Businesses today face an overwhelming “data tsunami” — a surge of data that, while valuable, often hampers application performance and strains architectural scalability. The more data an organization manages, the more challenging these issues become. With AI workloads adding to data management complexities, enterprises are looking for ways to achieve scalability, maintain performance, and meet evolving data demands.

CIO Dimension spoke with Bhanu Jamwal, Head of Presales & Solution Engineering, APAC, TiDB, to explore how a modern and innovative approach to databases is helping businesses overcome these challenges and innovate. TiDB – an open source distributed SQL database developed by PingCAP – tries to provide some answers. 

How is PingCAP’s TiDB positioning itself in the competitive database market, particularly considering the rapid growth of digital native businesses in India?

We’ve witnessed a remarkable surge in digital native businesses across India, spanning e-commerce, logistics, fintech, gaming, SaaS, and more recently, the emerging healthcare and Gen AI sectors. This explosive growth, however, comes at a cost – a massive influx of data.

Their increasing reliance on data creates a greater demand for scalable and reliable data solutions. Traditional databases often struggle to scale to meet this kind of growth and volume. Imagine a scenario where an e-commerce platform, during a peak sales event experiencing a huge surge in transactions. Traditional databases, such as MySQL or PostgreSQL, may encounter performance bottlenecks under such pressure.

Businesses are therefore compelled to either implement workarounds or adopt modern database solutions that can address these challenges. And these challenges now go beyond just scale. High availability is paramount; downtime is simply not an option today. Real-time analytics is another critical factor businesses. For instance, a bank needs to be able to detect fraudulent transactions in real-time to safeguard customer funds. So, it needs, scalability, availability and analytics- all at the same time. AI introduces further complexities and challenges to this mix. 

Recognizing these evolving needs, TiDB was born in 2015. We strive to address these challenges with our distributed architecture, which allows businesses to scale as their data grows. Distributed SQL databases have in fact emerged as an effective answer to modern data challenges. This approach has been gaining traction, as evidenced by Amazon’s recent entry into the distributed SQL market, acknowledging the growing demand for such solutions. 

How do you view the SQL vs. NoSQL debate, and how does TiDB position itself in this context?

The SQL vs. NoSQL debate has been ongoing for some time with both playing pivotal roles in enterprise environments. While NoSQL databases offer flexibility for specific use cases, they often require significant development effort to maintain data structures. SQL, on the other hand, remains the de facto standard for data access, with a vast pool of skilled developers. 

It’s interesting how many NoSQL databases, which originally distanced themselves from SQL, are now adding SQL layers to their platforms. It shows that SQL remains a simple and effective way to access data. 

TiDB’s aim is to bring best of both by offering a distributed SQL solution. Our Hybrid Transactional/Analytical Processing (HTAP) capabilities are designed to bridge the gap between SQL and NoSQL databases and handle both transactional and analytical workloads. 

How is AI and ML, particularly the rise of LLMs, changing the game for distributed databases, and how is TiDB capitalizing on these trends?

The rise of vector database is a trend worth mentioning in this context. Vector is becoming a powerful engine for today’s AI workloads. Though it has been around for a while, the adoption has rapidly scaled in recent years. These databases are becoming increasingly crucial for storing and processing the data generated by AI/ML models. 

But, this also makes the environment quite complex for enterprises. They often manage multiple databases for different workloads—a transactional database, a separate one for analytics, and yet another for vector storage. Managing databases is a complex process. 

At TiDB, we see an opportunity to simplify this process by offering a unified solution. Our vision is to provide a single database that handles all these workloads, eliminating the need for multiple systems. TiDB supports vector as a form of storage with TiDB serverless, enabling organizations to store and process both traditional data and vector data within a unified platform. We believe the integration of vector search capabilities into a traditional database will be a game-changer for businesses looking to leverage the power of AI and ML. 

Can you share some successful case studies of TiDB implementations in India and globally?

Absolutely! We have a number of success stories across various industries. In India, a leading e-commerce giant was struggling to manage the increasing complexity of their database infrastructure as they scaled, relying on a multitude of single-node databases. By migrating to TiDB, they consolidated their infrastructure, significantly improved performance, and gained greater operational efficiency. 

Delhivery, a leading logistics company that delivers a million packages a day, migrated to TiDB to power their real-time analytics and data marts use case, enabling them to handle thousands of requests per second with less than 100ms latency. 

Pinterest is another flagship customer that leveraged TiDB to modernize their data architecture. Globally, we’ve seen tremendous success in the fintech sector. A major Japanese fintech company, for example, experienced a 10x increase in throughput and a 30% reduction in latency after adopting TiDB for their high-volume wallet application. 

We’re also seeing significant growth in the SaaS market, where TiDB helps companies manage the complexities of multi-tenant environments and scale their databases to support growing user bases.

What are the key market opportunities for TiDB in India?

India is a dynamic and rapidly growing market with a thriving startup ecosystem. We see immense potential here. The sheer volume of data generated by India’s digital-native businesses and startups, coupled with their ambitious growth plans, creates a strong demand for distributed database solutions. India’s thriving Web3 community, its vast base of skilled developers are all opportunities that TiDB will be actively tapping into. 

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Business leaders have high hopes that artificial intelligence (AI) investments can drive market-changing innovations to transform everything from customer satisfaction to product innovation. However, a poor data foundation is holding 78% of organisations back from achieving these goals.

A new report from MIT Technology Review Insights, in partnership with Snowflake, the AI Data Cloud company, titled ‘Data Strategies for AI Leaders’ found that while businesses have big ambitions for generative AI — with 72% looking to increase efficiency or productivity, 55% betting on increased market competitiveness, and 47% aiming to see more innovation in products and services — the foundational data strategy needs to be improved to maximise AI’s potential.

Businesses need strong data foundations, powered by modern cloud data platforms, to enable them to harness their own stores of data, and also vast volumes of previously inaccessible data, largely from unstructured data such as videos and images. According to the report, just 22% of business leaders say they are ‘very ready’ to engage with AI, while 53% are ‘somewhat ready’. Higher readiness correlates with fewer challenges related to accessing scalable computing power, data silos and integration issues, and data governance. Despite many business leaders’ confidence in the results AI can deliver, they are realising that data is key to how quickly and effectively they can unlock AI’s value.

Another challenge facing organisations is deploying AI at scale. 95% of those surveyed reported facing hurdles when implementing AI. 59% of respondents cited data governance, security, or privacy as their most prevalent challenge, followed by data quality and timeliness (53%), and costs of resources or investment (48%). Spending and resourcing decisions, including those needed to improve data foundations, are a challenge when it comes to any technology investment. But the cost of generative AI itself is decreasing, with enterprises having begun to build smaller large language models (LLMs) that remain equally capable, and less expensive.

“Many of today’s organisations have big ambitions for generative AI: they are looking to reshape how they operate and what they sell,” says Baris Gultekin, Head of AI, Snowflake. “Our joint research shows that as organisations feel increasing urgency to deploy AI applications, they are realising that their data can help them deliver insights from previously untapped sources of information. A strong data foundation is at the core of generative AI capabilities, and business leaders need to move quickly to deal with concerns such as data security and cost, and establish the foundation they need to deliver on the promise of AI.”

Generative AI’s benefits are becoming visible to those companies farther along their data journey, because they invested heavily in data foundations and are now being rewarded by bringing AI onto that data. For any business looking to capitalise on AI, they must first establish a robust data foundation, which covers a broad collection of processes and assets involved in the gathering, aggregation, storage, and accessibility of organisational data. Investment in data foundations across an organisation will enable much more powerful generative AI users, while also reducing governance and security concerns.

The post Four out of five businesses underprepared for Gen AI due to poor data foundations: Survey appeared first on CIO Dimension.

For CIOs and CTOs, AI can automate routine tasks like network monitoring, system maintenance, and cybersecurity, reducing the need for hands-on management. This gives them more time to focus on innovation and developing long-term technology strategies. AI also helps them make data-driven decisions by providing insights from vast amounts of data, which allows companies to anticipate customer needs, improve operations, and create new products or services.

However, AI also presents new challenges. CIOs and CTOs will need to navigate ethical concerns around AI, such as data privacy and the impact of automation on jobs. They’ll also need to continuously update their skills to keep up with rapidly advancing AI technologies.

Moreover, AI is driving the need for strong collaboration between CIOs/CTOs and other departments like human resources, marketing, and finance. For instance, AI systems that monitor employee performance or improve customer experience require cross-functional alignment. This means CIOs and CTOs must become more integrated with the business side, ensuring that AI initiatives are aligned with overall company goals.

As companies continue to adopt AI-driven solutions, the demand for CIOs and CTOs who understand both technology and business strategy will grow. Embracing this shift will help them not only lead but also innovate in today’s competitive landscape. Those who stay ahead will be the driving force behind their organization’s success.

References:

• Forbes: How AI is Reshaping the Role of the CIO
• Gartner: Gartner Says Three-Fourths of CIO Job Description Will Be Rewritten by 2024

The post “How AI is Transforming CIO and CTO Roles: From IT Management to Strategic Leadership” appeared first on CIO Dimension.

The Generative AI (GenAI) wave is upon us. Every enterprise is buzzing about its potential, and investments are skyrocketing. But amidst the excitement, a crucial question remains: What truly separates successful GenAI projects from the rest?

In a recent visit to India, Jihad Dannawi, Regional VP – APAC, DataStax (a leading real-time vector database company) sheds light on the hidden complexities of AI projects. This exclusive interview with CIO Dimension delves into:

• The power of the right database in unlocking the true potential of Large Language Models (LLMs)
• Why developers are the backbone of GenAI and how to empower them
• India’s unique advantage in leading the GenAI revolution

Edited excerpts: 

DataStax positions Astra DB as the ‘go-to’ database for GenAI applications. Can you explain why Astra DB stands out as the best choice for GenAI applications compared to your competition?

For starters, we are a 14-year-old company that has built the most widely used enterprise-grade database. It has been delivering high scale, high performance, and high availability — even before Generative AI came in. 

We are at a stage where a lot of organizations are scaling GenAI from PoC to production. At PoC level, organizations are not looking at scale or performance. But when they move to production, they suddenly start to have performance issues. They need to be able to pull in massive amounts of data to increase the relevancy and accuracy of the models, which means that the database must be bigger.

But volume often comes in the way of performance at the production stage. When organizations move beyond entry level scenarios to more mature use cases, they need to have a database that can handle their speed, scale, and performance requirements      – all at a reasonable TCO. A lot of databases in the market struggle to balance these aspects. 

An industry benchmark clearly demonstrates that DataStax’s Astra DB Serverless (the cloud-native database service built on Apache Cassandra) significantly outperforms the competition in throughput, latency, relevance, and TCO.  

Another key offering from DataStax is RAGStack, which claims to reduce the complexity associated with Gen AI for developers? Could you elaborate? 

When it comes to generative AI, one of the key issues that the developer community faces – especially when they start building the big stuff for production – is that they don’t know where to start. Because there are so many components and tools available in the market, it’s hard to decide what to use –  especially when dealing with vector databases.   

That’s why DataStax built a layer for the developers called RAGStack. This helps them build production-grade retrieval augmented generation (RAG) Gen AI applications in a simpler and faster way – without the developers having to learn everything from      scratch. It’s fully open source, connected and improves developer productivity significantly. It drastically simplifies the process of implementing RAG – the process of providing context from outside data sources to deliver more accurate LLM query responses. 

RAGStack is part of our vision is to enable and empower developers to build GenAI applications in production more efficiently. We are helping developers and companies to quickly innovate.  Developers do not come with AI skills; they don’t know how to build LLMs. We firmly believe that the GenAI journey of an organization starts from the developer, and not the AI team. With RAGstack, they can start right away, without waiting to acquire new skills. 

Another major concern that organizations must deal with is the AI hallucination. How is DataStax working to ensure fewer hallucinations? 

Hallucination, or in other words the “context” for an LLM, is the tricky bit. Now technology allows you to launch GenAI very quickly, but GenAI is not always accurate. This is why retrieval augmented generation (RAG) is so important for enterprises building GenAI applications. They must be able to augment an LLM with relevant, up-to-date data so it can provide relevant, factual responses.

More data means better context. And this will also bring in the big competitive differentiator into the models and the eventual outcomes for businesses. This further emphasizes the point that many databases in the market that claim to be vector do not have the capacity to perform high volume data. 

Can you share some examples of how Astra DB has helped customers achieve significant business outcomes?

Through Astra, we have started to discover a lot of customers with smaller needs, who can move very quickly through the GenAI journey. When customers begin to deploy impactful GenAI in their business processes, we believe that we stand to gain.  

A great case in point is Skypoint, the Portland-based Gen AI healthcare company. They leverage Astra DB’s vector search to enhance their services, offering users real-time, contextual, and personalized AI agent solutions. 

Another key customer for us is India’s leading edtech player, Physics Wallah. This unicorn’s app has over 15 million downloads as of January 2024. Its personalized AI bot is built on Astra DB and assists students with their academic and support queries in a personalized manner. The database’s stability helped the company handle 50x traffic increase with zero downtime during a live demo with 1 million students.  

A lot of organizations are still in the experimentation stage. Our study shows that only 4% of GenAI projects have moved to production, so we are in the absolute beginning of the wave, a lot of the     projects in PoC will move in     to production this year.      

India’s GenAI market is predicted to reach a massive $17B by 2030 and India is reportedly ahead of the US in AI adoption by businesses. What will be the catalysts for this growth and what are DataStax’s plans to capitalize on this opportunity?

AI will impact the top line and bottom line of every business out there. India has a strong community of developers and GenAI starts with developers. So, India has the skills and the mass to innovate and drive more use cases. They      also have the right mindset – the ecosystem here loves to build and that’s a very positive mindset to have when it comes to AI. Because AI is not something you buy and turn on, it’s something you build. And with the right mindset, India will be faster to innovate. The GenAI wave is poised to trigger a paradigm shift, comparable to the impact of mobile and web technologies. India has the skills, capacity and the mindset to really benefit.  

From a DataStax point of view, India is a big market and we are definitely investing here. We are expanding our team to meet to the growing needs.  

The post India’s developer strength to drive GenAI advantage: DataStax’s Jihad Dannawi appeared first on CIO Dimension.

Salesforce yesterday announced the public beta availability of its much-touted Einstein Copilot, a conversational and generative AI assistant for CRM. Einstein Copilot was first announced as a prototype last year at the company’s Dreamforce Conference in September. 

With the beta release, the AI assistant will be accessible to a larger base of customers. 

So what makes Einstein Copilot standout? The company says, unlike other AI assistants or copilots that lack adequate company data to generate useful responses, Einstein Copilot enables customers to generate responses using their own private and trusted data. This is made possible by combining a conversational UI, a foundational large language model, and trusted company data. 

“AI is the single most important moment in the history of our industry. It will deepen customer relationships, increase productivity and drive higher margins at every company,” said Marc Benioff, Chair & CEO, Salesforce. “Our new Einstein Copilot brings together an amazing intuitive interface for interacting with AI, world-class AI models and above all deep integration of the data and metadata needed to benefit from AI. Einstein Copilot is the only copilot with the ability to truly understand what is going on with your customer relationships.”

Einstein Copilot, said the company, grounds its responses with trusted business data from Data Cloud to provide the necessary context for the highest quality outputs. This allows Einstein Copilot to generate more precise and tailored responses based on trusted company data. Additionally, the Einstein Trust Layer performs functions like masking personally identifiable information (PII), scoring outputs for toxicity, and helping to protect information from unauthorized access and data breaches through zero-data retention from Salesforce’s LLM partners. 

Salesforce also released the findings of a new research by Slack that underscores the role of AI tools in improving productivity. According to the research, 86% of IT executives believe that generative AI will have a major impact on their companies and AI use in the workplace accelerated 24% in the past quarter alone. And, 80% of employees using AI say that it is already improving their productivity. This increased AI adoption indicates that AI is becoming a crucial tool for accomplishing practical business tasks throughout an organization, the company stated. 

The company also highlighted some sample use cases of the AI assistant, including- 

• how salespeople can accelerate deal closures by summarizing records or generating customized communications to provide more personalized client engagement
• how customer service and field service agents can streamline case resolution and boost customer satisfaction by surfacing relevant answers, offers, and data from disparate systems 
• how financial service companies can simplify client onboarding and craft personalized financial plans by automating data collection and analysis, enabling advisors to dedicate more time to client relationships and strategic advice

The post Salesforce says its Einstein Copilot is rooted in trusted data  appeared first on CIO Dimension.

In November 2021, when IBM spun off its infrastructure services to create ‘Kyndryl’, there were quite a few naysayers. Many called it the unshackling of an unwanted part of the business with declining revenues and earnings. Two challenging years down the line, Kyndryl seems to have done a lot of things right. It’s third quarter fiscal 2024 earnings stand as a testimony. 

It beat Wall Street’s expectations, cut its losses by 50% and raised full-year outlook, increasing its stock value by more than 12%. Its 10% revenue decline is attributed to the strategic exit from zero-margin, low-margin revenue streams. At the time of spinoff, about 40% of its contracts were reportedly making little or no profit. 

In Q3 FY24, the company reported double digit growth in its consult revenue, indicating a strong growth in high-margin services. It’s clear that Kyndryl is shedding businesses that added little or no value, and becoming more focused towards areas where it sees higher margins and potential for growth. 

Quite understandably, it was an uphill battle to turn things around, says Karen Forbes, Vice President – Global Go-To-Market and Customer Advocacy at Kyndryl, during her recent visit to Bangalore. 

CIO Dimension caught up with Karen to know more about Kyndryl’s transformation journey. 

Edited excerpts: 

It’s been over two years since the spin off and it’s been a mixed bag, which is not a surprise after a big spin like this. How would you summarize the last two years of Kyndryl’s journey? 

It has been a big journey for us to embed the brand in the market as we stand alongside some of the giants. We had to build a services company that was unshackled by a product company. Two years down the line, we can say with conviction that we are not a legacy spinoff losing money. Our business results clearly project that now. Besides, we are in the new areas of market, we have built new practices and capabilities and are growing those capabilities in our customers. 

Our leadership had two key mandates for us during this massive transition. The first one is to look after our customers, most of whom agreed to stay with us. Secondly, we decided to retain every employee and give them the confidence to come across. Our leadership has done a fantastic job in steering us through that journey.

Two years ago, when the spin was announced, I don’t think many people were placing the bets on us. There was a lot of cynicism out there. But what we achieved in two years is phenomenal and each of us is proud of it. 

Preparing an organization with about 85,000 employees worldwide during a transformation of this scale is not a small feat. What are your learnings? 

The cultural journey has been challenging because we are changing hearts and minds. We were focusing on building confidence among our people. We spent a huge amount of time talking to them about the brand of Kyndryl, the purpose and mission of the brand. 

We created something called the ‘Kyndryl Way’ which talked about the changes we must embrace as we become a services company. For example, most of our teams on the ground never had to work with channel partners or alliances before. We had to teach many new skills to our teams and build trust because this different way of engaging with customers was not usual for us in the ‘old co’. 

The second big vector is our 3A’s strategic objectives that are at the center of our transformation, which is around Alliances, Advanced Delivery and Accounts. We have been making steady progress in all these areas. Our Alliances with tech leaders like AWS, Google and Microsoft have helped us expand our addressable market. Kyndryl Bridge platform has roughly 750 customers performing over 1 billion automations, generating annual savings of $500 million.  

Kyndryl’s hyperscaler partnerships are a significant part of your growth strategy. Tell us a bit more about these alliances. 

We exceeded our alliance revenue targets, with over $300 million generated this year and raised our full fiscal year target to $400 million. We increased our cloud-related capabilities with 38,000 hyperscaler certifications.

Clearly, there is a lot going on in the cloud space. We work with the hyperscalers to help customers understand what their cloud strategy must be VS leading them to a particular hypervisor.  We work together a lot to understand what we can deliver to customers around data management, Gen AI, sustainability, hybrid cloud, FinOps and much more. Some of these areas help us build relationships beyond CIOs to include CFOs, data managers among others.  As our revenue is historically shrinking, it’s important to be in those new places. 

We are also building specific solutions and reference architectures around different industries like financial services and technologies such as genAI and analytics with each of these hyperscalers. So, we are moving the partnership beyond the cloud migration aspect to larger areas. 

How are you modernizing your GTM to align with the new ways of doing things? 

We have been focusing on simplifying how we show up in front of the customer. For example, instead of having multiple specialists as multiple touchpoints, we try to be more cross-practice to solve the customers’ business problem. We start with changing the narrative by focusing on ‘Innovate, modernize and secure’. It’s kind of a flip on from being a product company where you walk in and talk about the products that you like to sell. 

We had an agreement with IBM to use their process and technology at spin. In the last two years, we completely modernized and transformed our front & back offices to “fit-for-purpose” modern IT infrastructure. This helped us consolidate over 80% of the application portfolio we inherited, from over 1,800 business applications to fewer than 360 applications. That made us more efficient and effective in the way we go to market. 

The third important part of the GTM is the skills. We continue to invest heavily on the skills of our people. People are the heart of what we do and they are our most valuable asset as a services company. 

Lastly, would you like to talk about some interesting customer stories from India and the significance of India as a market for Kyndryl? 

India is a particularly exciting market for us. Some of our key customers in India are from the banking and financial sector. A notably big win for us from this sector is Canara Bank which partnered with us to manage and transform their IT operation including the application landscape. We also work a lot of with small finance banks like Suryoday Bank for technology transformation and digital banking. NPCI is another key customer for us, helping them manage their data centers which handle up to 70 billion transactions a month. 

We have some really inspiring stories from our customers like Honda Motorcycle & Scooter India, USDC / Jain University and Dr. Lal Path Labs from India. Most importantly, what we see in India is a market where we can innovate in and scale for the rest of the markets globally.

The post We are not a legacy spinoff losing money: Karen Forbes, Kyndryl appeared first on CIO Dimension.

Every country needs to own the production of their own intelligence, NVIDIA founder and CEO Jensen Huang told attendees Monday at the World Governments Summit in Dubai.

Huang, who spoke as part of a fireside chat with the UAE’s Minister of AI, His Excellency Omar Al Olama, described sovereign AI — which emphasizes a country’s ownership over its data and the intelligence it produces — as an enormous opportunity for the world’s leaders.

“It codifies your culture, your society’s intelligence, your common sense, your history – you own your own data,” Huang told Al Olama during their conversation, a highlight of an event attended by more than 4,000 delegates from 150 countries.

“We completely subscribe to that vision,” Al Olama said. “That’s why the UAE is moving aggressively on creating large language models and mobilizing compute.”

Huang’s appearance in the UAE comes as the Gulf State is moving rapidly to transform itself from an energy powerhouse into a global information technology hub.

Dubai is the latest stop for Huang in a global tour that has included meetings with leaders in Canada, France, India, Japan, Malaysia, Singapore and Vietnam over the past six months.

The Middle East is poised to reap significant benefits from AI, with PwC projecting a $320 billion boost to the region’s economy by 2030.

At Monday’s summit, Huang urged leaders not to be “mystified” by AI. AI’s unprecedented ability to take directions from ordinary humans makes it critical for countries to embrace AI, infusing it with local languages and expertise.

In response to Al Olama’s question about how he might approach AI if he were the leader of a developing nation, Huang emphasized the importance of building infrastructure.

“It’s not that costly, it is also not that hard,” Huang said. “The first thing that I would do, of course, is I would codify the language, the data of your culture into your own large language model.”

And as AI and accelerated computing has developed, NVIDIA GPUs have become a platform for one innovation after another.

“NVIDIA GPU is the only platform that’s available to everybody on any platform,” Huang said. “This ubiquity has not only democratized AI but facilitated a wave of innovation that spans from cloud computing to autonomous systems and beyond.

All of this promises to unleash new kinds of innovations that go beyond what’s traditionally been thought of as information technology.

Huang even countered advice offered by many visionaries over the years who urged young people to study computer science in order to compete in the information age. No longer.

“In fact, it’s almost exactly the opposite,” Huang said. “It is our job to create computing technologies that nobody has to program and that the programming language is human: everybody in the world is now a programmer — that is the miracle.”

In a move that further underscores the regional momentum behind AI, Moro Hub, a subsidiary of Digital DEWA, the digital arm of the Dubai Electricity and Water Authority, focused on providing cloud services, cybersecurity and smart city solutions, announced Monday it has agreed to build a green data center with NVIDIA.

In addition to the fireside chat, the summit featured panels on smart mobility, sustainable development and more, showcasing the latest in AI advancements. Later in the evening, Huang and Al Olama took the stage at the ‘Get Inspired’ ecosystem event, organized by the UAE’s AI Office, featuring 280 attendees including developers, start-ups, and others.

The post Every Country Needs Sovereign AI: NVIDIA CEO appeared first on CIO Dimension.

Hemanta Banerjee has seen it all when it comes to data, analytics and AI. In his own words, “I have been working on analytics even before it became analytics”. And for someone with over two decades of experience and expertise in the field of data, there has never been a better time than now – a time when AI is set to revolutionize pretty much everything. 

Hemanta, who now heads the Public Cloud Data Services at Rackspace Technology, believes that we are at a pivotal point in the evolution of Artificial Intelligence with Generative AI transforming the way we work. “So far, AI was more like a Black box art performed by a set of people. With Generative AI, for the very first time, an average user can understand how to apply AI in the context of the work they are doing. That’s truly revolutionary,” he says. 

Watch the full interview here: 

He believes that the classical co-pilot AI model – wherein AI works alongside the user and assists the user to be more productive – offers immense potential to businesses. And copilots go beyond just automating simple, day-to-day tasks to transform some of the most complex use-cases, he emphasizes. “For example, we deployed a ‘data privacy officer’ for a Rackspace customer. It helped the organization to effectively address all the regulatory and privacy requirements in an unprecedented way,” he explains. 

However, as Generative AI becomes an integral part of the business processes, organization must buckle down and address some of the old and new data challenges effectively. 

“There are a whole new set of challenges around model misuse, AI bias and responsible use of AI,” he says, while elaborating on some of the AI best practices and guidelines including having the right oversight framework. 

The post AI copilots will transform the future of work: Hemanta Banerjee, Rackspace appeared first on CIO Dimension.

Supply chain leaders cite the need to reduce costs, improve the customer experience and expedite delivery times as their top-three challenges, yet fewer than half are leveraging their supply chain data to inform their strategy and 14% aren’t using supply chain data at all to make decisions. That’s according to survey findings released today in a new report, “Data-Driven Decisions: Why Many Supply Chains Are Falling Behind,” by leading supply chain visibility provider FourKites.

FourKites partnered with international research data and analytics group YouGov to poll 500 supply chain leaders across every industry in the US and UK to understand how they’re digitizing their supply chains and mitigating risk. While most respondents (72%) recognize the importance of connecting disparate supply chain data, and nearly half (42%) plan to invest in technology over the next six to 12 months, the majority of supply chain leaders are struggling to extract the information they need to make the data-driven decisions that will help them meet their strategic objectives. 

Specifically, 48% of respondents say their supply chain digitization is sub-par, and 43% say they have no single source of truth for their data.

“Two-thirds of responding companies are using supply chain data only for simple, day-to-day tasks — many have yet to realize the opportunity to look at their supply chain end-to-end to make informed, strategic decisions,” said FourKites Chief Strategy Officer Fabrizio Brasca. “At the root of this issue is the ongoing struggle to fully digitize the supply chain and have a single source of data. For instance, with a real-time view of inventory levels across facilities, supply chain leaders can proactively mitigate the risk of stockouts and prevent orders from being cut, leading to a better end-customer experience.”

Key findings from FourKites’ report include:

• Only 45% of respondents are using supply chain data to make strategic decisions, while 14% say that supply chain data isn’t being used at all to make any type of decision
• 48% rate themselves as “not great” or “struggling” at digitizing their supply chain
• 43% struggle to integrate internal systems and have a single source of truth
• 42% are investing in technology in the next six to 12 months to de-risk their supply chains. Investments are even more pronounced among enterprise companies, where 70% plan to increase tech investment.
• Nearly 52% of companies are diversifying their supplier/provider base in their efforts to de-risk their supply chains

The post Majority of supply chain leaders lack data insights: Research  appeared first on CIO Dimension.

Let’s talk numbers! An astonishing 70% of digital transformation projects reportedly fail – despite it being an all-encompassing focus for enterprises for over a decade. There could be several reasons for a digital transformation initiative to misfire. But, one of the key reasons is a ‘wrong start’, says Mani Mulki, Operating Director for Technology at Kedaara Capital and one of the topmost tech transformation gurus in the industry. 

Mani has led several transformation projects during his illustrious career as a CIO, working for many iconic brands like Tata Capital, ICICI, Godrej and Pidilite. And his biggest takeaway – digital transformation is about putting process before technology. 

In an exclusive interaction with CIO Dimension, Mani Mulki lays out the complexities of digital transformation and explores where organizations go wrong. 

Watch the full video here: 

“Most often, companies fail to understand what it really takes to digitally transform. It’s very easy to get a digital landscape in place. The tough part is doing the ‘transformation’ bit. A lot of organizations that embark on digital transformation merely automate whatever they are doing currently. Very few organizations actually question the status-quo,” he says. 

From his on-the-ground experience spearheading several digital transformation projects, Mani concludes that organizations that successfully transform themselves go back to the basics and challenge the existing processes. 

Mani has successfully amalgamated some of these best-practices to his current role, where he has the larger mandate of guiding a diversified set of Kedaara’s portfolio companies – spanning across healthcare, financial services, pharma, manufacturing and consumer tech. He works with key stakeholders of these portfolio companies in devising robust technology roadmaps that aligns tightly with the desired business outcomes. 

“I am pleased to be able to move into an influencer’s role from an operational role,” he says. 

And that’s perhaps a natural transition that every CIO seeks to make, in order to create a larger impact both within and outside the organization. 

As someone who has closely witnessed the evolution of the CIO role, Mani believes that the CIO role will disappear before long. In its stead, something more powerful will emerge. “IT was a complex function a few years ago and that’s what lent that complex credibility to the CIO or CTO title. With the advent of Cloud and other models, the use cases are more important than the technology itself. The role of a CIO or CTO will hence diffuse into that of a ‘chief transformation officer’ and the potential for that is going to be massive,” he sums up. 

Also watch:

Innovation talk: Muralidharan Ramachandran, Tech Evangelists & CIO of Startek

Shweta Srivastava, Head – IT Applications, JK Cement on the impact of LCNC on app development

Maheswaran S, Country Manager, Varonis, on shifting to data & user-centric security

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