75% Indian Enterprises Face Losses Due to SaaS Implementation Delays: IDC-Zoho Study - Related to data, millions, study, gartner, implementation

75% Indian Enterprises Face Losses Due to SaaS Implementation Delays: IDC-Zoho Study

Indian enterprises adopting software-as-a-service (SaaS) solutions are encountering significant implementation delays, resulting in rising costs and missed business opportunities, .

The analysis, titled ‘IDC State of SaaS Adoption in India Survey 2024’, revealed that 75% of enterprises implementing SaaS since 2020 have faced timeline overruns averaging 57%, with cost escalations reaching 43%.

These setbacks have led to an estimated average loss of ₹[website] crore per business due to missed opportunities, while also impacting productivity, customer satisfaction, and competitive positioning.

Highlighting the critical need for streamlined SaaS implementation, Sharath Srinivasamurthy, associate vice president of IDC India, stated, “The ability to deploy SaaS solutions efficiently is no longer just an IT priority, it is a business necessity. Long deployment cycles escalate costs, slow down innovation, and reduce market responsiveness. Enterprises need a strategic approach, one that integrates automation, contextual intelligence, and development tools, to accelerate implementation and unlock SaaS value faster.”.

Zoho CEO Mani Vembu echoed this sentiment, emphasising the importance of reducing deployment timelines.

“Our platform-first approach eliminates common implementation bottlenecks by offering deeply integrated applications, low-code extensibility, and AI-powered automation. This enables businesses to deploy solutions and go live faster, reduce implementation risks, and accelerate their digital transformation efforts,” he revealed.

Financial Impact and Business Performance.

The study highlighted the far-reaching consequences of SaaS implementation delays. While [website] of enterprises acknowledge the importance of timely deployment, 67% reported increased costs due to extended timelines.

Moreover, 53% saw digital transformation efforts hindered, 48% noted customer dissatisfaction, and 46% suffered missed revenue opportunities, directly affecting overall business performance.

The financial burden was particularly pronounced in financial and accounting (F&A) solutions, which recorded the highest cost overruns at 60%. With 66% of enterprises deploying these solutions, delays disrupted crucial processes such as invoicing and payment processing, leading to penalties and compliance risks.

Customer experience solutions were the most widely adopted post-pandemic (87%), experiencing an average timeline overrun of 51%. However, email and collaboration solutions recorded the highest delays at 68%, followed by legal solutions at 61%.

Key reasons for implementation delays included project management inefficiencies (47%), integration and security challenges (38%), talent shortages (38%), and technical complexities (38%).

The study mentioned how different industries have been affected by SaaS implementation delays. In the healthcare sector, 63% of enterprises reported cost increases, while 58% faced reduced productivity and customer dissatisfaction. In financial services, 75% of enterprises encountered rising costs, with 60% reporting disruptions in digital transformation and 55% experiencing customer dissatisfaction.

In the manufacturing sector, 72% of enterprises faced cost overruns, while 41% reported revenue losses. Retail businesses also struggled, with 61% reporting revenue losses, 58% facing higher costs, and 51% seeing declines in productivity.

Transitioning to a Platform-First Approach.

To overcome these challenges, enterprises are increasingly adopting a platform-driven approach to SaaS implementation. This strategy adopts automation, reduces customisation efforts, and ensures seamless integration.

The study found that 59% of enterprises consider automation and DevOps essential for accelerating deployments. Automated workflows help minimise manual dependencies, decrease errors, and improve implementation speed.

Furthermore, 53% of enterprises highlighted the role of integrated developer platforms with low-code, no-code, and pro-code capabilities in overcoming customisation bottlenecks. These platforms allow business consumers and IT teams to build and modify applications quickly, reducing deployment delays. Another 48% stressed the importance of plug-and-play solutions that simplify integrations and system connectivity.

“Businesses cannot afford to be slowed down by complex and fragmented implementations. Zoho’s platform architecture ensures enterprises have the flexibility to implement solutions at scale with speed and minimal disruption,” Vembu added.

He noted that by offering pre-configured industry workflows, AI-driven analytics, and seamless interoperability, organisations can accelerate their SaaS deployments while minimising costs and maximising business impact.

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How Confluent is Helping Swiggy Deliver Millions of Orders Daily

Food and grocery delivery platform Swiggy handles billions of orders annually across approximately 700 cities. Managing such a scale requires real-time coordination between restaurants, delivery partners, and end-clients. This is where data streaming platform Confluent steps in, supporting Swiggy’s massive data flow for seamless order processing.

As the , Confluent has been supporting Swiggy for over three years now.

At its core, Confluent’s platform is built on Apache Kafka, facilitating real-time data streaming. Notably, Confluent was founded in 2014 by Jay Kreps, Neha Narkhede, and Jun Rao, the original creators of Apache Kafka.

“If Confluent is down, Swiggy is down,” Rohit Vyas, country presales leader of India at Confluent, told AIM in an . He emphasised the platform’s role in Swiggy’s infrastructure.

Data streaming platform Confluent has been in the space for over a decade, catering to more than 5,000 enterprises.

“We are the second fastest software firm to reach a billion dollars in revenue,” noted Rubal Sahni, area vice president and country manager at Confluent, to AIM.

Catering to different domains, Confluent in the recent past partnered with Jio Platforms to integrate its data streaming platform with JioCloud Services. Another major customer is Swiggy, where real-time data processing is mission-critical.

Swiggy has processed over 3 billion orders, requiring a system that can scale dynamically. Initially, Swiggy relied on open-source Apache Kafka for its data streaming needs. However, as order volumes surged, maintaining an in-house Kafka setup became resource-intensive for Swiggy’s engineering teams.

This led to the transition to Confluent’s fully managed service, which provides real-time data streaming while reducing operational overhead. This move not only enabled Swiggy to streamline operations, and handle order surges efficiently, but also optimise resource allocation during high-demand periods such as festivals.

“So when they scaled up in a hyper scaling manner and their customer base began to bulge on, they realised that babysitting open source Kafka was draining a lot of their engineering talent. Then they approached Confluent,” Vyas explained. “What we enable for them is a multitude and a multi-dimensional aspect.”.

Confluent plays a critical role in enabling Swiggy to track order progress in real time, ensuring transparency at every stage. By ingesting vast amounts of data and processing it within milliseconds, the platform updates order statuses, estimated delivery times, and driver locations without delays. “If Confluent is down, apparently, Swiggy is down,” Vyas expressed.

From the moment a customer places an order, Confluent’s data streaming platform ensures that every step, from restaurant confirmation to assigning a delivery partner, happens seamlessly.

This entire complex system of fulfillment is happening over Confluent, making real-time updates possible, Vyas explained. The platform dynamically maps delivery partners, estimates preparation and packing times, and continuously syncs this information with Swiggy’s app.

Beyond scaling, ensuring security and governance is another challenge. Swiggy processes massive volumes of customer data, making compliance and security a top priority. Confluent provides built-in governance and monitoring tools, helping Swiggy maintain regulatory compliance while securing sensitive information.

Beyond logistics, Swiggy utilises AI-driven analytics powered by real-time data streaming. Predictive algorithms help optimise delivery routes and inventory management. “Swiggy Instamart tracks high-demand items in specific locations, ensuring superior stock availability in dark stores,” Vyas further stated.

As Confluent continues to expand its presence in India, the enterprise is focusing on scaling its industry reach and strengthening partnerships. “We have scaled our teams in India. We have opened newer industry verticals. We have invested in the public sector,” Sahni presented. The recent partnership with Jio Platforms aims to support public sector organisations with infrastructure for GenAI applications.

Beyond public sector expansion, Confluent is also increasing its footprint across industries such as healthcare, manufacturing, and tier-two markets. “We are adding more resources to cater to newer industry verticals, not just metro cities but also down south and up north,” Sahni added. To further strengthen its presence, Confluent is working closely with hyperscalers like AWS, Azure, and Google Cloud while expanding partnerships with enterprises looking for scalable data solutions.

The organization will continue to cater to markets of different sizes, from startups to large-scale enterprises. “We have various offerings depending on the size of the customer and nature of use cases,” Sahni mentioned.

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Gartner identifies top trends in data and analytics for 2025 - and AI takes the lead

Data is at the heart of most organizations, fueling everyday business functions. To help digital leaders better prepare their data and analytics (D&A) strategies, Gartner has identified the top D&A trends for 2025.

"D&A is going from the domain of the few, to ubiquity. At the same time D&A leaders are under pressure not to do more with less, but to do a lot more with a lot more, and that can be even more challenging because the stakes are being raised," said Gareth Herschel, VP analyst at Gartner. "There are certain trends that will help D&A leaders meet the pressures, expectations and demands they are facing."

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Out of the nine trends Gartner identified, unsurprisingly, AI-related technologies made up more than half of the list, including the biggest AI trend of the time -- agents. Agentic AI has begun permeating every business sector, with organizations finding ways to implement the autonomous assistance that AI agents offer.

Gartner advised D&A leaders to use agents to access and share their organization's data across applications. The analyst also recommended that D&A leaders use AI agents to automate closed-loop business outcomes, where data-driven insights continuously inform and optimize decisions.

Also: 4 ways to get your business ready for the agentic AI revolution.

When generative AI first became mainstream, the focus for many D&A leaders and their organizations was developing and implementing large language models (LLMs).

However, greater emphasis has since been placed on the value of small language models (SLMs). These small models are lightweight, tailored, cheaper, and faster to train, which is better for specific use cases. As a result, Gartner advised D&A leaders to consider SLMs for more accurate and contextually appropriate AI outputs.

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As there are so many different tools that D&A leaders can use, Gartner also recommended composite AI, which is the process of leveraging multiple AI techniques to increase technological effectiveness. This approach means exploring technology beyond generative AI and LLMs to take a deeper look at related disciplines, such as machine learning and data science.

Some of the trends that Gartner identified are indirectly related to AI. For example, the analyst encouraged using synthetic data to supplement areas where insight is missing or incomplete. This approach is especially valuable when using data for AI initiatives, as these projects require organized, complete data foundations for training and deployment. Another advantage of synthetic data is that it can replace sensitive data, prioritizing privacy, which is especially essential for AI.

Building on this conception, Gartner identified metadata management solutions as an imperative trend, advising organizations to implement tools that automate finding and analyzing metadata. The analyst expressed various metadata types, including technical and business metadata, can then be used for data catalogs, data lineage, and AI-driven use cases. In its multimodal data fabric trend, Gartner advised collecting and analyzing information at the metadata stage of the data pipeline.

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Other key trends highlighted by Gartner include decision intelligence platforms, which help organizations shift from simply using data to making smarter, decision-focused strategies. The analyst expressed this shift is critical to success.

Gartner also pointed to highly consumable data products as a trend, emphasizing the need for organizations to create useful, reusable data products that different teams can access to optimize and improve business-critical use cases over time.

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