Rana Gujral’s journey from software engineering to AI entrepreneurship is anything but linear, and that’s precisely the point. Now CEO of Behavioral Signals, he’s pioneering a bold new frontier: emotional intelligence in machines. Drawing from deep experience in business turnarounds, finance, and innovation, Gujral leads a company that listens beyond words, analyzing tone, cadence, and prosody to unlock human intent. In this conversation, he shares how complexity fuels his path, why emotion is the missing link in AI, and what ethical leadership looks like in an age where machines are learning to feel.
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You studied software engineering, transitioned into entrepreneurship, and are now the CEO of Behavioral Signals. What motivated this journey?
It wasn’t driven by a single motivation. My path has been shaped by observing opportunities and gravitating toward the ones that seemed most exciting, usually the ones that were new and challenging. I’ve always been drawn to initiatives that gave me a sense of uncertainty and difficulty. That complexity tends to come with an adrenaline rush: “This looks hard, but it could be fun.”
That’s how I’ve approached most of my professional decisions—chasing what felt right and meaningful. It’s been an organic process and, overall, an enjoyable one.
Before Behavioral Signals, you had a career in finance and led a business turnaround. What influenced that shift toward entrepreneurship in AI?
I had the opportunity to lead a business turnaround, an experience not many seek out because of the inherent challenges. These situations often involve emotional tension, organizational toxicity, financial stress, and a lack of optimism. However, for me, it turned out to be one of the most rewarding experiences of my career.
One key lesson was the undeniable importance of innovation. If a company can create real value and innovate meaningfully, it will be rewarded. Innovation must be the compass. Of course, leadership, financial discipline, and market strategy are essential, but long-term success depends on whether you’re building something valuable for the world.
That lesson has continued to guide our work at Behavioral Signals.
How has that experience influenced your approach to leadership in today’s fast-evolving AI landscape?
We are living through a remarkable moment in technological history. AI is reshaping society in ways that are visible in real-time, monthly, and even weekly. Few periods in history have seen this level of rapid, systemic change.
This acceleration is being driven by breakthroughs in AI. The magnitude of impact is on par with, or possibly greater than, the advent of the Internet. Every professional, from engineers and writers to doctors and lawyers, is seeing their work evolve.
As a leader in this space, you must be observant, agile, and decisive. Leadership today requires the ability to connect the dots, anticipate change, and seize emerging opportunities.
Behavioral Signals focus on emotional intelligence in AI. What inspired that direction?
Behavioral Signals originated from SAIL Labs at the University of Southern California, a research-focused initiative aiming to better understand human cognition, thought processes, decision-making, and emotional states. At the foundation of this is emotion and behavior.
We discovered early on that the field largely ignored a powerful communication modality: tone of voice. Emotional assessments at the time, and still to a large extent today, relied on converting speech to text and analyzing words.
While text-based analysis is valuable, it misses much of the nuance. Real insight often lies in prosody, pitch, tone, and cadence. So we made a pivotal decision: to focus solely on tone, ignoring the actual words. We built engines capable of extracting behavioral signals directly from vocal patterns.
That approach remains central to our mission.
What is your vision for the future of human-machine interaction, especially as it relates to voice and emotion?
Human-machine interaction will deepen significantly in the coming years. Today, we already speak to our devices—phones, cars, and smart assistants. Soon, we’ll interact with humanoid machines in public settings and private homes.
These systems will not only perform tasks but will increasingly be expected to communicate in human-like ways. The most natural and intuitive mode of communication is voice, it’s intrinsic to the human (and animal) experience. It predates language and is deeply ingrained.
However, understanding voice from a human perspective is extraordinarily difficult. It’s not just about natural language processing (NLP) or understanding the meaning of words. It’s about detecting the state of mind, emotions, intent, and context.
That’s the challenge we’re addressing: creating systems that provide emotionally aware, cognitively informed responses.
As AI becomes more sophisticated, how should we address ethical concerns around privacy and emotional inference?
Ethics are at the forefront of this conversation. Today, the main concerns are bias and privacy. Are we building equitable systems? Are we protecting user data?
As emotional AI advances, even more complex questions will emerge. What decisions should AI be allowed to make? If a mistake occurs, who is accountable? Do intelligent systems deserve certain rights? Should specific roles be reserved for humans?
These are philosophical as well as technical questions. We can’t ignore them. The focus should not be on stopping progress because it’s inevitable, but on guiding it responsibly. We need collaborative platforms that bring together diverse voices: technologists, ethicists, regulators, and citizens.
It’s not solely a government or industry responsibility; it’s shared. We must embed ethical considerations into design frameworks from the beginning.
How has leading a company like Behavioral Signals influenced your personal growth?
Every journey influences personal growth. Leading a company isn’t inherently more important than other life paths. Growth happens through observation, reflection, and adaptation.
For me, it’s about staying aware of what I’m doing, how I’m doing it, and whether the outcomes align with my intentions. When things go wrong, you have to ask: Is this failure, or is it an opportunity to view the problem from a new angle?
In startups, this learning process is constant. You’re dealing with people, systems, and markets, all of which evolve. I’ve led multiple companies across different sectors, and each one has required a fresh mindset. You never truly “figure it out,” you solve a new equation each time.
