retailers on the West Coast were able to:

• Reduce unsold food by 25 percent
• Increase food donations by 20 percent
• Increase food going to composting by 25 percent
• Reduce greenhouse gas emissions by 30 percent.

Getting specific with data is important to making better decisions. Capgemini Sustainability Lead Jordan Friedman said, “The statistics become more and more shocking the better data you get.” Waste is happening across the food value chain, and data helps to reveal the striking picture.

A cookbook for change

Food waste is everyone’s problem. Consumers contribute to food waste as do corporations. So, who should be responsible for fixing it? Capgemini research shows that 61 percent of consumers expect corporations to play a role in addressing food waste, but brands also want to influence consumer behavior.
“Technology is part of the solution, but at the end of the day, it’s people that can actually deliver the result,” Jackson said. Consumer education and behavioral changes will play a significant role in reducing food waste. People want to take part in transformation, whether at home or at work.
People can learn how to change their behaviors at home or when dining out. Human behavior in hospitality – such as buffets – plays a significant role in waste creation. Campaigns like The Consumer Goods Forum’s #toogoodtowaste help educate consumers on how to address the problem.
Businesses can inspire change by making people part of the solution. Bob’s Red Mill ran a competition requesting ideas from the workforce on how they might reduce waste every day. The organization received 700 ideas. Among them was tightening the screws on bags that held flour, as they would become loose over the day and flour would fall to the floor. Addressing this small issue helped reduce the amount of wasted flour by 70 percent.
Every business can pursue those small changes that can make a big difference in addressing food waste.

Investing in the future

One critical lever for reducing food waste is funding to support transformative projects. Friedman commented that Capgemini has been tracking the percentage of total revenue that organizations are investing in sustainability projects. She said, “Smaller companies are investing a larger percentage of their revenue than larger companies.” Recent Capgemini research found that organizations with $1–$5 billion in revenue increased investment this year, from 2.9 percent of total revenue in 2023 to 3.02 percent. Larger organizations invest on average 0.36 percent of total revenue.

There are funding gaps to fill, and a portion of that money can come from corporations along the food value chain. Policy will also be a strong contributor to change.
ReFED and partners also launched the Zero Food Waste Coalition, which includes more than 250 organizations across all 50 of the United States. The coalition advocates for standardized food labeling and expiration information.
Policies that standardize food labels may help the large percentage of people who get confused by the information on packages.
Zach Shaben, Public Affairs Manager at Too Good To Go for the US and Canada, said, “We are advocating for standardizing that across the products so people, so consumers, can understand those phrases.”

Reducing food waste across the value chain

The food waste problem has grown over time – but it is an addressable issue. No one growing up was taught they should throw away food. Farmers don’t grow food to see it end up in a landfill. Businesses don’t intentionally waste food and accept the cost of that waste. With behavior change across consumers and corporations, coupled with the right funding and policies, we can minimize food waste for a better planet.

How today’s business leaders can embrace energy-efficient practices to accelerate their sustainability agendas

Businesses striving to meet net-zero goals by reducing emissions must optimize energy consumption across all assets. This requires thinking beyond standard decarbonization efforts like pursuing renewable energy sources and investing in energy-efficient lighting and appliances. Accelerating progress toward sustainable change calls for bold action backed by technology.

At Capgemini’s Business to Planet Connect event, part of New York Climate Week 2024, sustainability leaders converged to collaborate on action plans for energy transition.
“It’s not just a question of doing better for the environment. If there isn’t a business case for it, it is not sustainable,” says Stuart Brodsky, Professor at Columbia University. A common theme emerged among these leaders: evolution or incremental improvement is not enough; we need a revolution.

Illuminating opportunities to improve

Nearly every business has physical assets that require energy. Every step to minimize the impact of these assets contributes to reducing the organization’s carbon footprint. For instance, companies can digitize their electric systems for energy efficiency. This can ensure operations are smart by using sensors and control systems that allow real-time monitoring and energy optimization.

With the right technology tools, a business can have the necessary energy stability while also saving money and cutting emissions.
Natasha Nelson from Schneider Electric suggests a “strategize, digitize, and decarbonize” approach that allows a business to understand its energy use, build a plan, create benchmarks, and track progress.
Capgemini worked with Schneider Electric to develop a solution that makes effective energy management easier. Our Energy Command Center (ECC) is an enterprise-wide architecture that enables users to reduce the energy consumption of their buildings by up to 30 percent. This control tower measures and predicts metrics including indoor air quality, energy intensity, water intensity, health of critical assets, and overall performance across all energy assets.

Scaling for a sustainable difference

Imagine applying the same energy-optimization tactics on a broader scale. The benefits become exponential – but there must be policies that push for change.

U.S. commercial buildings comprised 97 billion square feet as of 2018. Imagine if those building owners were required to adhere to rigorous environmental standards? Many find it easier to write a check to pay a fine rather than make a change to protect the environment.
According to Brodsky, “There’s no incentive in these building performance standards to pursue energy efficiency upgrades.”
More sophisticated policies and performance standards that incentivize, rather than punish, building owners will have far-reaching impact. Many businesses take the easy route of paying fines rather than pursuing improvements. Stringent standards that require building owners to incorporate energy-efficient materials, systems, and technologies will reduce energy use and carbon emissions. Such standards may also lead to higher market valuation.
Energy-efficient buildings have lower operating costs, can qualify for tax breaks, and may have greater appeal for environmentally conscious consumers.

Tapping into the power of data and AI

Data analysis can inform energy management decisions, but businesses often do not have the information they require.

“Everyone thinks we have better data than we do,” says Vardahn Chaudhry, Vice President of Investments and Digital Infrastructure at real estate investment trust JBG Smith. Businesses need to focus on the quality and quantity of the data they gather to build more accurate insights about energy efficiency.
The right data can program large language models (LLMs) and develop rules engines and AI systems that govern how buildings operate. Predictive analytics can help find patterns and predict future energy usage, so owners can make proactive adjustments that reduce waste. LLMs can also help automate tasks such as scheduling maintenance.

Legacy models won’t leave the legacy we want

Some businesses have lost sight of why they must make the energy transition. It is no longer just a moral imperative; doing better for the planet is now essential for corporations serving an increasingly aware and demanding customer and partner community. Adopting a business-to-planet mindset is the first step in accelerating sustainability efforts.

Businesses can play a key role in fostering innovations that transform legacy ways of working. Leaders can push for policy changes that make energy efficiency necessary, not just a “nice to have.”
Sustainability leaders at Business to Planet Connect shared key takeaways that summed up the spirit of the day:

• “Don’t be afraid to fail.”
• “We shouldn’t accept incremental progress. Swing for the fences.”
• “It’s all hands to the pump. We need everyone in the room to make revolutionary change.”

It is time to begin building a new legacy for our planet. If business leaders work together, anything is possible.

Impactful, personalized, and loyalty-driving customer experiences are critical to the success of many businesses. The need to differentiate, drive efficiencies, and create new products and services is more critical to the survival of businesses than ever before.

Microsoft’s AI offerings provide a huge range of solutions and tools capable of enhancing customer experiences across the domains of marketing, sales, service, commerce, and immersive experiences.

This article looks at Microsoft’s significant investment in AI, the spectrum of possibilities to elevate customer experience, industry examples, and the impact that can be achieved.

Microsoft’s significant investment in AI

Over the past decade, Microsoft has made significant investments in AI to ensure its platform and solutions are comprehensive and market-leading. This investment includes a commitment to building a robust AI platform from the ground up, from silicon to software. It also includes an open approach to leveraging enterprise data wherever it resides, it is a platform that covers a spectrum of opportunities from human-in-the-loop copilots to fully autonomous agents, fostering partnerships with leading AI organizations, and providing the guardrails for safe and responsible AI.

• Building AI from silicon to software: As well as partnering with leading chip maker NVIDIA, Microsoft has invested in designing custom AI chips to optimize the performance of its AI workloads. The upgrading of global data centers provides the necessary infrastructure to support large-scale AI compute workloads across their vast network of Azure data centers. This backbone is then used to provide AI features across the full Microsoft suite of products, plus tools for developers and makers to create and deploy bespoke AI solutions.

• Open approach and connectivity: Microsoft is committed to an open approach to data and AI, providing over 1,400 connectors that integrate seamlessly with a wide range of applications and services. This approach enables copilot and autonomous agent solutions that complement existing platforms like Salesforce, ServiceNow, and SAP, ensuring businesses can leverage AI without disrupting their current ecosystems.

• Continuum from simple to fully autonomous agents: Microsoft Copilot agents span a spectrum from simple agents that perform basic tasks like answering frequently asked questions, through intermediate agents that can handle more complex tasks by integrating with various data sources and systems, to advanced agents that have higher levels of reasoning, to fully autonomous agents that can orchestrate other agents and handle complex multi-step processes.

• Partnerships: Microsoft’s collaboration with OpenAI has led to the development of advanced AI models, including GPT-4o and GPT-o1 providing a range of options from multimodal experiences to deep reasoning as well as models of different sizes.
  
  Also, Microsoft has partnered right across the industry to offer a catalog featuring hundreds of models from model providers such as NIVIDA, Mistral, Meta, Hugging Face, and Cohere. This means the right functional and sized model for the job can be deployed on Azure.
  
  
• Responsible AI: Brand trust can be rapidly eroded when customers perceive brands are unethical or when companies misuse customer data. Microsoft’s Responsible AI principles are designed to ensure AI systems are developed and used in ways that are ethical, transparent, and accountable. These principles include fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability. By adhering to these guidelines, Microsoft aims to build AI that customers can trust. This trust is critical for a positive customer experience, as it ensures that AI systems are not only effective but also respect user rights and societal values.

Elevating customer experience: A spectrum of business cases

To understand how businesses can effectively drive business value from generative AI to enhance customer experiences, it’s helpful to consider three business case patterns introduced by analyst Gartner.

Defend: This involves deploying solutions such as copilots and agents that protect and maintain the current market position. These solutions are often designed to optimize existing individual productivity in pursuit of improving customer interactions without the need for extensive customization.

Extend: Here, businesses can extend and differentiate existing processes. This approach allows for more personalized interactions, greater use of the breadth of customer data and solutions that fit unique business models and customer needs.

Upend: This case focuses on creating entirely new categories, markets, or processes, often by training and fine-tuning foundational models, chaining models, and using vast repositories of often underutilized proprietary data. These innovations can significantly disrupt the market by offering unique and highly engaging customer experiences and products.

Let’s look at each business case in more detail together with industry examples of applying Microsoft AI:

Defend: Deploying Microsoft Copilots and agents

For over a year, Microsoft has offered a suite of copilots for sales, service, and marketing scenarios. Now, with new autonomous agents in Dynamics 365, further business benefit can be achieved across the personalization of content and efficiencies for customer-facing staff.

Industry examples include:

• Retail: Supplier Communications Agent optimizes the supply chain and minimizes disruptions by autonomously tracking supplier performance, identifying and responding to delays, and thus freeing procurement teams from time-consuming manual activities.
• Manufacturing: Sales Qualification Agent helps B2B sellers focus their time on the highest-priority leads, personalizing emails, and guiding customer contact.
• Telecommunications: Microsoft Customer Intent and Knowledge Management Agents work hand in hand with customer service representatives to resolve customer issues autonomously and add knowledge base articles to ensure customers have consistent interactions whether they’re on a website or speaking to a support agent, leading to better service quality, customer loyalty and agent productivity.

Benefits we have achieved with customers include:

• Improved customer and employee satisfaction
• Improved agent productivity – up to 50%, resulting in greater case throughput
• Reduction in training time for service agents
• 45% increase in sales task efficiency.

Extend: Extend Microsoft Copilots and create custom copilots and agents

While significant business benefits can be achieved through the scenarios above, they are unlikely to truly differentiate a business and the services they provide. For that, more bespoke and autonomous scenarios, leveraging data and systems from across the enterprise, will be required.

Microsoft provides low/no code tools such as Copilot Studio for creating bespoke copilots and agents or extending with additional skills. As well as this, Microsoft provides a comprehensive set of services and APIs that enable businesses to integrate AI into their applications effortlessly. Tools like Azure AI Services make it easier for developers to build multimodal services that use text, speech, and vision.

Industry examples include:

• Finance: In banking, AI-powered agents can handle routine customer inquiries through text or voice about account balances, transaction histories, and loan applications, providing immediate assistance and freeing up human agents to deal with more complex issues.
• Education: Educational institutions can use Copilot Studio to create AI tutors that provide personalized learning experiences for students, addressing their unique learning styles and needs.
• Automotive: Car manufacturers can enhance their customer service by creating AI agents that assist customers with vehicle information, maintenance scheduling, and even remote diagnostics.

Benefits we’ve achieved with customers include a financial services customer where an interactive voice service built with Copilot Studio is expected to deflect up to 40,000 calls from the contact center in the first year of operation.

Upend: Creating new categories of products by training foundational models

The largest scope for differentiation and revenue growth comes from completely unique products and services that re-invent or redefine a category.

Microsoft provides a series of advanced AI services such as Azure speech avatars for photorealistic avatars, Azure AI model catalog for “Model-as-a-Service” capabilities, and Azure AI Studio for completely bespoke copilot scenarios where full control is required.

These services open up industry examples such as:

• Immersive content creation: Leveraging Azure’s AI capabilities, entertainment companies can create immersive, interactive experiences. These virtual events could feature AI-generated avatars of real artists or entirely new AI-created performers, offering unique and engaging experiences for fans.
• E-commerce: By training foundational models, online retailers can develop personalized shopping assistants that understand customer preferences deeply, provide curated product suggestions, and create a highly engaging shopping experience.
• Travel: Travel companies can create AI agents capable of planning entire trips based on user preferences and past behaviors, offering personalized itineraries and real-time updates.

Conclusion

Microsoft provides a rich AI platform for transforming and elevating customer experiences. By understanding and addressing the unique needs of customers through defending, extending, and upending traditional approaches, businesses can create significant mutual value for both themselves and customers alike.

As AI technology continues to evolve from human-in-the-loop copilots to fully autonomous agents, the opportunities to enhance these experiences will only grow, promising an exciting future for businesses and their customers.

To understand how to elevate your customer experiences, together with Microsoft AI, visit Capgemini at Ignite 2024.

Capgemini Invent examines how multimodal AI and digital tools enhance treatment monitoring, real-time adverse event detection, and patient safety

Vast amounts of data exist across the life sciences sector, but it is often siloed or too complex for traditional analysis. Multimodal AI unlocks this untapped potential, integrating research data, clinical records, and real-world evidence to transform how we understand diseases, develop drugs, and personalize care. This shift promises a more nuanced, data-rich approach to medical research and patient care. Multimodal AI in healthcare will lead to better outcomes for both patients and practitioners.

What is multimodal AI?

Multimodal AI is a category of deep learning that is capable of processing multiple types of input simultaneously (i.e., text, visuals, audio, gestures, environmental cues, touch, physiological sensors, and GPS). Such models move beyond early unimodal models, which can only process one type of input. By leveraging multimodal data, AI can model more diverse domain knowledge, make more accurate predictions, and tackle more complex challenges.

Pioneering companies are beginning to use multimodal medical AI for drug repurposing. The integration of research publications, clinical results, and even molecular structures can unlock hidden potential within existing drugs, potentially delivering life-saving treatments more rapidly. 

Leading the digital revolution, advancements in large language models (LLMs), such as ChatGPT, have transformed the way we process and generate text that mimics human conversation. These models have a wide-reaching impact, extending beyond text creation to influence personal and professional aspects of life. By automating tasks that were once manual, these technologies are changing how we interact with digital platforms and make business decisions.

Alongside LLMs, the rise of large multimodal models (LMMs) marks the advent of multimodal AI. This AI iteration processes diverse data types (text, images, audio, et cetera) to create comprehensive domain knowledge models. Within the life sciences, multimodal AI in healthcare offers significant improvements in patient care and operational efficiency across the pharmaceutical value chain and throughout the whole life science field. The reason for this is simple: the integration of diverse datasets reveals unprecedented insights, making AI for healthcare a true gamechanger.

Multimodal data integration is crucial for understanding complex systems and structures through data, mirroring the diverse ways humans process information. It enables artificial agents to mimic nuanced human-like communication and is particularly vital in the life science sector, where data from research, patient records, genomics, and real-world evidence are foundational. Traditional isolated analysis of these datasets often led to fragmented insights, but AI’s capability to synthesize multimodal data heralds a new era of integrated analysis. However, adoption of multimodal AI presents several challenges, especially within the regulatory sphere of healthcare data handling and privacy.

“Multimodal AI is the future of AI, because it allows machines to perceive and understand the world in the same way that humans do.”

Fei-Fei Li, Professor of Computer Science at Stanford University

The rise of generative AI (Gen AI) and its foundational transformer technology has generated substantial excitement. And yet, the full potential of the transformer technology, crucial for enabling multimodal AI and artificial general intelligence (AGI), remains untapped. The following is an introduction to the field to help business leaders successfully integrate this emerging technology and unlock its many vectors for growth.

Artificial intelligence terminology

The transformative impact of AI extends beyond processes and products. It has the potential to reshape business models and influence the global economy. With such broad reach, innovators and executives need clearly defined and categorized key terminology (see figure 1).

The terms described in Figure 1 are likely to become household names in the near future. And yet, despite having a vague notion informed by popular culture, few people truly understand what artificial intelligence is. Fewer still, even within the sector, know how this emerging technology can be applied within the life sciences. But before we can explore the many exciting applications and any potential challenges, we need to examine the nature of data.

Modelling knowledge with data

To fully appreciate the capabilities of multimodal AI in healthcare, it is essential to explore the nature of data, how information is derived from it, and the process of leveraging this information to address relevant problems.

Raw data, in its initial form, often possesses limited value in itself. It becomes significant when contextualized with relevant questions, transforming into valuable information. The true value of this information is derived from the actions informed by it. As data is contextualized and correlated, its value increases substantially. Effectively structuring data is key to unlocking its full potential, facilitating the extraction of significant insights.

Data’s inherent complexity can obscure the valuable information it contains and the relationships within. With its advanced processing capabilities, AI is particularly adept at analyzing and interpreting this complexity. Data represents information in a format suitable for machine processing, namely binary code. Multidimensional sensors, which are essentially complex vectors of binary data, enable the processing of more complex information (see Figure 2). Data models break down intricate domain knowledge into formats accessible to machines, establishing connections and providing context for more effective problem solving. Speaking of which, let’s now turn our attention to the problems multimodal AI can solve within the life sciences.

Multimodal data and AI in life sciences

In pharmaceutical research, multimodal data integration unlocks significant opportunities for deeper clinical and medical data analysis. Professionals can gain improved insights into drug-cell interactions and drug mechanisms by combining various data types (molecular and genetic information, electronic health records, diagnostic images from MRIs and CT scans, and patient interaction recordings). These biomarkers are valuable sources of medical information that can lead to the identification of disease. Additionally, advanced AI technologies incorporate secondary data sources, such as patient reported outcomes (PROs) and real-world data (RWD), covering patient feedback and insurance claims. These rich but unstructured secondary sources are transformed by multimodal AI for more effective analysis, offering insights that closely mirror patients’ real-world experiences, thereby enhancing research applicability.

Previously, data from different sources was analyzed separately, limiting the discovery of correlations and patterns and hindering drug development efficiency. Multimodal AI facilitates integrated analysis, leading to a fuller understanding of drug effects, patient reactions, and treatment outcomes. This marks a shift towards more precise and patient-focused research.

However, integrating diverse data modalities poses challenges, such as handling data heterogeneity, avoiding redundancy, and maintaining patient confidentiality. Multimodal AI faces such issues as data quality variance, alignment difficulties, and the risk of overfitting. As a result, it is vital to implement robust, scalable solutions.

The promise of multimodal AI lies in its ability to merge complex domain knowledge across data types, a boon for the data-centric pharmaceutical industry. Figure 2 illustrates essential multimodal data sources for informed medical decision-making, linking these to use cases that trace the patient journey from prevention to treatment and follow-up. These examples highlight the transformative impact of multimodal data on healthcare outcomes and efficiency.

By harnessing the power of diverse data streams and employing sophisticated analytical techniques, pharmaceutical companies can significantly improve their understanding of drug safety and effectiveness in real-world scenarios. The success of such initiatives, however, hinges on the ability to navigate the complexities of data integration, analysis, and privacy protection, underscoring the importance of robust data management practices and advanced analytical capabilities in the pharmaceutical industry.

To fully appreciate multimodal AI’s transformative potential, let’s take a look at a recent successful initiative led by Bayer Vital GmbH.

The VENTASTEP proof of concept: Industry example²

The VENTASTEP study, conducted by Bayer Vital GmbH, is an example of technology-driven Innovation. It demonstrates how integrating multiple data streams in a clinical environment, with the help of digital tools, can provide valuable insights into treatment impacts, patient adherence, and real-time detection of adverse events. By diving into the different stages of the pharma value chain, we can learn from the lessons of the VENTASTEP study and understand the transformative potential of multimodal data and AI.

The study aimed to evaluate the impact of inhaled Iloprost treatment on patients with pulmonary arterial hypertension (PAH) by integrating multiple data streams in a clinical setting. It utilized digital tools to collect and analyze data and tried to detect and record irregular heart rates with AI.

“VENTASTEP was a proof of concept that underscored the potential of digital technology in enhancing our understanding of patient responses to medication. Integrating multimodal AI could significantly magnify this potential, offering deeper insights and more streamlined processes. Learning from this work is invaluable and paves the way for more structured and financially sustainable research ecosystems in the future.”

Dr. Christian Mueller, Bayer Vital GmbH

To monitor daily physical activity, heart rate, and inhalation behavior, the study utilized smart devices like the Apple Watch Series 2 and iPhone 6s, along with a dedicated app. The Breelib nebulizer facilitated the digital monitoring of inhalation data, providing a comprehensive view of treatment adherence.

The insights and experiences gained from the VENTASTEP study go beyond simple data collection. They provide a first outlook into the potential for collecting multimodal data and detecting adverse events in real time. The possibility of automated reporting to health agencies could lead to better patient safety protocols in the future. Additionally, the study’s findings could spur the development of more efficient and cost-effective digital monitoring systems, which can better engage patients through real-time data feedback. By connecting traditional clinical evaluations to digital monitoring, VENTASTEP has shown the potential of a promising avenue for the industry to move towards a more integrated, data-driven healthcare paradigm.

Final thoughts: The future of multimodal AI in healthcare

In the life science sector, the integration of multimodal data with AI is transforming traditional practices. It leverages varied data types, from genomic information to patient interactions, fostering advancements in drug development and personalized patient care. Multimodal data is driving earlier and better targeted interventions. For example, merging genetic data with patient medical histories allows for the development of targeted therapies, increasing treatment efficacy and customization. This integration also enhances patient communication, leading to more informed healthcare decisions.

The future of AI in healthcare will be a new paradigm of patient care. The application of AI and multimodal data extends from drug discovery, where AI can predict interactions and optimizes compounds, to after-sale services, such as personalized medication apps. These technologies streamline processes, reduce costs, and expedite treatments from the lab to the patient. However, every new solution comes with its own challenges. In the case of multimodal AI, it is important to correctly manage data availability, privacy, and regulatory compliance.

While many of the applications for multimodal AI are technically feasible, the availability of suitable data is the overshadowing obstacle. Additionally, the need for high computational power and specialized infrastructure for processing complex datasets is essential. At Capgemini Invent, we believe end-to-end, holistic implementation of AI models in healthcare is the future of life sciences. Multimodal databases will transform entire R&D departments (e.g., in-silico trials). Digital twins of complicated systems (e.g., bioreactors) will reveal new insights. And AI-powered internal knowledge management tools will facilitate rapid and accurate access to quantitative and qualitative research data. As a result, life sciences professionals will be able to pursue exciting new avenues for both business growth and societal impact.

Reach out for support

Capgemini optimizes the performances of processes across the entire value chain. Our life science teams support clients with an end-to-end approach that incorporates cutting-edge technologies and forward-thinking methodologies. To reinvent your drug discovery, clinical trials, patient care, and personalized medicine, check out our Generative AI Strategy or reach out to one of our experts for support.