In a rapidly evolving digital landscape, generative AI is revolutionizing the way businesses operate. To explore this transformation, I had the privilege of interviewing Professor Mohanbir Sawhney, Associate Dean for Digital Innovation at Northwestern Kellogg School of Management. As a leading expert in digital marketing, artificial intelligence applications, and business innovation, Professor Sawhney provided a comprehensive overview of how Gen AI is reshaping industries and the critical strategies businesses should adopt to maximize its potential.
Professor Sawhney emphasized that marketing, at its core, revolves around human interaction, making it a natural fit for generative AI. Unlike classical machine learning applications that rely on structured data, Gen AI thrives on conversational interactions, content generation, and dynamic engagement.
“When you look at the customer experience lifecycle—from insights and segmentation to offer creation, campaign execution, and performance analysis—generative AI enhances productivity and quality at every stage,” he explained.
For instance, businesses traditionally relied on structured customer surveys for insights, but now, AI-powered conversations can dynamically extract consumer sentiments in real-time. Likewise, platforms like Salesforce Einstein and Microsoft Copilot enable the automated creation of customer personas, targeted marketing campaigns, and hyper-personalized content.
To effectively integrate generative AI, Professor Sawhney suggests a two-by-two framework. AI can optimize internal workflows, such as automating meeting summaries and document analysis, or enhance customer-facing experiences, such as AI-powered chatbots for sales and support. Some applications provide immediate productivity gains, while others offer industry-specific, game-changing transformations.
For example, financial services may soon deploy AI-powered wealth advisors that provide personalized investment insights. Similarly, in retail, AI-driven digital twins could revolutionize e-commerce by autonomously negotiating purchases based on user preferences—ushering in an era of “bot-to-bot commerce.”
Across industries, generative AI is addressing unique challenges and driving efficiencies. AI-powered image recognition tools can diagnose equipment malfunctions, reducing costly technician visits. Awiros, an India-based company, is using deep learning for video analytics to enhance field service efficiency in industries like HVAC and aerospace maintenance. AI-driven contract lifecycle management tools can generate, review, and negotiate contracts, enhancing efficiency. LawGeex, an Israel-based company, specializes in automated contract review, helping businesses streamline legal processes with AI. AI transcription tools can auto-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 evaluate post-disaster damages in real-time using aerial imagery, expediting claims processing.
Professor Sawhney stressed that these applications are not standalone solutions but part of a broader AI ecosystem, combining traditional machine learning, deep learning, and generative AI to deliver optimal results.
For startups, cost-effective AI adoption is crucial. “Instead of investing in an array of specialized tools, startups should choose 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 line between “high-tech” and “high-touch” customer interactions. Professor Sawhney illustrated this with Mindbank AI, a startup developing AI-driven digital twins that learn user preferences and provide personalized mental health support. Such innovations have profound implications across industries, from AI-powered therapists to virtual financial advisors.
However, these advancements raise pressing 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. Establishing robust data security frameworks will be essential as AI-driven personalization expands.
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 perpetuate biases present in training data, necessitating rigorous oversight to prevent discriminatory outcomes. Inaccurate or misleading AI-generated content could have serious consequences, particularly in high-stakes fields like finance and healthcare. 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.
Professor Sawhney noted that while regulation is necessary, overly restrictive policies could stifle innovation. The challenge lies in striking the right balance between safety and progress.
Looking ahead, the pace of AI advancement is staggering. “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. Understanding core disciplines like linear algebra, statistics, and computer science is essential to leveraging AI effectively. Actively using AI tools will provide hands-on experience and practical knowledge. 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 is not just another technological advancement—it represents a paradigm shift in 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.
With insights from Professor Sawhney, it is evident that responsible AI adoption, continuous learning, and ethical foresight will define the next era of business innovation.