AiThority Interview with Jim Rowan, AI market activation Leader and Principal, Deloitte

Jim Rowan, AI market activation Leader and Principal of Deloitte, discusses Deloitte’s use of data-driven analytics and AI to enhance business decision-making and the stages of AI adoption, innovation and growth.

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Hi Jim, tell us about yourself and your role at Deloitte.  

I’ve been a principal at Deloitte for the past 9 years. Today, I lead Deloitte Consulting’s Applied AI practice. In this role, I help clients transform their businesses using data powered analytical and AI solutions that enable better decision making. More broadly over the course of my career, I’ve worked with clients across life sciences, healthcare and telecommunications, where I’ve led large scale transformational projects focused on AI & Data.  I’ve also had the opportunity to build our managed services capabilities for AI & Data as well as lead our integrated Strategy and AI & Data practices. 

Outside of Deloitte, I live in the Boston area with my wife and four children, and in my free time, I enjoy attending my children’s activities, running and indoor cycling on a Peloton.

Also Read: Building a Content Supply Chain in the Era of Generative AI

Deloitte is known for transforming businesses with data-driven analytics and AI. Can you talk more about how Deloitte leverages these technologies to drive better decision-making for its clients?  

One of the biggest challenges clients face is getting the right data to feed into their systems; but this also represents an opportunity to help businesses get more out of their data. We’re helping organizations rework their data strategies and legacy infrastructure to focus on value optimization, which will set a strong foundation for technological innovation like that with generative AI. This forward-looking approach allows organizations to evolve with the ever-increasing volume of data, instead of merely reacting to it. Organizations can start by:  

• Building a robust, modernized data infrastructure 
• Investing in high-quality data platforms that offer scalability and durability to support long-term growth 
• Aligning data strategy with broader company objectives 
• Engaging both internal and external stakeholders to ensure alignment and a broader perspective 
• Establishing robust data privacy and security practices and train employees on best practices   

What does the AI adoption journey look like for enterprise software with Deloitte?  

Consistent with past technology adoption patterns, we’ve seen clients who are starting with tactical benefits, such as improving their existing processes and reducing costs. This approach helps them derive value from so-called “low hanging fruit” while building knowledge, experience and confidence with a new technology. Next – and we see more clients beginning to venture into this area – organizations are focusing AI initiatives on innovation and growth. Overall, we’ve observed a few stages of adoption – ranging from some clients who are early adopters and have since started to scale solutions to production, whereas others are investing in fluency, experimenting with the technology, or waiting and watching to understand where the market is headed before scaling further. Some clients are already incorporating imbedded AI functionality from major software vendors as those SaaS offerings roll-out new features. That is helping organizations learn and adapt to how AI can further drive efficiency while looking at game-changing ways for AI to totally re-invent business models and processes. We are still in the early stages of this journey, so we expect a lot of innovation and rapid change to continue. 

According to reports, 80% of retail executives expect their companies to adopt AI-powered intelligent automation by 2027. What is your perspective on how technologies such as chatbots and virtual assistants impact business operations and customer experiences?  

As I mentioned previously, progress is already underway. Many of our clients are focusing their efforts on where they can derive immediate value from AI integration – this includes increasing efficiency, productivity and/or reducing costs to streamline business operations and improve customer experiences.   

We’ve seen a boom in AI-powered chatbots and virtual assistants. Not only do they help increase business efficiency, but they enhance the customer experience with personalized and accurate support, guidance, and troubleshooting to support a positive brand reputation and improved customer relationships and loyalty.  

Another common uses case is related to data access. While in many retail organizations the marketing function may have access to customer data, business stakeholders in product design, trading, retail operations, supply chain, and other functions may only have access to slices of customer information. A Generative AI system can help stakeholders across all business functions better understand the consumer by simplifying data mining and analysis with user-friendly interfaces and natural language queries. This empowers business users to make more informed decisions about product launches, sales, and other customer-related initiatives both quickly and efficiently, without burdening data analysts or the technical workforce.  We are also seeing continued adoption of AI tools to help improve the development process for new applications and maintenance of existing ones through code generation solutions, requirements gathering tools, etc. These opportunities are driving further efficiencies and reducing time to market for internal and external customers. 

Also Read: AI and Big Data Governance: Challenges and Top Benefits

With the growing importance of responsible AI, how does Deloitte approach ethical considerations in developing and deploying AI solutions?  

While each client is unique, the guidance we provide them on AI ethics is consistent with our Trustworthy AI framework, that allows us to help clients develop safeguards and balance competing ethical priorities during product development and operation. For example, by creating an organizational framework, boards are better able to assess risk and understand how various facets will impact compliance and governance. Establishing a trusted AI committee is also important, with representatives from different parts of the company—such as technology and risk management. Other key groups (e.g., legal, regulatory, privacy, ethics), and AI subject matter experts should also be included. We couple our framework with the implementation of software tools that provide guardrails and protect from data leakage along with solutions for continuously monitoring the performance of the AI solutions to address model drift.  The combination of the framework and practical implementation of solutions helps to drive the actionable approach to addressing ethical considerations. 

What AI initiatives are you currently focusing on to revolutionize healthcare delivery?  

Healthcare is one of the top industries that stand to benefit from AI, and applications that streamline operations are especially in demand. One example is using AI to accelerate the Prior Authorization process, which has traditionally been labor-intensive for both healthcare payers and providers.   

Using Generative AI to consume medical policies, guidelines, and provider-submitted information about underlying issues, patient needs, and medical history, the organization can automate a Prior Authorization submission (Provider) or generate a Prior Authorization approval or denial (Payer); provided these solutions are built with security and privacy in mind.   

Like most information sharing in the healthcare space, Prior Authorization requires the provider and payer to communicate sensitive patient data, such as protected health information (PHI) and personally identifiable information (PII), etc., which means this data is exposed to the model. Risks include unauthorized third-party access, as well as AI systems inadvertently revealing sensitive information during the generation process, thus compromising patient data confidentiality. We’ve been educating clients on how to avoid these issues and maintain compliance as they look to build applications in the healthcare space.

How does Deloitte’s AI-powered solution enable manufacturers to chart a course of data-driven excellence and thrive in a competitive market?  

There are many uses for generative AI in manufacturing, ranging from keeping equipment healthy to supply chain optimization. Applications like these serve to maximize resources and increase volume delivery while maintaining safety standards. There have also been use cases focused solely on improving health and safety, like personalized and immersive trainings for workers.   

We are also seeing generative AI incorporated into robots on factory floors. Capabilities include taking over repetitive tasks from humans to maximize time for innovation and advanced skills development, delivering parts to production lines, navigating the physical world through data training and communication with robots similarly to online chatbot tools to maximize efficiency and output. These are just some of the ways we’ve helped manufacturers use AI to increase efficiency and safety in their business.  There is also a lot of emerging innovation in the space of Robotics and the use of Generative AI that we are watching closely to figure out the best fit for enterprise grade solutions as the space continues to evolve. 

What are your biggest challenges and opportunities in leveraging machine learning to solve real-world problems?  

As we look at the current available models, deterministic AI solutions drive specific results, and therefore require use case solutions that are consistent and reliable. However, Generative AI can support creative work, and with that comes the challenge of unwanted variance. Balancing deterministic and probabilistic AI models – and matching the right machine learning approaches to the right use cases/problem sets – is essential for creating the most effective solutions and innovations and should be a critical piece of organizations’ AI strategy.  

Ultimately, Generative AI is an emerging area and we’re learning a lot as we continue to see the space evolve. And while R&D and innovation continue to push the needle on what Generative AI can do, we know that data is a critical aspect to enabling AI solutions and we also recognize that many organizations are uncovering the work it will take to build the right data foundations to support scaled AI deployments. Organizations are also learning that investments in training, education and adoption are critical to help scale the use of AI. 

In addition to technical talent, compliance and governance areas, we are seeing emerging trends where model performance changes regularly and new releases of functionality are continuously improving upon their predecessor. Following close behind technical talent (36 percent), compliance (28%) and governance issues (27%) are seen as barriers to AI adoption, as cited in Deloitte’s Q2 State of Generative AI Survey. Less than half (42%) of respondents agree they have done enough to govern generative AI adoption and mitigate its potential risks.     

This shows a lot of uncertainty in terms how AI will be regulated over the coming year, especially for global organizations operating in multiple regions. Looking at the bigger picture, the challenges that generative AI pose in corporate governance and risk parallel those in society governance and risk. In both realms, the technology’s potential benefits, and potential risks, are high. National organizations and governments will need to strike a balance in terms of making sure generative AI benefits are broadly and fairly distributed, without overly hindering innovation or providing an unfair advantage to companies with different rules.

Also Read: What Generative AI Regulations Can Mean for Businesses?

Finally, which emerging trends or technologies in the AI space are you most excited about that will help shape the future of the industries Deloitte works with?  

As more clients venture into the next stage of implementation – innovation and growth – we expect greater spend directed at exploring potential use cases, such as virtual shopping assistants and artificial model agencies in retail or drug discovery in life sciences. We are seeing value generated from new use cases being implemented across discrete AI solutions a well as value created by using AI features on software platforms. From code generation to content creation, organizations should look to link discrete uses cases to broader business processes to drive further value. And these are just the beginning, as R&D continues to advance in this space, we’ll see greater use of frontier models for multimodal solutions, the rise of integrated agent networks completing far more complex tasks than what we saw with RPA and really starting to blend AI and robotics for more real-world applications. 

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