In an age of data richness and complex decision-making, artificial intelligence has moved beyond just being a backend tool for automating everyday tasks.
Today, AI has emerged as a strategic partner in leadership, claiming “seats in the boardroom.” Currently, they have the ability to interpret data, understand leadership priorities, and propose tailored strategies for businesses.
Recognize this shift, Federation We have launched “Cogni: The Boardroom Series.” This is a leadership-centric event series that explores the transformational impact of AI on high-level decision-making.
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AI as a business
The conference, held in collaboration with the Madras Management Association (MMA), is entitled “Insight to Impact: AI in the Bostroom.” The event examined the growing influence of AI in the business power corridor. Thunai AI was the title sponsor of the event, and Neural was the knowledge partner.
The event will begin on Friday (June 6th) and S Srinivasan, Editor-in-Chief Federationmoderated a fireside chat with Dinesh Varadharajan, Chief Product Officer of Kissflow.
The freewheel conversation provided an insightful discussion about the role of AI as a strategic partner. This goes beyond just a technical tool. We focused on how AI allows leaders to make better, faster and smarter decisions.
Between hype and doubt
Srinivasan said AI could change the entire working pattern similar to the Industrial Revolution.
“On the one hand, there are people who believe that machines may soon replace humans, based on the type of consultation from places like Silicon Valley. That's one perspective. On the other hand, they are more skeptical and dismiss it as merely a technological advancement. As always, I believe the truth is somewhere,” he said.
Speaking to all paradigm shifts create new opportunities, Dinesh said that AI shifts in the boardroom support decision-makers, but are likely to replace AI, could pose challenges for those who follow directions primarily.
“Decision makers usually know exactly what they need and usually rely on the team to do it. But with AI, the executive or leadership team can do it on their own,” he further said.
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AI adoption
Dinesh criticized the superficial implementation of AI in organizations. There, companies simply add layers of AI capabilities to their modules and rebrand them as AI products.
“We don't want to put AI band-aids in our products. Rather, we want to build a new version of our products,” he said.
He detailed that building a new product version usually involves a layer of passing hierarchical instructions and translation, and requires a large team.
In contrast, Dinesh explained how a founding team of just four members can leverage AI to build prototypes of work products in four days without writing a single code. “If you're clear about what you need, AI will do it for you,” he asserted.
AI conversion
Srinivasan highlighted statistics showing high adoption of AI, particularly in sectors such as medicines and healthcare in India. However, he questioned whether this adoption would make sense or simply be exploratory.
Dinesh agreed, noting that using tools like ChatGPT does not constitute adoption. Speaking about AI adoption, he said companies need to use AI as a differentiator to improve efficiency and productivity. He added that AI alone is not enough, but in many cases human input is still essential. According to Dinesh, the integration of AI and human intelligence defines true transformation.
“Now, most companies use AI as a tool to replicate what humans are already doing. But it's not true AI adoption. And it's certainly not a change. When all stakeholders come together and look at how the business operates and rethinks all of it, a real change begins.”
The stage of AI engagement
Dinesh outlined that it usually progresses in AI adoption, denial, adoption and innovation.
Saying that very few companies are in the transformational stage, he emphasized that transformation is a tough job and that it requires that all stakeholders come together to look into how their business is run and rethink everything.
Srinivasan observed that while artificial intelligence is seen widespread visibility, as reflected in ChatGPT's 500 million users, actual industrial applications remain questionable.
Dinesh agreed, emphasizing that adoption of such AI is important, but that it is not always equivalent to conversion.
He initially cited the example of Klarna, a Swedish company that replaced 700 support staff with AI, but rehired many. “They realized that AI alone wasn't enough. In many cases, human input remained essential,” he pointed out.
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Meeting room preparation
When asked about understanding AI and its meaning about the level of viewing committee members, Dinesh shared that most stakeholders are aware of both urgency and the need for change.
However, he noted that it is extremely difficult to make a decision without a clear understanding of where technology is heading. “AI is a moving target. It changes every day,” he warned.
He emphasized the unprecedented pace of innovation, adding, “We need to run with AI, otherwise we will be left behind. That's the survival challenge right now.”
Startups overtake legacy companies
Dinesh said that the different approaches in AI adoption across the size of the company are more likely to employ AI, as it is easier for them due to lack of legacy systems.
“Everyone has their own priorities, and channeling the energy of a larger organization towards a common purpose is very difficult. That's why startups can innovate so quickly, which is why they kill bigger companies,” he said.
Data dilemma
Emphasizing the key bottlenecks in AI adoption, Srinivasan said access to data remains a critical challenge, especially in industries with governance issues such as banking and financial services.
In response, Dinesh noted that the most pressing issue is not regulatory resistance, but the vast availability of data. He emphasized that AI needs unified data access to generate meaningful insights.
“Even if it's not structurally connected, data should be logically connected,” he warned. When asked if companies were willing to invest in data collection, Dinesh acknowledged that while some large companies have already begun investing in data lakes, such practices are far from wide.
“And if companies want to stay competitive in this new era, they need to make that investment right now,” he pointed out. “In the end, it's a huge change. It won't happen overnight. Leaders need to be confident that data infrastructure is important to truly leverage the power of AI.”
Will the manager be replaced?
Addressing concerns that AI will replace managerial roles, Dinesh emphasized that AI cannot replace the empirical knowledge that humans bring to decisions. “AI can only work with data, but the decisions we make in our business are based on a lot of information that is not logically relevant,” he explained.
Adding that using AI as an augmentation is important, he emphasized that AI never replaces the managers that define business workflows. Decisions often rely on trust, intuition and tacit knowledge, all of which are difficult to encode, he noted.
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AI budget preparation
Srinivasan highlighted how most companies allocate less than 10% of their IT budget to AI initiatives, questioning how AI adoption will take off under such constraints.
“When organizations rethink how they operate, they often save a significant amount of money. These savings have to be redirected to AI,” Dinesh said.
Accepting that AI is expensive, he said, “If you're saving a lot through AI-driven efficiency, you justify your initial spending. But for that to happen, companies must be willing to invest.”
Responding to concerns about short-term losses, Dinesh acknowledges the complexity and says, “It really depends on the type of initiative you take on. Most importantly, identifying the right problems to solve with AI.” “The key to any boardroom conversation is to highlight challenges that lead to significant cost inefficiencies and show how AI investments can directly address these issues.”
Regulations and Guardrails
When asked about AI regulation, Dinesh said it was still too early to implement a strict framework, suggesting that the industry is not yet mature enough to do so.
“AI is still at a stage where we don't know where we're heading. It's very difficult to impose regulations to control them at this point,” he said.
While acknowledging the serious risks, he said imposing guardrails is extremely difficult as AI is progressing at a rapid pace and is likely to overcome current control.
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AGI vs. human intelligence
Asked whether the term artificial general information (AGI) is overused without a clear understanding of its meaning, Dinesh stated that AGI is an evolving concept and that there is no single, definitive definition.
Srinivasan then asked if he believed that biological intelligence was still far superior to mechanical intelligence. Dinesh declared this, saying, “There are many things machines can't do.”
“Time for thinkers”
Ending the conversation with an optimistic note, Srinivasan said this was an exciting moment for computer scientists and engineers.
“I think it's a great time for people who are immersed in this field with computers. There's a lot going on and it's all very promising,” he said.
Dinesh agreed, adding that the opportunity extends beyond engineers alone.
“It's a great time for all thinkers,” he said. “If there is a vision that was traditionally translated to translate into reality for the multiple layers that it has to go through, the power is now in our hands. We can use AI as our assistant to make that vision come true.”
