JAGGAER Names Gopinath Polavarapu as AI Officer
JAGGAER, a global leader in source-to-pay and supplier collaboration, has announced the appointment of Gopinath 'GP' Polavarapu as its new Chief Digital and AI Officer (CDAO).
The company revealed that GP will lead the acceleration of its AI strategy. He will focus on embedding intelligence into the JAGGAER One platform to create a hyper-automated, conversational, and collaborative solution.
This move supports JAGGAER's mission to enhance human decision-making and drive faster business outcomes.
In his new role, GP will guide the company's end-to-end AI roadmap. This includes Agentic AI for the JAGGAER One platform, internal productivity tools, and talent acquisition. He will also lead the formation of an AI Center of Excellence and a hub for AI thought leadership.
JAGGAER reported that GP brings deep experience in enterprise AI. Most recently, he served as Chief Solutions Officer at Kore.ai. In his current role, he will work closely with Chief Product Officer Jon Lawrence and Chief Strategic Officer Pascal d'Arc to lead the AI transformation.
The company emphasized that GP will: Automate key functions like spend analysis, contract intelligence, and invoice processing
Transform data into real-time autonomous actions and intelligent process automation
'I'm honored to join JAGGAER's exceptional team,' said Gopinath Polavarapu. 'By embedding fine-tuned LLMs, AI Agents, and Agentic workflows into the platform, we will deliver unmatched efficiency, compliance, and cost savings.'
JAGGAER also highlighted GP's passion for mentoring AI talent and driving digital innovation.
Yancey Spruill, Interim CEO of JAGGAER and Operating Managing Director at Vista Equity Partners, stated, 'The addition of GP will foster faster innovation through generational technology. Each leader at JAGGAER brings a unique perspective, and GP is no exception.'
He added that the next five years will bring significant transformation, and GP's vision will help guide JAGGAER's next phase of growth with Agentic AI.
Hashtags
Try Our AI Features
Explore what Daily8 AI can do for you:
Comments
No comments yet...
Related Articles
Tahawul Tech
a day ago
- Tahawul Tech
'Agentic AI is on every organisation's roadmap, but most companies are stuck in prototype purgatory.' – Shaun Clowes, Confluent
Confluent, Inc. has announced Streaming Agents, a new capability in Confluent Cloud for Apache Flink® that makes it easy to build and scale AI agents that monitor, reason, and act on real-time data. Streaming Agents removes barriers to enterprise-grade agentic artificial intelligence (AI) by unifying data processing and AI workflows and providing easy, secure connections to every part of a business, including large language models (LLMs) and embedding models, tools, and other systems. It accelerates the adoption of agentic AI, enabling more efficient workflows, faster time to value, and the creation of entirely new business models and opportunities. 'Agentic AI is on every organization's roadmap. But most companies are stuck in prototype purgatory, falling behind as others race toward measurable outcomes,' said Shaun Clowes, Chief Product Officer at Confluent. 'Even your smartest AI agents are flying blind if they don't have fresh business context. Streaming Agents simplifies the messy work of integrating the tools and data that create real intelligence, giving organizations a solid foundation to deploy AI agents that drive meaningful change across the business.' IDC research shows that while organizations ran an average of 23 generative AI proofs of concept between 2023 and 2024, only three reached production. Of those, just 62% met expectations. 'While most enterprises are investing in agentic AI, their data architectures can't support the autonomous decision-making capabilities these systems require,' said Stewart Bond, Vice President of Data Intelligence and Integration Software at IDC. 'Organizations should prioritize agentic AI solutions that offer easy, secure integration and leverage real-time data for the essential context needed for intelligent action.' Build and Scale Real Time AI Agents With Streaming Agents Streaming Agents brings agentic AI directly into stream processing pipelines to help teams build, deploy, and orchestrate event-driven agents with Apache Kafka® and Apache Flink®. By unifying data processing and AI reasoning, agents gain access to fresh contextual data from real-time sources to quickly adapt and communicate with other agents and systems as conditions change. Streaming Agents are always on and works on a business's behalf, operating dynamically, processing high-volume data streams, and instantly responding to real-time signals with context-aware reasoning like human operators would. For example, Streaming Agents can do competitive pricing by continuously monitoring prices across ecommerce sites and automatically updating product prices on a retailer's site to reflect the most competitive offer for customers. Key features of Streaming Agents include: Tool calling for context-aware automation: Tool invocation via Model Context Protocol (MCP) enables agents to select the right external tool, such as a database, software-as-a-service (SaaS), or API, to take meaningful action. Tool calling accounts for what's happening in the business and what other systems and agents are doing. Tool invocation via Model Context Protocol (MCP) enables agents to select the right external tool, such as a database, software-as-a-service (SaaS), or API, to take meaningful action. Tool calling accounts for what's happening in the business and what other systems and agents are doing. Connections for secure integrations: Securely connect to models, vector databases, and MCP directly using Flink. Connections also protect sensitive credentials, encourage more reusability by sharing connections across multiple tables, models, and functions, and centralize management for large-scale deployments. Securely connect to models, vector databases, and MCP directly using Flink. Connections also protect sensitive credentials, encourage more reusability by sharing connections across multiple tables, models, and functions, and centralize management for large-scale deployments. External Tables and Search to boost AI accuracy: Ensure that streaming data is enriched with non-Kafka data sources, such as relational databases and REST APIs, to provide the most current and complete view of data. This improves the accuracy of AI decision-making, vector search, and retrieval-augmented generation (RAG) applications, reduces cost and complexity by using Flink SQL, and leverages the security and networking capabilities of Confluent Cloud. Ensure that streaming data is enriched with non-Kafka data sources, such as relational databases and REST APIs, to provide the most current and complete view of data. This improves the accuracy of AI decision-making, vector search, and retrieval-augmented generation (RAG) applications, reduces cost and complexity by using Flink SQL, and leverages the security and networking capabilities of Confluent Cloud. Replayability for iteration and safety: Agents can be developed and evaluated using real data without live side effects, enabling dark launches, A/B testing, and faster iteration. Streaming Agents are available today in open preview.
Arabian Post
12-08-2025
- Arabian Post
Jumbo Teams Up with Jio Haptik to Deliver AI Solutions
Jumbo Enterprise has entered into a strategic partnership with Jio Haptik to offer next-generation Agentic AI solutions to enterprises across the GCC. This collaboration brings together the regional reach and IT expertise of Jumbo with Jio Haptik's advanced AI-driven customer experience technology. The agreement positions both companies to transform enterprise operations in the Gulf region. By integrating Agentic AI, businesses will benefit from more efficient customer engagement, leveraging AI to drive personalized experiences and enhance operational efficiencies. With Jumbo Enterprise's established foothold in the region and Jio Haptik's global reputation in AI-powered solutions, the alliance is poised to offer cutting-edge innovations to various sectors, including retail, banking, telecom, and hospitality. Jumbo's deep understanding of the GCC market combined with Jio Haptik's leading AI technologies ensures that the partnership will provide tailored solutions to meet the specific needs of businesses in the region. This collaboration is timely, as companies are increasingly seeking to modernise their customer service operations and embrace automation to stay competitive. ADVERTISEMENT The partnership aims to leverage conversational AI, chatbots, and virtual assistants to streamline customer interactions, while also improving efficiency across multiple communication channels. By automating tasks such as customer support, product recommendations, and query resolutions, the deployment of Agentic AI could substantially cut down operational costs for enterprises, whilst also enhancing the customer experience with faster, more accurate responses. Jio Haptik's platform is already known for delivering scalable solutions to some of the world's largest enterprises. As part of this new venture, the company will bring its powerful AI capabilities to the GCC region, providing local enterprises with the tools to boost their engagement strategies. With an increasing number of GCC companies embracing AI, Jio Haptik's partnership with Jumbo is a timely move to tap into a growing market demand for smart, data-driven solutions. For Jumbo, this collaboration marks a significant step in strengthening its position as a leader in the IT solutions space in the UAE and broader Gulf region. The company's extensive history in offering diverse technological solutions will complement Jio Haptik's cutting-edge AI technology, helping to further digitalise the region's enterprise sector. The rise of artificial intelligence in business operations is becoming a defining trend in the GCC, where companies are investing heavily in digital transformation strategies. Both Jumbo and Jio Haptik are set to capitalise on this momentum, providing region-specific AI solutions designed to support enterprises in their journey towards greater automation and enhanced customer satisfaction. The new partnership also highlights a growing trend of cross-border collaborations between companies from different parts of the world. As companies in the GCC look to expand their digital capabilities, partnerships like this are expected to drive greater adoption of AI across multiple industries.
Tahawul Tech
07-08-2025
- Tahawul Tech
Do you want to get the most out of Agentic AI? Dataiku GM lists the three boxes you need to tick for success
Sid Bhatia, Area VP & General Manager – Middle East, Turkey & Africa, Dataiku, has written an op-ed that lists the THREE architectural boxes every organisation should tick in order to get the most out of Agentic AI. In the United Arab Emirates (UAE), AI is now an everyday tool. We use it as individuals, and we use it as professionals. Among the businesses that use it, the more successful implementers follow a Universal AI adoption path that changes the corporate culture from within and infuses the workforce with AI literacy. Stemming from this enthusiasm, analysts foresee AI in the UAE as a multibillion-dollar segment, with generative AI (GenAI) alone taking about US$383 million in 2025 and more than US$2.5 billion in 2031 — a CAGR of nearly 37%. But AI itself has changed. As businesses have come to understand the limitations of GenAI and the importance of taking an operationally centric approach to tool procurement, decision makers have begun to explore the idea of having AI agents with modular autonomy take over from other forms of AI. The UAE's agentic AI market garnered revenues of around US$34 million in 2024. By 2030 it is expected to be worth more than 10 times this figure — some US$352 million. At a CAGR of almost 48%, agentic AI, in the UAE at least, will be adopted at a faster rate than GenAI. As with GenAI, or any AI, or indeed any technology, procurement of agentic AI is no guarantee of success. We must be diligent about how we build our architecture, the ideal example of which, I believe, has three basic characteristics. Flexible AI waits for nobody. At its current speed of evolution, modern business IT environments find it difficult to keep pace. To stand a chance, CIOs must look at how easy or difficult it is to maintain their tech stacks. If a new version of the GPT core model arrives on the market, will it be easy to adopt, or will it require weeks of overtime work from the DevOps team and others? To streamline adoption, enterprises should ensure that the underlying framework, in which AI agents will operate, is flexible enough to support plug-and-play models. Modular architecture is crucial to the success of almost any modern technology; but if the regularity of recent versions of GPT is anything to go by, then the journey organisations will take with agentic AI is likely to be marked by particularly frequent upgrades. Architectures should be crafted around four layers: the generative model layer, the feedback layer (which implements learning loops across multiple models), the deployment layer, and the monitoring layer. Matches models to jobs To apply the FOMO principle to AI procurement is to invite disaster. The individual or team that oversees the organization's Universal AI journey should be laser-focused on business issues first and AI only as the means to overcome challenges. Organisations should be fully cognizant of what issues are being addressed by AI. Is it an exercise in optimisation? Is it the addition of a completely new business capability or a new product or a new service? Whatever is being added, it should come with a net-positive value. The AI procurement team should work with targeted beneficiaries to ensure everyone knows how to measure success and what constitutes a risk. For example, giving GenAI-powered virtual assistants to sales or customer-service employees may lift their productivity, conversion rates, and even profitability ratings. But these benefits may be neutralised if employees share sensitive data with a cloud-native model. Thankfully, formal metrics like answer correctness and B-score allow analysis of models for their suitability in a use case. 'LLM as judge' — where AI models are used to monitor the effectiveness of other AI models — is also viable. Backed by strong governance The AI journey is fraught with risk. Today, we see many organisations prioritizing speed over security, and we see AI budget growth outpacing that of IT budgets. The introduction of AI must align with the compliance obligations and financial limitations of the enterprise. The only way to achieve this is through appropriate governance. Governance has a broad remit. On the security side, it can mandate content-filtering to ensure customers are never exposed to output that would be damaging to the organisation's brand. On the financial side, it can prescribe dashboards that monitor costs and categorise them by project and user. So critical is governance to AI success that some modern AI platforms include it as part of the suite — signaling that solutions vendors now consider it as important as the building of ML models. Even when AI was in its infancy, some industry leaders were calling for 'responsible AI' that cracked open the black box and presented models' innermost workings for scrutiny. Guardrails and trust go hand in hand. Security builds trust with customers. Cost-effectiveness builds trust with the C-suite. The path to Universal AI We can have the AI future we want, but only if we apply due diligence. By ensuring we take the right steps towards security and cost-effectiveness we can introduce agentic AI in ways that produce the right results. It may be the talk of the town right now, but we must adopt agentic AI strategically if we are to prosper from its merits.
