CIO Bhavani Amirthalingam on driving change in the AI era

Bhavani Amirthalingam’s career spans more than 25 years in key technology and executive leadership roles across a multitude of industries and geographies. She spent 15 years at World Wide Technology, serving as CIO and vice president of customer solutions and innovation for the high-tech, high-growth company. She then led the digital transformation of Schneider Electric, a global Fortune 100 energy management company. Most recently, she has served as EVP and chief customer and technology officer at Ameren, which she joined 2018 as SVP and chief digital and information officer before adding customer experience and operations in 2023.

On a recent episode of the Tech Whisperers podcast, Amirthalingam talked about her career journey and the differentiators and leadership philosophies that were instrumental in enabling her to earn the chief digital information officer role, then the additional accountability of chief customer officer, and the call to join a public board. After the show, we spent some more time discussing what it takes to lead successful large-scale transformations and how leaders and businesses should be thinking about and leveraging AI and data going forward. What follows is that conversation, edited for length and clarity.

Dan Roberts: With your experience helping large organizations transform the customer experience and raise the digital quotient enterprise-wide, what advice do you have for other leaders embarking on large-scale transformations?

Bhavani Amirthalingam: Having cross-functional teams coming together with clear, integrated objectives is extremely important for these transformations to be successful. Sometimes organizations make transformations all about the technology. But it’s not a technology initiative. It’s not an operational initiative. It’s a company initiative, or it’s a customer initiative.

First, an important element in making these transformations work is creating a two- or three-in-a-box — depending on key stakeholders across the organization — responsible and accountable for the transformation, both on the ground and all the way to the top.

The second thing is, more often than not, organizations underestimate the change management that is instrumental for transformations to be successful. They think of change management as something you do at the end of the project, and that approach is bound to fail. Change management is what you embark on at the beginning and stay focused on all through and after the implementation.

Change management starts when you define the why behind the transformation, and you do that together. You get the folks who are in the trenches doing the work, or if it’s your customers, you get them involved, to get those perspectives so you can understand what is important to them and then keep them engaged throughout the process. You need to identify and create this ‘focus group’ up front, making sure it has diverse perspectives. There’s probably going to be one individual that is your best adopter because they want the change. They are going to be your easiest adopter. And then you might want to have one of your hardest adopters as well, because that gives you a view of what the apprehensions are and why they have them.

As you’re building and designing solutions, understanding and driving change management adoption needs to start on day zero, not on day nine or ten, because a lot of times, that’s where things fall apart. It’s having good ownership — and that’s not just ownership of the technology team; it’s the ownership of the teams and the leaders who are going to be impacted across the organization.

Folks can get enamored by the technology. They’ve been waiting for a long time, and they’re hungry for it, so you move forward. But then you are not thinking about change management and the fact that you’re looking to disrupt and change things that people have been doing in a certain way for years.

What are some of the other potential blind spots that can undermine your success?

You might have the right objective, the right initiative, the right teams coming together, everybody’s rallying, you’ve got the right change happening, and the adoption’s there — and yet, you might not be able to realize the value of this transformation. 

Value realization is such an important aspect of transformations, so it’s important that you define together upfront what success looks like. What key performance indicators are we going to look to say that we are at X, we need to get to Y, and we were able to get there. And what does that timeframe look like? What does that journey look like?

And then, when you’re talking about global Fortune 500 companies, there are so many layers of complexity. You have to understand the organizational dynamics and culture. The organization might be really matrixed, for example, so you have to figure out what you need to do to get everybody to a common understanding.

You also must make sure the CEO is engaged and that this is not just left to the technology leader or the operations leader. When you think about these large transformations where you want to drive a large-scale impact and change the digital quotient for the organization for the long run, CEO understanding and engagement is mandatory for success and value realization.

You have done a lot of exciting things to leverage data and AI throughout your career. What are some of the opportunities for leaders to use AI to impact the way employees work and improve the customer experience?

I’ll start with saying AI is not new. It has been around since the 1950s with machine learning. Using data and algorithms to imitate the way humans learn came into the scene in the 1980s, and this further evolved to deep learning in the 2000s. Accelerated computing has led to the creation and scaling of large language models that have now democratized AI, finding ChatGPT a place in the dictionary.

I have had the opportunity to use AI to improve the customer experience by providing customers greater insights into their energy usage, enhance asset performance by predicting and preventing failures and targeting maintenance to improve reliability, increasing safety for customers and employees by detecting gas and water heater leaks, reducing bills for low-income customers through voltage optimization on circuits, and many such use cases. My advice to leaders is to identify areas with the largest potential and impact, assess the readiness of data, build or deploy existing solutions that leverage AI, and make sure you are rethinking how people will work differently with these new capabilities right from the beginning of your initiative.

On generative AI, one can take a multiprong approach. First, you can be a taker, by understanding the embedded capabilities that are being incorporated into existing software platforms you currently own and defining how you can leverage those for the organization. An example could be around platforms you use today for software development that now have AI capabilities — how can you use that to streamline development and capabilities in your collaboration suite, CRM, workforce applications, and the list continues to grow.

The second approach could be one of a shaper, where you create a private and secure instance of an existing large language model leveraging one of the hyperscalers — essentially your own version of ChatGPT for your enterprise, fine-tuned to company-specific data that enable very specific use cases for your business. You can build copilots for different functions across the organization. The third could be of a maker, where you build your own large language model, which can be expensive and complex, but can create a differentiating value for your business.

Two of the most common use cases being leveraged across industries are improving developer productivity and improving efficiency for customer care representatives in call centers. An example could be summarizing after-call work. You’re driving productivity, efficiency, and how you’re interacting so you can spend your time with the customer on things that are more important and that only you can do. The key is identifying the use cases with the biggest business value and impact as well as defining how you will work differently to benefit from these capabilities. You must lead with a cybersecurity mindset as you build out these capabilities.

The human element is going to be more important, not less. Keeping that in mind and upskilling our folks around that is really important. AI is not taking away everyone’s jobs but, as Karim Lakhani at the Harvard Business School said, “AI won’t replace humans — but humans with AI will replace humans without AI.”

Talk to us about how leaders should be thinking about the role of data quality in terms of their AI deployments.

Data quality is the cornerstone of effective AI deployment. Without it, scaling AI solutions is like constructing a building without a solid foundation. Leaders must prioritize investments in data quality and governance. The time required to train algorithms with quality data is substantial but necessary for achieving desired outcomes at scale. Pilots often reveal the feasibility of scaling; however, not all initiatives will make business sense to expand. It’s crucial to design a sustainable architecture with the end goal in mind, ensuring scalability aligns with business objectives.

Leaders should view data quality as a strategic asset. High-quality data ensures algorithms are trained effectively, leading to more accurate and reliable AI applications. It’s essential to establish robust data governance frameworks that ensure data integrity, security, and compliance. This foundation supports AI systems that can adapt and scale as business needs evolve.

How do you see AI affecting different aspects of the business going forward?

I think every aspect of the business will be touched in some form or fashion. As an analogy, think about how the internet, mobile, social, and cloud technologies have changed how we work and live over the last 25 years. AI will have an even bigger impact on how we operate, both in day-to-day life and in business, and I think the change will be more rapid.

I am truly excited about the possibilities of solving very large complex problems that can improve human life in our lifetime, whether it is addressing the challenges of climate change or finding ways to cure cancer. You think about the pharmaceutical industry and how long it takes to do drug research and development. I think the use of AI will shrink that timeframe dramatically. In terms of product innovation, the service or product you offer itself might go through significant evolution or even be fundamentally disrupted depending on the industry. So it’s really important that you’re also looking at the ‘product’ you deliver and thinking about it with that lens.

It’s so interesting to hear you speak about AI with so much energy and optimism, because many people are much more anxious about it.

It’s energizing to consider AI’s potential to tackle diseases currently beyond our reach. However, this advancement doesn’t come without risks. Cybersecurity and data privacy are critical concerns that must be addressed. The energy consumption from the data centers enabling AI challenges our net-zero goals. Despite these challenges, I’m confident that we will develop the necessary safeguards and continue to harness AI for the greater good of humanity. I believe AI will help solve some of the challenges created by AI.

For more leadership insights and advice from this transformational business executive and Forbes 50 Innovative Technology Leader, tune in to the Tech Whisperers podcast.