Driving successful AI transformations at the enterprise level

Chandramauli Chaudhuri leads the Data Science initiatives across Fractal’s Tech Media & Telecom vertical in the UK & Europe. He works in close collaboration with senior business stakeholders and CXO teams across some of the leading global enterprises, enabling the development of long-term strategic AI solutions.

Being in the field of Artificial Intelligence and Machine Learning for close to a decade and working across a wide range of industries, his primary area of interest lies in R&D, algorithmic customisation, capability enhancement, and MLOps deployments of solutions. Analytics India Magazine interviewed Chandramauli to gain insights into AI transformation at the enterprise level.

AIM: What are the key factors when it comes to driving successful AI transformation for an organisation? What are the emerging AI trends enterprises are capitalising on?

Chandramauli: As a business leader driving AI transformation across an organisation, it is critical to understand that Artificial Intelligence is just the means of value realisation and not an end goal by itself. Thus, the factors differentiating success and failure lie in its synergy with the company’s core principles, value proposition and customer-centricity. AI adoption is not a plug-and-play solution that yields overnight returns. Businesses need to think beyond just the cutting-edge software, high-end infrastructure and skilled coders. Alignment of the company’s culture, customer expectations and ways of working to support such transformations need to take equal if not greater importance. The companies that are doing well, especially in banking, finance, media, telecom, and tech, are those that have integrated AI into their day-to-day functions. They are moving it away from being a siloed and ‘specialised’ initiative undertaken in small pockets, to broader cross-functional collaboration. 

As far as emerging trends are concerned, organisations have started focusing a lot more on two key areas – execution excellence and risk management. This means nurturing an agile mindset across teams, pursuing the right use cases, developing a strong data foundation, investing in the right skills, and having a robust strategic roadmap. There has also been growing acknowledgement of the challenges associated with cybersecurity, user privacy, and digital consent. Issues like lack of explanations, absence of audit trails and presence of bias in AI systems have gained far greater prominence from the global community in the last couple of years than in the past decade. It’s true that we still have a long way to go and yet to fully appreciate the complex socio-political and economic implications. However, we have started looking in the right direction, focusing on building greater transparency and trust. The early adopters of these practices stand to reap the rewards in both the short and the longer term. 

AIM: Where does the industry stand in terms of scaling AI solutions? How can AI/ML become a differentiating factor for companies?

Chandramauli: AI opens new frontiers to solving real-world problems. We have already seen some great examples of AI powering decisions in almost every domain, from climate change to the choice of songs. Add to this, the ability to augment with new-age technologies like 5G, IoT, AR, VR, etc., and scale through cutting-edge hardware, open-source architectures and cloud computing – what we gain is the prowess to redefine the limits of end-user personalisation and engagement. The resulting increase in efficiency, effectiveness and productivity has a direct impact on the top and bottom line. It is redefining the way successful businesses look at their strategic and operating models. Naturally, there is a lot of excitement around the future and a rush to seize any competitive advantage. 

But, this is only one side of the story. For the vast majority of companies that are trying to drive innovation and scale their AI operations, progress has not been at the scale or pace that people might assume. Many are still struggling to push past the pilot and experimentation phases. This is mainly because, the traditional mindsets, legacy technologies and ways of working run counter to that needed for a full-scale AI transformation. Studies suggest that, currently, only about 8-10% of the firms engage in practices that support widespread adoption. For the remaining ones, enabling AI transformation at scale still very much remains a work in progress. 

AIM: How does AI help organisations add value and manage risks?

Chandramauli: Large enterprises need thousands of decisions to be made every single day. Doing so effectively, requires a combination of process automation, contextual insights and cognitive engagement. Not only that, shifts in the industry landscape can trigger quick changes in customer behaviour, rendering past insights completely useless. Such dynamicity poses a significant challenge for the traditional software and analytical solutions, which depend largely on pre-defined logic and set patterns. The COVID-pandemic has been a reality check for many organisations in this regard. 

AI, on the other hand, is particularly well suited to address such real-life challenges. Consider a business problem like content piracy as an example. Despite decades of effort, it continues to be a perpetual problem in the media and entertainment industry. It causes billions of dollars in losses every year. The volume of pirated content across the globe, in fact, reached record highs during the lockdowns. This is because, the consumers and distributors of pirated content are continuously changing, updating and scaling their operations beyond the limits of traditional anti-piracy systems. AI-powered solutions, learning and re-calibrating in real-time from online trends, network logs and consumption data feeds, can be much more effective at identifying and mitigating such behaviour.

AIM: What are the things organisations should keep in mind while building an AI roadmap?

Chandramauli:  To build successful AI roadmaps, leaders need to devote attention to four key aspects. 

First, define a strong and clear narrative, that explains to everyone what AI is and why it is so critical to the future of the organisation. This needs to start from the top – the board and the executive team, along with the key decision-makers including managers and team leads.

Second, dedicate time and effort to address the unique barriers when it comes to such fundamental shifts – apprehensions of the workforce about becoming obsolete, difficulties in adopting the agile ways of working, etc. The leadership team has to provide a vision that brings everyone together and shows how they fit into a new AI-driven culture.

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