Generative AI is now 80% cheaper than it was just 16 months ago — a shift that businesses must grasp to stay competitive said Dr. Ayesha Khanna, AI thought leader, board member and consultant to CEOs worldwide.
The rapid decline in AI costs means that companies delaying adoption risk being outpaced by rivals leveraging its efficiency. But AI adoption isn’t just about speed, it’s about responsibility.
Amid the buzz around GenAI, Dr. Khanna highlights a critical shift in how businesses, particularly in Asia, approach AI adoption. The region's diversity in language, culture and religion presents unique challenges. "One of the heads of a leading Southeast Asian bank told me, we want to build a customer service agent that can handle 80% of our FAQs, but we serve diverse communities and we’re afraid."
This concern highlights a crucial point: AI adoption isn’t just about efficiency, it’s about inclusivity, cultural sensitivity and regulatory compliance. Businesses must navigate these complexities while harnessing AI’s benefits.
A prime example is Sarvam AI in India, which enables businesses to communicate with customers in their local languages, making digital services accessible to those who may not be literate. Google is taking a similar approach in Africa, where its AI tools now support 25 languages, bridging communication gaps and unlocking new economic opportunities.
As AI-powered communication advances, its economic and social impact will be profound, especially in regions where language barriers have historically limited market access. The future of AI isn’t just about technological capability; it’s about ensuring that innovation reaches and empowers diverse communities worldwide.
Dr. Ayesha Khanna, AI thought leader, board member and consultant to CEOs worldwide
The challenges of enterprise data with AI
A major challenge businesses face is making sense of their vast data repositories. Dr. Khanna explained, "One of the problems with older companies is that they have a lot of data, but not enough documentation about how the data is structured or where it is. Even if you put everything in a data lake, it's still impossible to make use of it."
Companies like Grab, a mobile-first platform with 40 million users across eight countries, are tackling this issue head-on. Their internally developed AI model, Hubble IQ, utilizes elastic search and AI-powered tools like Glean, now valued at $4 billion, to extract actionable insights from enterprise data. The key takeaway? The real value of AI isn’t just in organizing data but in making it actionable.
The fine line between personalization and intrusion
Customer engagement strategies are also transforming, moving away from broad personas to hyper-personalized experiences driven by behavioral data. But as Dr. Khanna notes, companies must be cautious: "It’s a fine line between creepy and personalized. Truly hyper-personalization isn’t just about optimizing to the maximum; it’s about empathetically considering what your customers are ready for."
One of the biggest concerns surrounding AI adoption is its impact on jobs. She warns against the simplistic view of AI replacing human workers: "The worst mistake people make is thinking, ‘Now we don’t need people anymore, so we’ll just fire them.’ That’s a terrible mistake. Nobody knows your domain, culture and business problems better than your employees."
Forward-thinking companies recognize that AI should enhance human capabilities, not replace them. For example, IKEA repurposed customer service agents into interior design consultants, leveraging their experience in a way that AI alone couldn’t replicate. The key is not just cutting costs but reinvesting in human expertise.
ROI measurement: The AI adoption dilemma
One of the most significant hurdles in AI adoption is measuring return on investment (ROI). Many organizations struggle to quantify AI’s direct impact because multiple projects contribute to the same business outcomes. Dr. Khanna explains: "True leadership isn’t about focusing on one AI project. It’s about an AI strategy that pervasively raises revenue or reduces costs."
Publicly traded companies are increasingly discussing AI in earnings calls, demonstrating its growing strategic importance. Walmart, for instance, leveraged GenAI to enrich product descriptions for its 425 million-item catalog, improving customer-product matching and driving e-commerce revenue.
Similarly, Klarna implemented an AI assistant to streamline customer interactions, saving $40 million annually while increasing efficiency and engagement. While AI’s ROI can be difficult to pinpoint, its operational and revenue-generating benefits are undeniable.
Speed is a defining factor in AI’s impact, with Silicon Valley companies setting the pace. Amazon CEO Andy Jassy described GenAI as a "game changer," highlighting how it reduced software upgrade efforts from 50 developer days to mere hours, saving the company $260 million annually.
Beyond coding efficiencies, AI-driven automation is revolutionizing industries. Toys ‘R’ Us, for example, created the first AI-generated advertisement, with 80% of the work, including scriptwriting, voice generation and video production, completed by AI. However, the remaining 20% involved human oversight, which ultimately determined the ad’s quality and effectiveness.
As Dr. Khanna emphasized: "That 20% of work was worth 1000 times in value in the final output. AI’s true power isn’t just in automation but in augmenting human expertise."
Embracing innovation with responsibility
The AI revolution is here, and businesses must move beyond hype to strategic, responsible implementation. Companies that invest in AI not just for efficiency but for long-term innovation will lead the next wave of digital transformation.
However, success depends on more than just technology. Organizations must create a culture that encourages experimentation, learning from failures and upskilling employees to work alongside AI.
As Dr. Khanna puts it: "We can’t succeed if we don’t have some failures. Asia, in particular, needs a mindset shift toward embracing failure as part of innovation."
Ultimately, the winners in the AI race will be those who balance technological advancements with human ingenuity, ensuring AI is a tool for progress rather than disruption.
