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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Harness, an AI-native software delivery platform company, has appointed Prashant Verma as the Head of R&D, India Center. 

An IIT Delhi alumnus, Prashant comes with over 24 years of engineering leadership and is renowned for building high-performing engineering teams. He has previously contributed to leading enterprise and consumer product organizations such as i2, Informatica, Goldman Sachs, LinkedIn, Flipkart, Zoomcar, and Traveloka.

In his new role, Prashant will focus on establishing Harness India as a global innovation hub, building a world-class R&D center with top-tier talent, and fostering a tech-first culture where engineers drive innovation and take ownership. He will also enhance collaboration with global teams across the US, Europe, and other regions, while positioning Harness India as a thriving workplace and employer of choice.

Jyoti Bansal, Co-founder and CEO, Harness, “We are thrilled to welcome Prashant Verma as Head of R&D, India. His proven expertise in building and scaling high-performing teams aligns perfectly with our vision for Harness India. Under his leadership, the India Center will play a pivotal role in driving impactful innovation and contributing to our global growth strategy.”

Prashant Verma, Head of R&D India, Harness, said “I am honored to join Harness and lead the R&D India Center. Harness India will be a core driver of the company’s global growth and innovation journey. My vision is to build a world-class R&D hub that empowers engineers to solve complex problems, deliver transformative solutions, and contribute to impactful success.”

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Securonix, Inc. announced that Kash Shaikh has been appointed President and Chief Executive Officer and member of its Board of Directors, effective immediately.

Kash succeeds Nayaki Nayyar who has been serving as the CEO of the security vendor since December 2022. Nayaki has decided to step down and focus the next phase of her career on public Board service, the company said in a press release. 

Kash is a seasoned enterprise technology Executive with Fortune 50 experience and has a record of driving profitable growth across Software, SaaS, and AI Infrastructure markets. 

He has over 30 years of experience of fostering business transformation and delivering innovative technology solutions for global enterprise organizations. He most recently served as President and CEO of Virtana, a leading hybrid-cloud infrastructure observability solutions provider.

Prior to Virtana, Shaikh served as Global Vice President and General Manager for Dell’s Enterprise Solutions Unit. Shaikh has also held leadership and executive positions at Ruckus Wireless, Hewlett Packard, Cisco, and Nortel Networks.

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Avient Corporation, the global manufacturer of specialized polymer materials, recently opened its Global Delivery Center in Pune. The state-of-the art facility will act as a key enabler for the manufacturer’s digital transformation initiatives globally.  

With a core focus on key pillars such as ERP systems, IT infrastructure, and Global Business Services, the center aims to significantly strengthen Avient’s global service capabilities and support its innovation roadmap.

The center is also expected to create new job opportunities, specifically in Pune, as it plans to double its headcount in the next few months. 

Vikas Vij, Managing Director of Avient said, “The inauguration of our new Global Delivery Center in Pune marks a significant milestone for Avient in India. This facility will not only enhance our ability to serve our global customers but also create valuable employment opportunities in the region.” 

Dr. Yogesh Dadke, IT Delivery Leader, Avient, will be leading the facility’s operations and its strategic direction. “The establishment of our Global Delivery Center in Pune marks a significant milestone in Avient’s global expansion journey. This initiative not only represents a compelling growth story for our company but also demonstrates our strategic investment in the region’s talent pool. Our Pune center will serve as a hub for innovation, technological advancement, and operational excellence, underscoring our commitment to leveraging local expertise to drive global success,” Dr. Dadke added. 

Avient has been championing sustainability and innovative approaches to products for many decades. It leverages unique technologies to improve the recyclability of products and enable recycled content to be incorporated, thus advancing a more circular economy. Avient’s Dyneema is touted as the world’s strongest fiber and the company has been working with light-weighting solutions that replace heavier traditional materials like metal, glass and wood. 

Technology has been a key priority for Avient in driving many of these breakthroughs and the new global delivery center reinstates the company’s emphasis on technology.

“Our new Global Delivery Center in Pune is a testament to Avient’s commitment to innovation and excellence. This center will be crucial in our ongoing digital transformation efforts, and we are excited to be building our team with talented employees in Pune,” said Zaneta Potoczny, Vice President, Global Business Services, Avient. 

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Tekion, a cloud technology provider for automotive sector founded by Tesla’s former CIO Jay Vijayan, has secured $200 million in growth equity capital from Dragoneer Investment Group. This financing round sets Tekion’s valuation above $4 billion, the company said in a press release. 

Tekion plans to invest the capital to expand its product offering for dealer partners and OEMs, accelerate implementation timelines, and deliver better customer support.

“Tekion has charted an extraordinary growth path, revolutionizing the automotive retail industry through our comprehensive and fully integrated platform,” said Jay Vijayan, Founder and CEO of Tekion. “We are delighted to partner with Dragoneer, whose team has followed our progress closely for multiple years, believes in our long-term vision, and carries an impressive track record investing in best-in-class vertical SaaS businesses.”

The funding announcement follows Tekion’s performance in 2023, with 97% year-over-year annual run rate revenue growth and expanding partnerships in the industry including with over 2,000 automotive retailers, multiple leading-brand OEMs, and over 250 ecosystem technology partners. The company had recently made key appointments to its leadership team, including the CFO, CRO, and CTO. 

“Having watched Tekion’s rapid pace of innovation and market adoption over the past several years, we are proud to partner with and support Jay and his world-class team in the next phase of the company’s growth journey,” said Christian Jensen, Partner at Dragoneer. “

Tekion had raised $250 million funding in October 2021. The Pleasanton-headquartered firm has close to 3000 employees globally and caters to some of the leading automotive dealers across geographies.  

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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.  

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Despite the global economic uncertainties, worldwide IT spending is expected to total $5.06 trillion in 2024, an increase of 8% from 2023, according to the latest forecast by Gartner. This is a slight revisal of the market research agency’s earlier forecast of a 6.8% growth and puts worldwide IT spending on track to surpass $8 trillion well before the end of the decade. 

The latest number reflects a considerable surge in the overall IT spending growth rate, which was a meagre 3.3% in 2023, only a 0.3% increase from 2022. Gartner had highlighted that the slump in spending in the past year was largely due to change fatigue among CIOs, as they hesitate to sign new contracts, commit to long-term initiatives or take on new technology partners.

Talent gap might be fueling demand for IT services 

IT services spending will continue to witness strong growth according to the agency with close to 10% growth. This growth, according to Gartner, is largely driven by the fact that enterprises will rely heavily on IT services firm for their IT talent needs, instead of investing on internal staff – a strong indicator of the persisting talent shortage in the industry.

“Enterprises are quickly falling behind IT service firms in terms of attracting talent with key IT skill sets. This creates a greater need for investment in consulting spend compared to internal staff. We are at an inflection year for this trend, with more money being spent on consulting than internal staff for the first time,” said John-David Lovelock, Distinguished VP Analyst at Gartner. 

GenAI to see more execution in 2025

GenAI is expected to drive substantial data center systems spending in 2024, as more organization move to execution phase in 2025. Gartner predicts that spending on data center systems is expected to see a notable jump in growth from 2023 (4%) to 2024 (10%), in large part due to planning for generative AI (GenAI).

“We are seeing a cycle of story, plan, execution when it comes to GenAI. In 2023, enterprises were telling the story of GenAI and in 2024 we are seeing most of them planning for eventual execution in 2025,” said Lovelock. “Technology providers are required to be a step ahead of this cycle and are already in the execution phase. They are bringing GenAI capabilities to existing products and services, as well as to use cases being identified by their enterprise clients. 

“There is also gold rush level spending by service providers in markets supporting large scale GenAI projects, such as servers and semiconductors,” said Lovelock. “In 2024, AI servers will account for close to 60% of hyperscalers total server spending.”

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AI and data consulting firm 66degrees has appointed Pankaj Chugh as President and Chief Operating Officer. In this role, Chugh will oversee the company’s sales, marketing, alliances, and solutions. 

Chugh most recently served as an Executive Vice President at LTIMindtree where he ran the Retail & Consumer business, scaled some of the largest clients in Tech industry, set up delivery centers in India and US, ran the global Digital Product & Platform Engineering business, and incubated and rapidly expanded the Google Cloud business. Prior to this, at Infosys he ran and grew different industry portfolios across Europe and Americas, and led growth strategy and M&A for Infosys’ Europe and global Hi-Tech and Manufacturing verticals. 

“Pankaj’s expertise in building technology services businesses, scaling global teams, and serving as a key client advisor will serve as a great complement to our talented team, allowing us to continue to scale and better serve our existing accounts,” said Ben Kessler, CEO.” With his arrival at 66degrees, Pankaj immediately adds to three of our 2024 strategic pillars: setting client success as our north star, bringing solutions and accelerators to market, and building our global delivery team.”

Pankaj Chugh said, “Every industry is rapidly becoming a digital-first industry. Cloud, AI and data have created renewed urgency to embrace the disruption. Clients have already been asking 66degrees to help navigate the change, and this is a great opportunity to support them leapfrog. I am very excited to be a part of one of the brightest technology and business minds in the services industry.”

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Technology solution provider CEI has acquired Chennai-based Fourth Dimension Technologies, an IT Solutions and Managed service provider (MSP) with a global footprint and 35 years of experience. The acquisition is expected to significantly expand CEI’s service portfolio and its ability to deliver technology solutions to clients across the globe.

Founded in 1992, CEI has expertise in Technology Strategy & Advisory Services, Custom Technology Solutions, Cloud Solutions, Artificial Intelligence, Managed IT Services, and Staffing. 

Fourth Dimension, founded by Jagannathan Narayan, has been providing IT solutions and managed services for over three decades for clients spanning diverse industries such as banking and financial services, manufacturing, and healthcare. This includes 24×7 coverage, Cloud Management (CSP), Managed Security Services (MSSP), Managed IT and Service Desk (Hybrid as well as Cloud), Mobile Device Management, and IT Infrastructure observability and support. Additionally, Fourth Dimension strengthens CEI’s existing partnerships with Microsoft and expands its reach to include industry leaders like Cisco, VMWare, Fortinet, and RSA.

“We are thrilled to welcome the Fourth Dimension team to the CEI family,” said D. Raja, CEO of CEI. “This acquisition is a significant step forward in our mission to provide our clients with the most comprehensive and innovative technology solutions available. Fourth Dimension’s expertise in managed services and their global reach perfectly complement our existing offerings, allowing us to deliver an exceptional level of service and support to a wider audience, while also expanding our offerings to our current clients.”

“Joining forces with CEI represents an exciting opportunity for both of our organizations,” said Jagannathan Narayanan, CEO of Fourth Dimension. “By combining our resources and expertise, we can create a truly unique offering that will empower our clients to thrive in today’s dynamic technological environment.”

The financial terms of the deal were not disclosed.

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Generative AI or GenAI has perhaps had the fastest uptake in the history of enterprise with vast majority of the organizations trying to leverage the technology for various use cases. According to Gartner, more than 80% of enterprises will have used GenAI APIs or models, or deployed GenAI-enabled applications in production environments by 2026, up from less than 5% in 2023. But to get there, enterprises have an uphill task ahead of them. 

In an email interview with CIO Dimension, Lakshminarasimman Raghavan, Group VP Technology, Publicis Sapient, shares what it takes to advance GenAI projects from PoC to production. 

Edited excerpts: 

Majority of the GenAI projects in 2023 focused experimentation and proof of concepts. Do you see GenAI use cases moving from initial pilots and PoC to production this year?

As GenAI emerges as a fundamental capability for IT organisations, there is a pressing need for a transformative shift in capability development. Building Gen AI capabilities in-house is imperative to keep pace with the rapid evolution and widespread adoption of this technology across various industries. While trying out new technologies through PoCs shows what’s possible, true insights emerge when solutions are deployed into real-world scenarios. To effectively test and refine solutions, the time spent on PoCs must be streamlined. 

IT leaders can direct their focus on specific challenges, working to transform them into tangible realities. This approach facilitates the rigorous testing and refinement solutions within live environments.  Many of the organizations that we work with are in the PoC stages with some of them starting to rollout their solutions to production this year.

What are some of the key GenAI trends that will emerge and dominate through the year? 

GenAI is expected to unlock new opportunities for automation, efficiency, and innovation across enterprise operations, leading to significant advancements in various domains. Here are some key trends:

• Automated code generation: GenAI will revolutionize software development by accelerating code creation processes. This will lead to more cost-effective and rapid development cycles, enabling IT organizations to deliver solutions more efficiently.
• In-house GenAI capabilities: Organizations will increasingly invest in developing in-house GenAI capabilities to address specific challenges and enhance operational efficiency. This shift will reduce reliance on external partners and vendors, allowing companies to have more control over costs and resources. These capabilities not only include just technical aspects but also aspects such as knowledge in biases, ethical aspects and operational aspects such as testing, optimizing and tuning GenAI solutions etc.
• Transformation of talent landscape: The emergence of GenAI AI as a core capability will transform the engineering and IT talent landscape. Skilled professionals with expertise in GenAI will become indispensable, driving the adoption and implementation of this technology across various industries.
• Focus on scalable solutions: IT leaders will prioritize scalable solutions over PoCs to harness the full potential of GenAI. By implementing and refining solutions in live environments, organizations can derive real insights and drive tangible outcomes, rather than investing time in experimental trials.
• Streamlining of data: The fundamental building block of any GenAI based solution is the underlying data ranging from code all the way to behavioral and transactional data. Organizations will need to invest in streamlining their data to enable effective GenAI implementations. 

A transformation in the engineering and IT talent landscape is anticipated, with Gen AI skills emerging as essential. A new wave of engineers will rise, armed with the expertise necessary to unleash the potential of this technology. Organizations are expected to internalize GenAI endeavours, driven by the imperative to manage costs and bolster operational efficiency, diverging from sole reliance on public cloud infrastructure.

What could potentially be the top challenges for CIOs as they try to deploy GenAI at scale within the enterprises?

A few aspects that CIOs must consider while deploying GenAI at scale include:

• Aligning with business goals: CIOs must seamlessly integrate transformative technologies, like GenAI, with business objectives. This requires a proactive approach to understand GenAI’s influence and equip teams with the necessary tools to outline viable business cases. The challenge lies in decoding GenAI’s impact and aligning technological advancements with overarching business imperatives.
• Prioritizing data engineering: Establishing a robust Core Data Architecture is crucial for adapting to the evolving tech landscape. CIOs are prioritizing efforts in data engineering, focusing on tasks such as formatting data appropriately, optimizing data locations for model training, and assessing existing data platforms. This concentrated attention and investment in data engineering are essential for leveraging GenAI effectively.
• Building in-house capabilities: Developing in-house GenAI capabilities requires significant investment in talent, resources, and infrastructure. CIOs need to evaluate the extent to which they rely on external partners and vendors versus building these capabilities internally. Securing the necessary talent and allocating resources to sustain and evolve these capabilities over time presents a considerable challenge as well as opportunity. This involves evaluating the costs, talent acquisition, and resource allocation needed for either approach.

These factors underscore the need for CIOs to strategically align with business teams, emphasize collaboration, and proactively decode GenAI to ensure successful deployment at scale while navigating the complexities of organizational transformation and technological advancement.

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