How organisations can advance their AI maturity: Prithivijit Roy, Accenture Applied Intelligence

Organisations that have reached a higher level of AI maturity than others have mastered a set of capabilities across technology, organizational strategy, talent and culture. In an interaction with Dataquest, Prithivijit Roy, Managing Director, Accenture Applied Intelligence (AAI) explains why AI is everyone’s business and how organisations can advance their AI maturity. 

DQ: Where are enterprises in their AI adoption journey currently? What are the challenges they face?

Prithivijit Roy: Each industry is in different stages of their AI adoption journey, according to Accenture’s latest research on AI maturity. Analyzing the performance of 1,200 companies across 16 industries and 15 countries including India, we found that a small group of organisations (12%) dubbed “AI Achievers” outperform their peers on AI and use it to drive business growth. On the other end of the spectrum are AI Experimenters that account for the majority (63%) of companies. These organisations are scratching the surface of AI’s potential, signaling an opportunity to further adopt AI at scale across their enterprise. 

Experimenters are often stuck in the experimentation or pilot stages with AI– a common occurrence where the perfection of a product or tool becomes detrimental to scaling technology. This group includes industries with legacy technology that struggle to transform their digital core. While the technology industry is currently far ahead in its AI maturity journey, there is tremendous opportunity for AI adoption to grow across industry sectors like banking, automotive, aerospace and defense, and life sciences. Further, investment in AI is rising across the board. Per our research, nearly half (49%) of all companies globally are expected to devote more than 30% of their technology budgets in AI over the next two years, a sign of what’s to come. 

DQ: How can enterprises advance their AI maturity?

Prithivijit Roy: Companies that have reached a higher level of AI maturity than others have mastered a set of AI capabilities in the right combinations – including the technology itself (data, cloud and AI), organizational strategy, talent and culture. In order to advance their AI maturity, organisations must:

• Champion AI as a strategic priority for the entire organization, ensure C-suite sponsorship and empower teams to innovate; 
• Create an AI core that integrates foundational AI capabilities across the enterprise, such as cloud platforms and tools, data platforms, architecture and governance;
• Industrialize AI tools and teams, and nurture a culture of responsible AI design to successfully scale the use of AI;
• Invest in talent and skills training that increase AI literacy, and hire for multidisciplinary AI-related skills.

DQ: What is Accenture Applied Intelligence and how does it help clients transform businesses and industries?

Prithivijit Roy: At Accenture Applied Intelligence, we believe that AI is everyone’s business. Our mission is to scale AI, analytics and automation—and the data that fuels it all—to power every single process and every single person so they can do things differently and do different things. ​ To do that, data and AI strategy needs to be inextricably linked with business strategy, and AI needs to be understood and embraced by everyone in the enterprise. 

We combine data, analytics, AI, and automation to help our clients transform their businesses and industries. We help them make informed and intelligent decisions based on data-driven insights, with a greater degree of confidence and in a more responsible way. 

DQ: How has India shaped up as a talent hub for AAI and how are you helping your people stay relevant?

Prithivijit Roy: India is a hub of innovation for Accenture globally. At Accenture Applied Intelligence, there’s a lot of critical work underway that’s being led by our India team. We have a talented team of specialists in data science, data engineering, machine learning operations, data visualization, storytelling, cloud architects, full stack developers, UI and design professionals and people with deep industry and functional domain expertise– who leverage AI to solve real world problems for our clients. 

We are deeply committed to upskilling and reskilling our people so that they are at the leading edge of solutioning for our clients. Our learning approach combines foundational training, role-based deep skill building, data driven skills certification and responsible AI practices. Our learning methods are experiential, immersive and hyper-personalised. We also partner with leading academic institutions around the world in the study, skills, and application of AI such as the Alan Turing Institute, Massachusetts Institute of Technology and UC-Berkeley Institute for Data Science. 

DQ: What are the top skills that data scientists and data engineers need to hone?

Prithivijit Roy: Data scientists need deep mathematical and statistical knowledge in order to build and apply machine learning models and strong computer science skills to understand how data models can be engineered into scalable solutions. Data engineers need to be conversant with cloud platforms, data lakes, and automation. Relevant business domain knowledge – be it industry or functional expertise - is key as it helps data scientists and data engineers to contextualize data capabilities to reinvent business processes. Above all, it is important that talent possess learning agility and a problem-solving mindset.

DQ: Why should businesses adopt a Responsible AI strategy?

Prithivijit Roy: AI-enabled decisions have a bearing on people’s lives, which brings issues such as ethics, trust, legality and responsibility to the fore. Responsible AI covers seven dimensions – soundness, fairness, transparency, accountability, robustness, privacy and sustainability. It creates a framework that ensures the ethical, transparent and accessible use of technologies in a manner that is consistent with user expectations, organizational values, and societal laws and norms.

Hence, a responsible AI strategy is now a Board-level priority for organisations - to mitigate risks for clients, employees, partners, communities and society at large. It involves setting foundational responsible AI principles to help shape key objectives, putting in place a governance strategy and building tools, frameworks and processes for algorithm vetting and monitoring and skilling people to drive a culture of responsible use of AI.