Select Key Imperatives to Make it a Successful Decade of AI Key Imperatives to Make it a Successful Decade of AI

03 May 2021

We have been talking about the potential and promise of Artificial Intelligence (AI) for a long time. One of the most significant aspects, which has put AI at the center of all business and social conversations is our ability to harness data and apply AI to drive crucial insights. We can leverage the potential of AI and use it to solve some of the most complex problems faced by the world today.

Several sectors across retail, finance, healthcare and e-commerce are already deploying AI to increase efficiency and productivity, enhance customer service, and build a stronger security cover. These digital transformation journeys have further accelerated in the wake of the COVID pandemic.

Today AI is everywhere, and it is all pervasive. It won’t be incorrect to call this as the ‘Decade of AI’ as it is supporting several human and business functions as not just artificial but augmented intelligence. Farmers are using AI for crop planning; AI chatbots acted as the first responder to COVID-19 queries; AI is supporting supply chains, helping build the future talent pipeline and also accelerating drug discovery.

However, irrespective of the nature or size of the organization, there are some key imperatives to be taken into consideration to leverage the true potential of AI and scale it to ensure organizational and national growth:

Need to have a strong information architecture

Using and exploiting AI is a goal for many enterprises around the world, but before you can begin working with AI, a number steps have to be taken. For example, AI requires machine learning and machine learning requires analytics. To work with AI, machine learning or analytics effectively, we need a simple, elegant data, or, Information architecture (IA). In other words, there is no AI without IA. It is extremely important to have the right architectural investments and build the right platforms for AI. The platforms enable collecting, organizing and transforming the data that fuels the AI for every organization. Monitoring data and AI models and having the right governance in place is key for scaling AI and finally being able to automate the AI lifecycle itself ensures that the power of data and AI is harnessed and the outcomes from the investments are tangible and clear to everyone.

Promote trust and transparency in AI models

Trust is a key factor that could tilt the scale for adoption of AI as AI learns from data which is often generated from human workflows. More than 180 human biases have been defined which can affect decision making. Without the right guardrails and framework for trust, these biases could find its way into our AI models. Therefore, instilling confidence in data and AI models is paramount. To capitalize AI to the fullest, it needs to be democratized, trusted, explainable, secure and open. Governance lifecycle of AI is highly important, and companies have to trust it knowing that even after using AI they will have full ownership and protection of their data and insights. Bias in AI systems could erode trust between humans and machines that learn. AI systems that will tackle bias will be the most successful.

Need for a strong partner ecosystem

For organizations or nations to implement AI at scale, take a lead in AI innovation and draw important insights, one needs advanced platforms, services and a strong technology partner to fully harness the power of exponential data they produce. Today, we are thriving in vast ecosystems that are broad by nature, potentially spanning across multiple geographies and industries, including public and private institutions, as well as consumers. As a result, one needs to continuously deal with challenges related to culture, ethics, bias and strategic imperatives. A strong partner is one who not only has the ability to broaden the use of AI across a company, but also has the knowledge to do it the right way. Leveraging the partner’s expertise in these specific areas becomes absolutely critical to making AI work effectively.

Upskill and reskill to boost AI productivity

Successful data science teams in high-growth companies need to be diverse. No one person is likely to be an expert in the multiple fields of AI, including computation, data management, applied math, business use cases, decision science and so on. AI requires teams of people with different skill sets and perspectives to collaborate and this is a global ask. Given the background, the Indian skills ecosystem needs to look at broadening the talent base capable of creating and executing AI solutions to keep pace with global AI leaders. I believe the industry is equally responsible for providing talent, technology and platforms to support this talent mandate.

So, when we look at furthering the national growth agenda, it is impossible to ignore what we may be able to achieve with implementation of AI. AI is an area where skepticism may be high, its adaptation methodology and investments might be argued, but smartly targeted AI and machine learning tools, with well-deployed algorithms fueled by huge data sets, can drive lasting improvements across various social delivery applications of governments.

AI, together with hybrid cloud and, in the not so distant future, with quantum computing, will have a profound impact on virtually every sector of society. This will be the new natural differentiator to retain competitive parity.

The article by Mr Sandip Patel, Chairman, CII AI Forum and MD, IBM India and SA, first appeared in the April 2021 issue of CII Communique. Click here to read the issue.

Share to...