Dr Ullas Nambiar, the Managing Director for AI Products at Accenture, has vast experience in delivering innovation, being a mentor and leader, managing stakeholders, and putting together R & D teams.

Ullas has held many essential business positions, such as Software Analyst, Research Scientist, Associate Vice President, and Chief Technology Officer.

INDIAai interviewed Ullas Nambiar to get his perspective on AI.

How did you get started in the field of artificial intelligence?

I first got introduced to AI in my BTech program. It was a simple introductory course, but that had me intrigued. I was introduced to Prolog programming, which was very different from the standard C, and C++ prevalent then. I then went in-depth into the area and pursued a PhD in AI from Arizona State University and a post-doc at San Diego Supercomputing Center & UC Davis. 

In 2021, the global market for artificial intelligence (AI) was at USD 328.34 billion. The market is to increase at a CAGR of 20.1 per cent from USD 387.45 billion in 2022 to USD 1,394.30 billion in 2029. What, in your opinion, will be the next major AI market trend in the next ten years?

Scaling implementations of AI beyond proof of concepts will be the first big ask for most organizations. From an AI technology perspective, a few key ones will be

• NLP at scale 
• Understanding documents, emails etc
• Generative AI 
• Content generation, interpreting images/graphs and writing reports 
• Ethical use of AI
• AI in Metaverse Continuum

What are the difficulties of operating an AI research laboratory in the IT industry?

Industrial research, whether in AI or other areas, comes with a unique set of requirements that make it different from academic research. Below are a bunch of challenges in operating an AI research lab.

• Industrial labs are focused on applications of technology to impact business rather than on improving the understanding of any particular area. Fresh PhDs and Masters's candidates find it difficult to adjust from academia, where the focus is only on establishing thought leadership, unlike in industrial research, where business impact is a critical component of the research. Hence finding the right talent who can come in and balance the need to stay up-to-date in their area while ensuring timely value delivery to business is a challenge.  
• Industrial applications need high accuracy, efficiency and explainability of AI algorithms. Further, AI researchers need to ensure algorithms are efficient, outcomes are explainable and do not require costly computational resources. Again these are aspects that rarely are taught or evaluated in academic research. 
• Data is the new oil. And like oil, data rarely is available in readily usable forms. Unlike academia, where most research uses cleaned standardized data sets, industry data varies by application and use case. Often data itself might be challenging to obtain. More than 50% of the effort in building AI applications goes into getting data, cleaning and labeling data to be ready for use by AI algorithms. Domain expertise is needed to label data, often not readily available in labs. This approach leads to researchers having to spend significant time working with businesses or engineers to get the 

In summary, success depends on understanding these specific challenges and creating best practices to address the challenges for industrial AI application design.

Would you please explain how AI has become a significant element of the global market? How do you envision AI's future in India?

AI has a rich and long history, with the initial formal research starting way back in 1956. Since then, the field of AI has seen two `winters’ (when interest in the subject waned and funding dried up). However, a renewed interest in the area started after 1993 with new academic researchers working on it and funding agencies supporting it. Today's many innovations we associate in public with AI came from academic labs, such as the fantastic industry-changing Google search engine. In addition, the business world has caught up in the last few years with AI. Especially with Business leaders and Investors now universally agreeing that Artificial Intelligence (AI) and Machine Learning (ML) will transform their businesses by reducing costs, managing risks, streamlining operations, accelerating growth, and fueling innovation. Accenture research shows that AI can boost profitability rates by an average of 38 percent by 2035 and lead to an economic boost of US$14 trillion across 16 industries in 12 economies by 2035.

The AI stakes in India are high. In India, AI research in Industry and Academia has been motivated by societal needs—the need to bridge language barriers in the country or enable disadvantaged sections of society to reap the benefits of information technology. Leading Indian banks have rolled out, or are pilot testing, AI-powered multi-lingual conversational chatbots for their websites and mobile applications. AI technology has helped deal with COVID-19 in India. It has enabled preliminary screening of COVID-19 cases, containment of coronavirus, contact tracing, enforcing quarantine and social distancing, tracking of suspects, tracking the pandemic, treatment and remote monitoring of COVID-19 patients, vaccine and drug development etc.

To fully seize the opportunities presented by AI, India's policymakers, universities, corporations, entrepreneurs, and workers need to come together and do much more. Indeed, to boost its AI Quotient, India must harness an innovative private sector and a supportive policy and regulatory framework—pursuing a balanced approach to AI enhancement across stakeholders.

What plans does Accenture have for AI? Also, would you like to talk about the most critical goals Accenture wants to reach and the knowledge and skills you look for when hiring people?

AI is at the core of all client deliveries at Accenture and is critical to making our clients future-ready. On average, companies that embrace AI exhibit 1.7x higher efficiency and 2.8x higher profitability. We focus on helping clients unlock transformational value, become agile and gain a competitive edge while driving sustainable growth. Specifically, we use the Accenture SynOps platform, an innovative human-machine operating "engine". That optimizes the synergy of data, applied intelligence, digital technologies and talent to help organizations transform business operations, create exceptional user experiences, and deliver incredible results. SynOps harnesses data and insights from more than 1,000 Accenture client engagements and hundreds of years of cumulative expertise across business functions, industries and domains. In addition to a higher return investment from existing IT systems, the result is more significant insights — enabling better decisions and business outcomes.

Our AI team comprises AI researchers, Data Scientists, Data Engineers, Visualization Experts, Software engineers, business analysts and product managers. While we expect individuals applying for these roles to have the necessary academic credentials and industry experience, we also look for the below crucial aspects when hiring. They are  

• Continuing to learn, innovate and "think outside the box." This process includes staying well versed in newer aspects of their domain
• Laser focus on client value creation
• Bringing a "can do" attitude.

What are the essential benefits that AI has brought to Accenture?

In general, timely adoption of AI allows organizations to analyze and improve resource utilization resulting in significant cost reduction. AI can tackle mundane activities while employees spend time on more fulfilling high-value tasks. Accenture has brought AI into all aspects of client-facing solution delivery, thereby providing faster and higher returns to our customers. At the same time, we have also looked at our internal processes and applied AI-powered automation to bring efficiencies to them.

What are the most important things an organization needs to do to make AI transformation work? What are the latest AI trends that organizations are utilizing?

To uncover strategies for AI success, Accenture designed a holistic AI-maturity framework. AI maturity measures the degree to which organizations have mastered AI-related capabilities in the right combination to achieve high performance for customers, shareholders and employees. By analyzing over 1600 firms, we could find that the firms who best utilized AI had done well in leveraging foundational technologies like cloud platforms and tools, data platforms and good governance while also leveraging AI strategically to bring differentiation. However, we found only 12% of companies could do well in both aspects listed above, and we call them "AI Achievers".

While industries like tech are far ahead in their respective AI maturity, the gap will likely narrow considerably soon. For example, automotive is betting on a significant surge in sales of AI-powered self-driving vehicles. Aerospace and defense firms anticipate continued demand for AI-enabled remote systems. And the life sciences industry will expand its use of AI in efficient drug development. However, financial services and Healthcare firms are still lagging in adopting AI as they are highly regulated and are also lagging in adopting foundational technologies like the cloud.   

What advice do you give individuals who wish to work in artificial intelligence research? What should they concentrate on to advance?

AI is a broad field, and one can easily get lost in trying to master all aspects of AI. Hence my advice for folks starting in AI is

• Choose an area that most interests you and is closer to your existing strengths.  
• Do an in-depth assessment of the state-of-the-art space by reading recent papers in the area. Always stay current.
• AI will change its form over time, but the fundamentals will remain more or less the same. So, master the fundamentals well. Ignore the buzzwords, as they will change over time.  
• Interact with fellow researchers as often as possible to debate ideas and get feedback. Do not be afraid to share your work with others.

Could you recommend some books and articles about artificial intelligence that you have found to be of high quality?

Anyone starting in AI must read "Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig". In addition, there are several good books and articles to get deeper into AI and Machine Learning concepts. Depending on interest, one should choose. For the latest publications in the area of AI and ML, I recommend looking at papers published in top conferences like AAAI and ICML.

For those interested in understanding the applicability of AI, I suggest reading "Human + Machine: Reimagining Work in the Age of AI by Paul Daugherty and James Wilson."