How Tech Innovations are Shaping the Next Decade

Chandu Nair facilitates a discussion with Mahesh Ramachandran on how AI and technology are rapidly shaping the future. They explore adoption trends, disruptive innovations, and strategic frameworks for evaluating emerging technologies, emphasizing agility, AI integration, and the risks of delayed adaptation.

The time to achieve mass adoption of new technologies has decreased dramatically

Chandu Nair, Entrepreneur & Advisor

We start our day with mobile phones. Technology permeates every aspect of our lives. From WhatsApp revolutionizing communication to smart slippers and toilets, technology is everywhere. Cars are becoming increasingly intelligent, and laptops can now offer insights that sometimes surpass those provided by even our closest family members. Homes come equipped with smart locks and a host of other smart features, making them truly intelligent.

India’s advancements in space technology demonstrate the wide-ranging impact of innovation. Space tech influences our lives in countless ways: from the clothes we wear to the cars we drive, turbines we build, communication networks, weather forecasting, climate predictions, and even food processing. The extent of its impact makes it clear why other countries are closely watching India’s progress in this field.

Oppenheimer’s vision for nuclear technology extended beyond its use in creating weapons to applications like nuclear medicine. However, its most notable impact remains the development of the atomic bomb. Looking back at historical innovations, starting with fire and the wheel, we see their lasting influence. For instance, the design of modern railway tracks is based on the width of horse-drawn carriages. The pace of innovation has drastically improved productivity over time. Since 1760, human productivity has increased thirtyfold due to various groundbreaking inventions.

Moreover, the time to achieve mass adoption of new technologies has decreased dramatically. Airplanes took 68 years to reach 25% adoption, while Pokémon Go achieved this milestone in just 19 days, Aarogya Setu in 13 days, and ChatGPT reached over 100 million users in just about two months.

Waiting too long to embrace new technologies could leave us struggling to catch up.

Mahesh Ramachandran, Founder, Tech Innovations

It was once advised to Henry Ford’s lawyer not to invest in the Ford Motor Company because, as they put it, “The horse is here to stay, but the automobile is only a novelty.” However, the lawyer went ahead and invested $5,000, which eventually turned into $12.5 million. The point I’m trying to articulate is this: How do we determine whether a new technology, product, or service is just a passing fad or something that will endure and fundamentally change an industry?

It’s also interesting to note a pattern when it comes to new technologies. We tend to overestimate their impact in the short run (five years or less) and underestimate their effect in the long run (more than five years). When a new technology emerges, it’s often accompanied by a great deal of hype about its potential.

Take artificial intelligence as an example. Just after the release of AI chatbots like ChatGPT, there was significant euphoria surrounding their capabilities. Gartner refers to this phenomenon as the Hype Cycle. It suggests that new technologies go through phases: an initial period of euphoria, followed by a phase of disillusionment, and eventually a plateau of productivity. Understanding this cycle is crucial for assessing whether a technology will thrive or fade away.

Predictions about the future are always uncertain, and that’s the only certainty we have. As a quote goes, “The best way to predict the future is to invent it.” To navigate this uncertainty, it’s important to adopt a framework to make sense of the technologies emerging around us.

The rapid pace of adoption reflects the accelerating impact of digital technology. As leaders, we must recognize that early adoption is no longer a luxury. Waiting too long to embrace new technologies could leave us struggling to catch up. However, the key challenge remains: how do we identify which technologies to adopt and which might become fads? To address this, we need to understand the context in which we are operating, evaluate the technologies available, and consider which ones are likely to succeed or fail.  

Chandu Nair: How can leaders and professionals develop a framework to evaluate and adopt technological innovations?

Mahesh Ramachandran: To build such a framework, we can learn from examples of what larger companies have done when it comes to adopting technologies. Let’s take AI as a case in point. Companies—be it large corporations, mid-sized businesses, or smaller firms—adopting AI successfully tend to follow a structured approach and consistently identify practical use cases.

The first step is to ensure there is a strategic fit for the business. If the technology doesn’t align with your business goals or operations, adopting it may not make sense. That’s the foundational principle.

The second step is to assess the maturity of the technology. Has it undergone multiple iterations, or is it still in its infancy? Technologies that show significant progress often attract substantial investments and attention from universities, scientists, and R&D initiatives. If you observe widespread innovation and funding in a particular field, it’s a strong indicator that the technology is here to stay.

The third step is to evaluate whether the technology offers a new business model or transformative value. If it does, you’ll need to design and implement a consistent process for adoption. This requires not just technical integration but also changes in workflows and organizational culture.

This framework helps businesses make informed decisions about which technologies to adopt and how to integrate them effectively.  

Chandu Nair: Why do supposedly smart people in well-run companies fail to adopt new technologies? Can you provide some examples to illustrate what went wrong?

Mahesh Ramachandran: One of the biggest challenges for highly competent people is recognising and accepting disruption within their industry. Take the example of Kodak, which was once a dominant player in the photography industry. Interestingly, Kodak invented the first digital camera in 1975, but the leadership, who were experts in chemical and film technology, underestimated the importance of digital photography.

They viewed digital technology as a threat—something that would cannibalize their core business of film production. This hesitation illustrates a classic dilemma: when you’re excelling at a particular business, it’s hard to shift focus to a new, unproven technology that might jeopardise your existing strengths.

This challenge arises because success creates a sense of certainty. Leaders tend to rely on historical evidence, believing that what worked in the past will continue to work in the future. As a result, they fail to identify the significance of emerging technologies or the disruption they could bring.

Chandu Nair: What happens when companies fail to discern the difference between a lasting trend and a passing fad? For example, as investors, we’ve invested in SaaS companies. Now, with the rise of AI, SaaS companies are facing a significant challenge: should I integrate AI into existing products, or should I create a completely new, AI-first product?

Mahesh Ramachandran: Let me share some examples of companies that failed to adapt to changing trends and technologies, despite their initial dominance.

Blockbuster vs. Netflix: Blockbuster was a leader in the video rental business with physical stores offering VHS tapes and later DVDs. In the early 2000s, Netflix, initially a DVD rental service, began exploring video streaming. At the time, streaming technology was in its infancy. Blockbuster had the opportunity to acquire Netflix for $50 million but dismissed the idea, believing streaming wouldn’t take off. By 2010, Blockbuster had filed for bankruptcy, while Netflix became a global entertainment giant.

Nokia and Blackberry: Nokia was once the world’s leading mobile phone manufacturer. However, it failed to recognise the significance of smartphones and touchscreen technology, focusing instead on feature phones. When Apple launched the iPhone in 2007 and Android devices followed, Nokia’s market share plummeted. By 2014, Nokia’s mobile division was sold to Microsoft.

Similarly, Blackberry, synonymous with business phones, missed the transition to touchscreens and the app ecosystem, which proved critical in the smartphone era. Like Nokia, Blackberry’s dominance faded quickly.

Xerox’s Untapped Innovations: Xerox’s Palo Alto Research Center (PARC) was a hub of groundbreaking innovations, including the graphical user interface (GUI), the computer mouse, and Ethernet. Despite its pioneering work, Xerox failed to commercialise these technologies effectively. Companies like Apple and Microsoft capitalised on Xerox’s ideas, turning them into mainstream successes.

Yahoo’s Missed Opportunities: Yahoo, once a dominant search engine, failed to adapt to the rise of algorithmic search technology, which Google perfected. Additionally, Yahoo missed the chance to acquire Google and Facebook in their early days, passing up transformative opportunities. Eventually, Yahoo was sold to Verizon in 2017, marking its decline.

Polaroid and Digital Photography: Polaroid, an icon in instant photography, failed to embrace digital photography, sticking to its traditional film-based business. This inability to pivot led to its downfall, despite the growing popularity of digital cameras.

Sears and E-commerce: Sears, a retail giant in the U.S., couldn’t keep up with changing consumer preferences and the rise of online shopping. Failing to adapt to the e-commerce wave, it declared bankruptcy while companies like Amazon flourished.

Other Examples of Missed Opportunities:

• MySpace: Once a leader in social media, it failed to innovate and compete with Facebook’s user-friendly interface and growing ecosystem.
• Google Glass: Introduced with great fanfare, Google Glass offered innovative features like voice search and augmented reality. However, privacy concerns, high costs, and limited applications led to its failure in the consumer market.
• 3D Television: Promoted as the next big thing in home entertainment, 3D TVs never gained traction due to a lack of content and the inconvenience of using 3D glasses.
• MiniDisc and Segway: Both technologies were ahead of their time but didn’t achieve widespread adoption due to high costs, impracticality, and competition from better alternatives.
• Second Life: As an early virtual world platform, it was a precursor to concepts like the metaverse. However, it never gained mass appeal, with limited consumer interest in a fully virtual lifestyle.

The key takeaway from these examples is that even great companies can fail if they don’t recognise or act on technological disruptions. Success depends on discerning whether a new trend will fundamentally reshape the industry or is just a passing fad. That leads to the natural question: How do we identify which technologies to adopt and which to avoid?

Chandu Nair: Amazon started off as an online bookseller because, as Jeff Bezos says, every day is Day Zero for Amazon. Another interesting example is Microsoft, which was initially a very offline-focused player but managed to transition into the cloud space and remains highly relevant today. Microsoft continues to innovate and evolve, including its role in OpenAI and other cutting-edge technologies. There are examples of companies that have managed to stay ahead of the game. What has been the impact of recent technologies, and what are the issues arising because of this?

Mahesh Ramachandran: What is interesting for us to understand is the recent growth of OpenAI’s ChatGPT and how different companies have adopted strategies to either integrate OpenAI’s ChatGPT or develop competitive approaches. OpenAI’s ChatGPT, in a matter of months, created a revolution where what was once considered a niche technology in natural language processing and generative AI has become mainstream. Most of us now see how it can shape content creation, assist in writing code, and even enhance customer support.

For years, we never thought a major alternative to Google could exist as a search engine. That said, Google has now started exploring ways to compete with OpenAI’s ChatGPT. The key question is whether companies should compete directly with ChatGPT or adopt and work with it. Google decided to compete directly by introducing Bard and now Gemini. Bard was a failure, and Gemini is experiencing only limited success. Currently, Google offers both Gemini and conventional search on the same page.

On the other hand, Microsoft was very quick to integrate ChatGPT into its products, placing it ahead of the curve. Microsoft also made a substantial investment in OpenAI, which has helped it significantly. This integration has made Bing a more popular search engine because ChatGPT’s technology was seamlessly incorporated into Bing. Today, if you use Bing, you will see much better results than before. Of course, there are many pitfalls of technology.

Chandu Nair: People often say that AI is more suited for youngsters. However, in reality, only a few companies build the large language models, and the real value comes from the prompts that are given. The quality of these prompts does not necessarily come from youngsters but from seasoned professionals with domain expertise and experience. For instance, Supreme Court Justice Dave recently posed a thought-provoking question at a medical conference: If a surgery performed using AI or a robot goes wrong, who should be held responsible?

Mahesh Ramachandran: The first part of this process is called prompt engineering. If you phrase a question more specifically and accurately, the AI-generated response will be more relevant. On the other hand, the broader the question, the vaguer the answer tends to be. One major challenge in AI is hallucination—when AI generates responses that are factually incorrect or entirely fictitious. For example, while AI can create a movie script, it might also fabricate an answer to a question. It’s essential to be cautious of this risk.

There are multiple governance challenges with AI. Data privacy is a significant concern, as is AI bias. Organisations like ISACA are working to establish frameworks for AI governance. An effective AI framework must incorporate ethical principles to ensure that AI systems do not produce biased or incorrect answers and minimise the risk of hallucination.

Explainable AI is an important emerging concept. ChatGPT, for instance, has introduced new versions that explain the rationale behind their decisions. For example, if a bank uses an AI system to deny a loan application, it must be able to explain why the loan was denied. Currently, such decisions are often made within a ‘black box,’ offering no transparency. Similarly, if an AI system shortlists candidates for a job and rejects an applicant, it should provide specific reasons for the rejection.

In short, governance is crucial to prevent AI from going rogue. It ensures ethical use while enabling organisations to leverage AI strategically for success.

Chandu Nair: When we talk about technology, everyone tends to focus on digital software or AI-related innovations. However, there are other industries where significant transformations have occurred due to technological advancements. Can you share one or two examples of industries that were not traditionally tech-heavy but have been revolutionised by new technologies?

Mahesh Ramachandran: One example is the adoption of electric vehicles (EVs), popularised by Tesla, which transformed the automotive industry. Another fascinating area is precision agriculture and AI applications in dairy farming. For instance, sensors are being used on cattle to collect data, leading to remarkable results in improving productivity and efficiency.

An excellent example of this is Stellapps, an IIT-incubated company that provides dairy IoT solutions. Stellapps uses wearables, IoT devices, and data analytics to enhance milk production and streamline dairy operations.

Additionally, the Indian government has introduced ONDC (Open Network for Digital Commerce), a transformative initiative. ONDC is an online marketplace that enables small and medium enterprises (SMEs), Kirana stores, and small retailers to digitize their operations. As part of India’s digital public infrastructure, it offers small businesses access to tools that level the playing field against e-commerce giants like Amazon. This initiative is empowering and has the potential to revolutionise retail by supporting inclusivity and growth for smaller players.

Chandu Nair: I understand that even Arun Ice Cream’s Ibaco uses IoT sensors to maintain proper freezer temperatures, ensuring the taste and quality of their ice cream. Can you shed some light on one of the largest industries in the US—the healthcare sector—which is larger than India’s GDP? What innovations are happening there?

Mahesh Ramachandran: One remarkable example is Aravind Eye Care, one of the largest not-for-profit organizations in the world, known for its extensive cataract surgeries. They have amassed a vast amount of patient data related to eye health. By conducting regression analysis on this data, they enabled Google to perform correlation studies using retina scans. This technology can predict various conditions, such as a person’s propensity to develop cancer, their gender, and other health indicators—all from a single retina scan. This demonstrates the potential of technology to diagnose multiple illnesses through advanced imaging.

Another significant innovation is the use of telemedicine powered by AI in healthcare. For instance, a company called Curie.ai employs AI to analyse medical images, particularly for promoting liver health. In the future, it might become mandatory for radiologists to use AI software as a second opinion while diagnosing conditions. This integration of AI ensures greater accuracy and efficiency in healthcare delivery.

When evaluating new technologies, it is essential to assess the problems they aim to solve. If a technology doesn’t address a real problem and exists merely for the sake of innovation, it may not be worth pursuing. A clear business case is critical. Additionally, consider the role of early adopters and the broader ecosystem: Does the technology require substantial changes in hardware or infrastructure?

Another critical factor is the network effect. Technologies that leverage a network effect often succeed. To understand this concept better, you can refer to the NFX Network Bible. Scalability and accessibility are also vital considerations. Can the technology scale effectively, and can it cater to a broad user base? Furthermore, regulatory and monitoring mechanisms need to be in place. Even with technological advancements, societal acceptance is crucial. The public must embrace the technology beyond its hype.

Finally, follow the funding and talent. If investors are backing a technology, it indicates strong growth potential. Observe consumer behavior and trends: Does the technology align with evolving customer preferences? Timing is also critical. It’s okay to adopt a ‘wait and watch’ approach if it seems too early to implement a particular technology

Chandu Nair: How can companies establish a framework to pivot successfully?

Mahesh Ramachandran: Let me share a framework that I’ve been following. Start by identifying signals for change. These signals could include shifts in customer behavior, emerging technologies that create new opportunities, or increased competition gaining ground.

Next, consider the 3 Ps: Performance, Pain, and Potential. Can your current business model deliver results in the evolving technology landscape? Are customers facing a pain point that can be addressed with the new technology?  Is there an opportunity for the new technology to unlock additional revenue streams?

When pivoting, embrace incremental changes rather than making drastic shifts. Avoid abruptly stopping what you’re already doing. Instead, test changes incrementally and pivot slowly, refining your approach as you go. In essence, leaders must view their business models as temporary blueprints rather than fixed structures. Be prepared to adapt and make changes, when necessary, but approach adoption thoughtfully and methodically to ensure

We have yet to realise the full potential of AI, but significant funding is flowing into the field. This suggests that, for many companies, there isn’t a strong business case to shift to AI yet. Those profiting from AI today resemble the toolmakers during the gold rush—the NVidias and data center operators of the world are investing heavily in this space.

However, with AI’s current energy demands, particularly from power-intensive GPUs, the reliance on fossil fuels could become a significant concern. Unless there is a breakthrough in energy generation or algorithmic efficiency, managing the environmental impact of AI will be a daunting challenge. Addressing this issue requires collective responsibility and innovative solutions.

In the meantime, individuals and organisations should focus on leveraging the tools available today to become significantly more productive—perhaps tenfold—rather than risk becoming irrelevant. Skills like prompt engineering and other AI-related capabilities are essential to maximizing the benefits of these tools.

For leaders, fostering a culture of experimentation is critical. Failure should be viewed as a stepping stone rather than a setback. Encouraging this mindset helps teams innovate and adapt.

Key areas to focus on for future success include:

1. Data literacy: Develop the ability to collect, analyse, and extract meaningful insights from data. This skill is essential across all roles.
2. Agility in decision-making: Emphasise shorter decision cycles. Start small, iterate quickly, and refine as you go. Embrace concepts like minimum viable products (MVPs).
3. Emotional intelligence and resilience: With disruption comes uncertainty. Leaders must foster trust within teams and remain resilient.
4. Human literacy: Understand human behavior and thinking to enhance decision-making and team collaboration.

The future may be uncertain, but the ability to learn, adapt, and lead with empathy will always be in demand. Success will come to those who embrace change, leverage technology effectively, and prioritise human-centric leadership.