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Special feature from FP: Amit Luthra, Managing Director of ISG at Lenovo, discusses the importance of independent data for autonomous AI.
Approximately 93% of Indian corporations intend to put money into AI by 2024, their interest isn't limited to GenAI and LLMS, they're also considering AI inferencing models. In such a situation, the creation of autonomous AI becomes essential. However, it would be incredibly challenging to achieve this without independent data.
While the achievements of artificial intelligence are indeed remarkable, what we've seen so far is merely a small part of its potential. Our attention has mostly been drawn towards generative AI and LLMs, but rapid advancements are simultaneously occurring in other facets of this field.
For instance, 93% of corporations in India have plans to invest in Artificial Intelligence (AI) by 2024. They are not solely interested in General AI (GenAI) and Learning Management Systems (LLMS), but are also considering AI Inferencing models. These models assist them in understanding and examining circumstances and suggesting suitable actions. While GenAI may have invoked a mixture of wonder and fear, for Indian enterprises, the future lies in analytical AI, automation, edge-computing, and AI inference.
During the reveal of 'CIO Playbook 2024 – It’s all about Smarter AI' report, we had a conversation with Amit Luthra, who is the Managing Director of Infrastructure Solutions Group at Lenovo India. We discussed the potential future of AI in India.
We cover everything from the concept of data autonomy and moral structures surrounding AI, to the false notion of AI "taking" our jobs and how Chief Information Officers intend to utilize AI, not merely because it's a trending term, but due to its potential to create significant change. Here are the modified segments from our dialogue:
What changes have occurred in India's technology requirements over time, particularly in relation to AI? Four key areas have driven India's digital shift — Social Media, Mobility, Analytics, and Cloud Computing. Within Analytics, AI has always played a significant role. Most corporate software has attempted to incorporate Artificial Intelligence. Two major events occurred that altered the landscape.
Initially, the emergence of IoT led to a surge in data production from various sensors. Businesses recognized that they couldn't afford to accumulate data before analyzing it to achieve their objectives. For instance, if a manufacturing plant aimed to enhance their production quality, they needed to analyze their data in real-time, particularly if the data was time-critical. This situation amplified the demand for Edge Computing.
The emergence of GenAI and the creation of sizable LLMs would be the next step. To construct these large LLMs, you require advanced systems with substantial processing capability.
Nonetheless, I believe that although a significant portion of the current investments in India are concentrated on developing LLMs, we will need to shift our attention towards boosting investments in AI inferencing in the future. The growth rate of inferencing is expected to be much higher. This would also involve a combination of CPU-GPU workloads, rather than just depending on GPU-based systems.
What are the primary obstacles businesses, particularly startups, face when implementing AI? AI, specifically generative AI, requires a well-organized structure. The most significant hurdle is maintaining efficient data management and safeguarding data integrity. If the information fed into your AI models is poor quality, the output will reflect that. You may develop an impressive LLM that can learn swiftly and utilizes remarkable algorithms, but if the training data is incorrect, incomplete, obsolete, or of low quality, it could lead to problems.
It's equally crucial to establish a structure for analytics that aligns with particular objectives. Companies must understand the purpose behind their need for an AI model and their intended outcomes. They should question, "What is the end-goal I want to reach with the collected data?"
Numerous AI initiatives in many institutions don't succeed due to lack of a definitive purpose for implementing AI. The ultimate goal for certain companies might be to enhance customer happiness, while others may aim to boost their output.
Some might aim to establish cleanliness procedures to boost efficiency, while some might just aspire to deter theft and examine deceit. Companies need to confirm their ultimate goal, and what they intend to accomplish. Once this is defined, the appropriate technology can be implemented.
In addition, the safety and moral aspects of this situation require equal attention. You're working with information that can either boost or hinder you. Companies should consider how to handle these ethical aspects and determine their boundaries. They must also guarantee that they handle delicate data correctly, using the appropriate security measures and sensitivity.
The CIO Playbook 2024 indicates that nearly half of all CIOs struggle to fill AI-related positions. Despite India having the most significant number of engineers and developers globally, there seems to be a gap between industry needs and the existing capabilities of these professionals. AI has been a part of the engineering curriculum for over twenty-five years. However, applying what is taught in engineering institutions to the demands of the modern corporate sphere is a different story. It's unrealistic to expect a recent graduate to demonstrate proficiency in AI right off the bat.
The problem isn't a lack of resources, but rather creating a supportive structure for these resources.
This is notably difficult due to the rapid rate of change. What was deemed important last year might be irrelevant this year. A business owner may now be questioning, how can I constantly guide and educate my teams, how can I consistently facilitate an educational structure?
The problem of retention also arises. Employee poaching becomes a dire concern, particularly in the context of AI. Businesses in these situations must keep investing in their assets and also demonstrate the determination to retain these assets.
There's quite a bit of discussion happening regarding Autonomous Sovereign AI and Data Sovereignty. Given the current development progress of LLMs, what potential obstacles do you foresee in establishing sovereignty? Data sovereignty involves understanding where your data is located and guaranteeing that it remains within your country's geographical limits. Generally, data could be saved on the cloud, but the physical hosting of the cloud may be located in another country.
For instance, numerous multinational corporations formerly maintained centralized data centers situated anywhere globally, to ensure efficient and seamless operations. In such scenarios, sovereignty cannot be achieved as the data, which could include personal details of Indian nationals, is stored outside the country.
In this situation, it is vital to have data sovereignty laws, which mandate that personal information of Indian citizens, for instance, remains within the country.
After confirming this, the safety and analytics structure along with the government's involvement become crucial. These structures must guarantee that essential data isn't disclosed, and it is managed appropriately and preserved according to the regulations.
So, how do companies in India utilize this information and create an analytics structure around it? Understanding your goals and the outcomes you're aiming for is crucial. Each area and application varies.
What type of LLMs or applications might emerge in this situation? I'll illustrate with an instance. If I need to contact the customer support of a service I'm using, I'd rather call them than interact with a chatbot on an app, primarily due to the poor quality of the experience. However, we're now witnessing the rise of AI-powered voice assistants in customer service roles, with full voice activation capabilities.
A further instance could be in the retail industry. In the near future, we might replace traditional sales staff with digital counterparts that can respond to your unique inquiries, tailored to your specific needs. Imagine walking into a dealership and asking for a detailed breakdown of a new car's features. A less experienced salesperson might create misunderstanding for the potential buyer. Furthermore, if your question is highly specific and seldom asked, they may not have the necessary knowledge to provide an answer.
Picture this, instead – a virtual persona that closely resembles a human, equipped with complete knowledge about a specific product they are tasked with. After just one training session, they'll be capable of addressing any queries you have.
In addition, the company doesn't have to invest significant time in retraining or reorienting employees if any new versions are introduced. During this process, the only requirement is to guarantee that the language models, regardless of their size, are continuously learning and have access to the most recent data sets. Think about how varied the previous versions of ChatGPT are compared to the GPT4.5-powered ChatGPT.
Once the LLM is developed, it's crucial to ensure that it's transferable, current, and most importantly, simple to implement. This is where Lenovo steps in, assisting businesses with a wide array of solutions from handheld devices to cloud-based services, as AI isn't limited to a single LLM. AI covers the whole system.
The Indian government has not yet established rules regarding AI in the country. From a business standpoint, what are your expectations for these regulations? Additionally, how do you think the Digital Personal Data Protection Act, passed in 2023, will affect the advancement of AI in India? It's crucial to have a robust framework for security and ethics. How can we protect individuals from the threat of deepfakes, which have already emerged? The discussion around security and ethics will be paramount. Once the landscape is understood, entities creating LLMs will have a clear idea of the boundaries.
Business executives, whether adept at AI and technology or not, are enthusiastic about incorporating AI into their operations. So, how can CIOs effectively communicate with them to guarantee that AI is correctly and strategically applied? According to our CIO Playbook 2024 survey, business objectives and technological requirements often differ. Top-level executives are particularly interested in generative AI, possibly because it's currently a trending topic.
In 2023, Generative AI wasn't a prevalent concept, but now it has gained significant importance and has become a hot topic. People were primarily focused on customer experience and satisfaction rates in the previous year. However, currently, it has become their secondary focus as they all understand that while getting a new customer is costly, keeping an existing one is significantly simpler.
Likewise, income and earnings growth, which have always been considered the topmost priority by all companies, is now ranked third. Sustainability, which was never a primary concern, is currently the fifth most important priority due to moral considerations.
Let's now turn our attention to what the Chief Information Officer (CIO) or the tech aspect of businesses focuses on. CIOs maintain the viewpoint that automation should be the main concern. Giving priority to automation will address numerous issues. Therefore, Artificial Intelligence (AI), particularly generative AI, is considered only after automation has been implemented.
To effectively handle AI's high-performance tasks, you'll require a specially designed infrastructure. Moreover, it's essential to incorporate security and ethical considerations into your AI's core concepts. As previously stated, these elements will serve as a structure. AI can't function on its own; it requires a clear objective. The execution of AI is akin to devising a strategy, which consists of various components and necessitates a structure. We've always advocated for this approach and are delighted to discover that it's been validated in our recent survey involving CIOs.
What sectors should the government prioritize when integrating AI into their administrative tasks? How can AI enhance the efficiency of government operations? The government has set its sight on AI. As constituents who have elected this government, it's our duty to support their strategy. When government officials and experts interact with industry leaders like us in conferences, they seek diverse opinions and input on multiple topics, encouraging us to express our views and suggest optimal practices.
I believe that if the government develops a vision for AI and establishes principles such as data sovereignty, it will be necessary for everyone to comply. Furthermore, our Prime Minister, Shri Narendra Modi, has also advocated for 'AI for All'. This is a concept we at Lenovo have been discussing for several years. We also firmly believe that the vision will become more potent when AI is accessible to everyone.
Ultimately, the burning query everyone is pondering: How will AI revolutionize work and job roles as we currently understand them? AI is simply another tech or series of instruments that will assist individuals in excelling at their tasks. Take this into account – we had troops in World War I, World War II, and all subsequent conflicts. During World War I, troops were equipped with .303 rifles, but today they possess LMGs and MMGs. Despite the shift in weaponry and the dramatic evolution of technology since then, soldiers remain essential.
AI is simply another instrument, similar to firearms, that will allow them to perform more efficiently and handle advanced machinery.
Indeed, AI will transform the characteristics and possibly the standard of work. However, it won't eliminate jobs or render them obsolete. There might be less necessity for certain job roles, but new opportunities will also emerge.
I'm of the opinion that AI will lead to increased wages due to the necessity for enhanced skills. Individuals will be required to develop themselves, or risk becoming irrelevant. Consider the transformation in the IT field. Two decades ago, I used to inquire during job interviews if applicants were familiar with SAP sizing, and exchange sizing. Nowadays, such queries are no longer relevant as they've become outdated.
If an individual highlights their expertise in PowerPoint on their resumes in this modern era, but lacks knowledge in integrating PowerPoint with AI, they're missing an important skill. AI is a tool that will facilitate our evolution. Ultimately, it's about staying updated, maintaining employability, and remaining pertinent to the industry. To achieve this, self-improvement is necessary.
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