How is behavioral science going to shape the way Gen AI evolves from here on and also how will this impact businesses run in future? Human understanding is an essential part of how any technology evolves, Gen AI more than others given that is one of the most quickly adopted, still feared, partially misunderstood and definitely one of the most talked about advancements in recent years. Its role is key in how interactions are understood by AI – things like subtext & context can now be codified. Beyond that, change management in enterprises will be a key space for BSci. strategies – to drive & sustain adoption of AI with confidence, trust & efficacy. Finally, to navigate governance for AI more effectively (fraud, privacy, risk management), you will need to understand humans better to de-risk AI. Businesses are already moving from responsive to reactive to real-time to predictive. That trend will only continue as synthetic data becomes more accurate, behavioral data sets are integrated and more decision-making in the enterprise is AI-powered
How challenging is dealing with the psychological parts of AI?
AI today is far from sentience (despite the occasional story that breaks), what it is displaying frequently is human-like behaviors & emotions because of what humans are teaching it! As my incredible colleague – Akbar Mohammed recently said to me: When you strip all the novelty away, it is math. We can certainly learn about teaching AI abstract constructs better from reviewing literature on how children learn, how the brain is wired, how memories form, how neutral pathways get disrupted. What’s more interesting is how users of Generative AI will feel about AI displaying “human” behaviors – like a robot experiencing “pain” or a chatbot talking to you about “love” and “a search for meaning” – we (humans) love to anthropomorphize! Personally, I prefer technology behaving like technology and not over-indexing on humanness in how it communicates, as it’s still quite broken and far from convincing!
What are some common misconceptions about the intersection of analytics and behavioral science and how do you address them?
That they need to be applied sequentially – often times clients will complete a quant study and then run qualitative research to make sense of the data points, or start with qualitative research, then try to prioritize and validate insights with numbers. The challenge with that is either way, it’s easy to make numbers / people tell the story you want to tell. Instead, my recommendation is to apply them cohesively, integrated. This means setting up teams with cross-functional members who can look at the Intelligence & Emotion aspects in tandem, identify latent features, find behavioral markers and define far deeper insights, strategies & recommendations. Related is the misperception that behavioral science = a set of principles you can copy paste and apply to any situation. While there are certainly best practice and frameworks you can use across contexts, human behavior isn’t that simple! Much like algorithms, every human emotion has a related response, an action tendency you can tag to it and hence address – the complex part is knowing which framework/method to choose.In your opinion what are the key skills or attributes that professionals need to excel in cross functional strategic roles like yours?
In our team to succeed – one needs to have an extremely strong foundation, at least 1 core competency in AI, Engineering, Behavioral Science or Design. Additionally, you need Business Consultant chops. You need to be able to onboard yourself to any topic, any domain with agility. You need to be unafraid to sound stupid, and this is a big one – take balanced risks. Know how to go from provocation to idea to activation to productization (scale) and know how to take a team & your client along with you. Everyone should build their own process toolkit to frame & dimensionalize problems. Once you know the problem frame, the solution design comes much easier. If you don’t have clarity on what you’re solving and for whom, in what horizon, you can get very lost in the research phase. The world is changing constantly, and you need to understand evolutionary science & build foresight into your practice in equal measure.
How do you foster an environment that encourages cross disciplinary and knowledge sharing within your team? Our team has many fun and (some) process-driven ways to do this consistently. We work in a variety of problem types: Responsible AI, Dynamic Consumer Journeys, Futures of Industries, Emerging Tech Integration, Strategic Roadmaps… we have a biweekly team townhall, where by rotation teams share the latest highlights – more focused on their reflections, observations & learnings from an outside-in view. We are a small team, where everyone is one Teams message away to help you unpack a problem from 4 dimensions: intelligence, emotion, speed & scale (IESS). We actively share articles, memes & hypothesis with each other, analyzing business problems & the latest reality TV show the world is hooked on with equal seriousness. We also maintain a “Dimension Dictionary” where in the team meetings, 1 person will share a concept from AI, 1 from Design/BSci., 1 from Engineering, and 1 from Domain/Industry by rotation. The whole team also collaborates on white papers, articles & delivers training – to test, enhance & push boundaries on what we know. We all embody Fractal values of staying humble, hungry and smart.
Human brain has evolved a lot in the past as technology kept on changing. As AI progresses and eases human work, how do you see human behavior changing in times to come? Our behavior is deeply influenced by the environment around us: kids today understand ‘scrolling’ on a screen before many of them speak sentences, teenagers (and adults) associate bullying with a digital anonymous face, we are seeing a spike in mental health challenges, new kinds of psychological dependence/addiction – our brains are quite literally getting rewired to adjust to the amount of stimulation they’re getting. I actually see us hitting a limit, a threshold at which at least a large portion of people opt for no-tech spaces & times-of-day. Tech is already integrated in pretty much everything we do, my hypothesis is rather than the metaverse and virtual reality, people might just tune it out for a while and enjoy a walk, a conversation and not scrolling hands for a while.
Author : Shivani GuptaHead Fractal Dimension
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