About Us
From Idea To Production
Generative AI solutions are reshaping how we work, and AI Agents are the future. Data-Hat AI assists Enterprises in navigating the AI landscape and building profitable and scalable Enterprise AI solutions. As transformation leaders implore the AI landscape, they seek experts to assist in developing solutions and building strategies. And that’s where Data-Hat AI comes in! Guided by an industry veteran, Kshitij Kumar (KK), who has over 2 decades of experience in introducing and implementing Data and AI solutions in Large Enterprises in the US, UK and Europe; a global team of AI and ML experts design Enterprise level AI, GenAI and AI Agent solutions. We go beyond product development, we collaborate with stakeholders and technology leaders to build a Data and AI strategy, develop a Minimal Viable Product (MVP) and establish ROI. We help Enterprises with impactful AI solutions development.
Why Choose us?
Improve Operational Efficiency and Reduce Cost. Faster
Automation for repetitive tasks
Leverage Your Enterprise Data Assets Better
Generate AI Impact Faster
AI-Driven Process Automation
Our Vision
Empower Enterprise build impactful AI solutions and tools. Create sophisticated AI, Generative AI and Agentic AI systems with Responsible and Explainable AI framework.
Our Mission
Effective utilization of Data through AI for improved business performance.
Develop Autonomous Agents
Automate Enterprise Workflows
Predict Data Insights
Deliver Strategic Impact
Our core values
A Data, Generative AI, and AI Agents visionary.
About Kshitij Kumar
A Top 100 CDO
Kshitij Kumar (KK) has served as Chief Data Officer and Enterprise Board member across prominent organizations including Haleon, Farfetch and OneFootball. Throughout his career, he has consistently demonstrated exceptional expertise and innovation in leveraging Data, AI/ Generative AI, Machine Learning technologies, Responsible AI, and AI Governance to drive transformative business outcomes.
Idea Behind Data Hat AI
Vision
As KK journeyed through different industries (from pharma to sports and from manufacturing to retail), he realized that the core problem that Enterprises faced was Big Data. Before an Enterprise could begin with any AI solution they needed to clean, sort and connect data. The GenAI and Agentic AI solutions available often solve part of the Enterprise problem. So, he gathered the World’s best AI and ML engineers to form Data-Hat AI, an Enterprise-level solution provider, developing GenAI and AI Agent tools that monetize Data and automate end-to-end business process.
Achievements
Achieved
KK is recognized among the World’s Top 100 Chief Data Officers (2023, 2024), Top 100 Data Influencers (2024), and Top 40 Chief AI/Analytics Officers. KK is also a member of the Gartner Data and AI Board. His expertise lies in building advanced data ecosystems that drive organizational transformation and competitive advantage.
Help Center
Got a question? Get your answers
If you are stuck building Data and AI strategy, or require a partner to provide AI, Generative AI, or AI Agent solutions, then reach out to us. We can help Fast-track our AI Journey.
01. What are the most impactful use cases of generative AI in large enterprises?
Generative AI can transform enterprise operations by enabling intelligent content creation, knowledge discovery, personalized customer interactions, and decision support across functions. High-impact use cases include:
– Automating internal documentation and reporting
– Generating marketing content and customer-facing communication
– Creating product descriptions and personalization in e-commerce
– Enhancing supply chain visibility with predictive simulations
– Enabling AI-powered chat-bots and virtual assistants
Data Hat AI specializes in converting enterprise data into generative AI solutions like domain-specific knowledge bases, and department-specific AI agents. Their approach ensures enterprises derive business-ready outcomes, not just prototypes.
02. How do AI agents differ from traditional automation tools, and where can they be applied in my organization?
Unlike traditional automation tools that follow fixed rules, AI agents are autonomous, adaptive, and goal-oriented. They can make decisions based on context, learn from data, and interact across systems and departments. Data Hat AI offers a suite of enterprise-ready AI agents including:
– Sales Operation Effectiveness Agent
– Customer Retention Agent
– Supply Chain Agent
– Product Price Optimization Agent
Each agent is tailored to solve domain-specific challenges and operates autonomously to deliver continuous business value.
03. What are the risks and compliance concerns when deploying AI agents across business units?
Risks include data privacy breaches, model bias, poor explainability, and misalignment with regulatory frameworks. Data Hat AI embeds Responsible AI and Explainable AI (XAI) principles into every solution. They offer:
– Human-in-the-loop frameworks
– Clear success metrics tied to business KPIs
– Ethical guardrails to prevent model drift and bias
Their approach includes building explainable models and aligning with enterprise governance and compliance protocols.
04. How do we structure a roadmap to scale generative AI or AI agent initiatives enterprise-wide?
A strategic and well-considered approach to introducing AI-enabled solutions is key to ensure efficiency gains, revenue maximisation, customer success, process optimization, and reduced operational costs, all while justifying the necessary investment of time and resource.
– Identify High-Impact Opportunity
– Assess whether to Build in-house or to partner with vendor
– Develop Minimum Viable Product
– Govern and Measure ROI
– Scale
To assist in developing a Data and AI strategy read our resource guide The Executive’s Guide to Impactful AI.
05. What kind of ROI can we expect from generative AI and agent-based automation, and how do we measure it?
ROI depends on the use case but can include:
– Increased productivity and automation (20–40%)
– Reduction in human effort and decision time (30–50%)
– Improved customer retention and engagement
– Faster go-to-market with product innovations
– Higher forecasting and pricing accuracy
Measurement metrics include:
– Time saved per task
– Uplift in conversions or retention
– Reduction in costs/errors
– Increase in throughput or customer satisfaction (CSAT)