Decoding the Generative AI Revolution: Insights from Professor Mohanbir Sawhney
The digital world is undergoing a seismic shift, and generative AI is at the epicenter. To understand this transformation, I spoke with Professor Mohanbir Sawhney, Associate Dean for Digital Innovation at Northwestern Kellogg School of Management. A leading authority on digital marketing and business innovation, Professor Sawhney offered a comprehensive view of how generative AI is changing the game and the crucial steps businesses should take to thrive.
Professor Sawhney emphasized that marketing, at heart, is about human interaction, making it a perfect fit for generative AI. Unlike traditional machine learning that depends on structured data, generative AI excels in conversational exchanges, content creation, and dynamic engagement. “Generative AI boosts both productivity and quality at every stage of the customer experience lifecycle—from gaining insights and segmenting customers to crafting offers, running campaigns, and analyzing results,” he explained.
For example, businesses used to rely heavily on structured customer surveys. Now, AI-powered conversations can dynamically extract consumer sentiments in real time. Platforms such as Salesforce Einstein and Microsoft Copilot accelerate the creation of customer profiles, tailored marketing campaigns, and highly personalized content. To successfully incorporate generative AI, Professor Sawhney suggests a two-pronged approach. AI can refine internal operations, such as summarizing meetings and analyzing documents, or it can enhance customer-facing experiences, like AI-powered chatbots for sales and support. Some applications offer quick wins in productivity, while others promise industry-specific transformations.
Financial services, for example, could soon use AI-powered wealth advisors to deliver personalized investment recommendations. Retailers might use AI-driven digital twins to revolutionize e-commerce by negotiating purchases based on individual preferences, ushering in an era of “bot-to-bot commerce.” Generative AI is also addressing unique challenges and improving efficiency across industries. AI-powered image recognition can diagnose equipment failures, reducing costly technician visits. Companies like Awiros in India are using deep learning for video analytics to enhance field service efficiency in industries like HVAC and aerospace maintenance. AI-driven contract lifecycle management tools automate contract generation and negotiation, improving efficiency. LawGeex, an Israeli company, specializes in automated contract review to streamline legal processes with AI. AI transcription tools can automatically populate electronic health records, streamlining doctor-patient interactions. Drones equipped with AI-powered image analysis can assess soil health, detect pests, and optimize harvesting schedules. AI models can quickly assess post-disaster damage using aerial imagery, speeding up the claims process.
Professor Sawhney stressed that these applications are not isolated solutions, but rather pieces of a comprehensive AI ecosystem, combining traditional machine learning, deep learning, and generative AI to achieve optimal results. For startups, cost-effective AI adoption is key. “Instead of investing in a suite of specialized tools, startups should opt for a platform-based approach—leveraging AI capabilities within robust ecosystems like Salesforce, Adobe, or Microsoft Dynamics,” he advised. By embedding AI into existing infrastructure, startups can avoid excessive subscription fees while ensuring scalability.
One of the most exciting aspects of generative AI is its potential to blur the lines between “high-tech” and “high-touch” customer interactions. Professor Sawhney highlighted Mindbank AI, a startup developing AI-driven digital twins that learn user preferences to provide personalized mental health support. Such innovations have profound implications across industries, from AI-powered therapists to virtual financial advisors. However, these advancements also raise critical ethical concerns. “The more AI knows about you, the greater the privacy risks. If a digital twin is hacked, it’s not just data theft—it’s identity theft at an unprecedented level,” he warned. As AI-driven personalization expands, establishing strong data security frameworks will be essential.
As AI adoption accelerates, businesses must navigate complex legal and ethical landscapes. Key issues include intellectual property and copyright, as AI models are trained on vast datasets, often without clear attribution or compensation. AI models can also perpetuate biases present in training data, necessitating robust oversight to prevent discriminatory outcomes. The EU AI Act classifies AI applications by risk level, with stricter compliance requirements for high-risk use cases. Future regulations will likely define global AI governance.
Looking ahead, the pace of AI progress continues to astound. “By 2027, AI models will have the cognitive capabilities of PhD-level researchers,” he predicted. Reports suggest that by 2026, over 20% of U.S. energy consumption will be dedicated to AI data centers—raising concerns about sustainability and infrastructure readiness. The long-term impact of generative AI will depend not just on technological breakthroughs but also on society’s ability to manage change responsibly. The key challenges will include workforce reskilling, cost-benefit optimization, and establishing ethical AI governance.
For students and early-career professionals, Professor Sawhney emphasized three key areas. First, understanding foundational disciplines like linear algebra, statistics, and computer science is crucial to leveraging AI effectively. Actively using AI tools will provide hands-on experience and practical knowledge. Finally, as AI democratizes knowledge, critical thinking and inquiry skills will become more valuable than rote memorization. “The biggest asset a young person can have is curiosity. In a world where AI can generate answers, the key differentiator will be knowing the right questions to ask,” he concluded.
Generative AI isn’t just another technological advance—it’s a paradigm shift. It is changing how businesses operate, how customers engage, and how industries evolve. As AI capabilities continue to grow exponentially, companies must strategically integrate AI into their workflows, while navigating ethical and regulatory challenges. The future is uncertain, but one thing is clear: AI is here to stay, and its impact will be transformative. Responsible AI adoption, continuous learning, and ethical foresight are key to navigating this next era of business innovation, as Professor Sawhney’s insights clearly demonstrate.
