As enterprises push AI beyond pilots, these 10 data and AI consulting firms are addressing challenges across data, analytics, governance and deployment.
CIOL Bureau25 Sep 2026
13:49IST
New Update
Data and analytics have moved well beyond dashboards and periodic reporting. For many large enterprises, the problem today is not collecting more data. It is making sense of data spread across different systems, legacy infrastructure, cloud platforms and business functions.
Generative AI has made that problem harder to ignore. Enterprises experimenting with AI need reliable data pipelines, governance and enough context to move projects beyond pilots. Gartner has predicted that through 2026, organizations will abandon 60% of AI projects unsupported by AI-ready data.
That is also changing what enterprises expect from data and AI consulting firms. The work increasingly stretches from data engineering and cloud modernization to analytics, AI deployment and governance. Here are 10 companies operating across different parts of that enterprise data and AI stack.
1. Fractal Analytics
Fractal works across enterprise AI, analytics, engineering and design. Its work covers areas including decision intelligence, customer analytics, risk, revenue growth, supply chain and AI-led business transformation.
A key part of its approach is moving analytics and AI into business workflows rather than treating them as standalone technology projects. That reflects a broader enterprise challenge: connecting models and data platforms with the decisions employees make across different functions.
2. Tiger Analytics
Tiger Analytics works across data strategy, data modernization, data science, AI engineering, machine learning, business intelligence and MLOps.
Its projects span retail, CPG, financial services, manufacturing, healthcare and technology, with applications ranging from forecasting and customer analytics to operational decision-making. Its mix of data engineering and analytics reflects how enterprise AI projects increasingly depend on the infrastructure underneath the models.
3. Eucloid Data Solutions
Eucloid Data Solutions works across data, AI and marketing technology. Its capabilities include modern data platforms, data engineering, customer intelligence, business intelligence, cloud transformation and enterprise AI.
One example shows how that work is evolving. For a large US home healthcare provider, Eucloid consolidated more than 65ance and cost efficiency by around 30%. Genie-powered AI assistants were subsequently introduced as the first AI use case on that foundation
The company is also working on GenBI and AI agents aimed at bringing AI into business workflows. Eucloid is a Databricks Premier Partner and has teams in India and the US, working across healthcare, financial services and retail.
4. Tredence
Tredence focuses on industry-specific data and AI projects, covering data strategy, maturity assessments, enterprise-scale models, generative AI, automation and responsible AI.
Its approach highlights an issue enterprises continue to face: producing an analytical insight is only one part of the process. Organizations also need systems and workflows that allow employees to act on that insight across business functions.
5. Quantiphi
Quantiphi works across AI applications, generative AI, conversational AI, data analytics and cloud modernization.
Its combination of cloud engineering and machine learning addresses the production side of enterprise AI, where companies need to connect models with existing infrastructure, data and applications. That becomes increasingly relevant as organizations move from isolated AI experiments toward broader deployments.
6. LatentView Analytics
LatentView Analytics has operated in the analytics market since 2006. Its work covers analytics, AI, digital transformation and business insights across sectors including technology, CPG, retail, financial services and healthcare.
Its capabilities cover different stages of enterprise analytics, from business intelligence and customer understanding to the use of AI and analytics in decision-making.
7. Mu Sigma
Mu Sigma approaches the market through decision sciences, combining mathematics, analytics and business problem-solving.
Rather than separating analytics from business decision-making, its model focuses on using data and analytical frameworks to address complex business questions. The company refers to professionals working in this model as Decision Scientists.
8. C5i / Course5 Intelligence
Course5 Intelligence combines analytics, AI, market intelligence and research.
Its capabilities include advanced analytics, natural language processing, customer analytics and digital transformation. This places much of its work around connecting customer and market information with business decision-making.
9. MathCo / TheMathCompany
MathCo develops custom data products and works across analytics and AI, supported by its NucliOS platform.
Its model emphasizes enterprise ownership of the intellectual property and intelligence systems developed through engagements. That addresses another consideration for companies building AI capabilities: how much of their data, models and decision-making infrastructure they want to own internally rather than continuously depend on an external provider.
10. SG Analytics
SG Analytics has expanded from its investment research roots into data, analytics and AI consulting.
Its capabilities now cover AI, advanced analytics, data engineering, research and intelligence, with domain experience across financial services, capital markets, technology, media and telecom.
Its evolution also reflects the wider convergence taking place between traditional research, data engineering, analytics and AI as enterprises look to connect information with operational decisions.
Enterprise AI Is Becoming A Data Problem
The common thread across these companies is not simply generative AI. It is the infrastructure and data work required before AI can operate reliably inside an enterprise.
An organization may have access to advanced models, but those models still depend on the quality, availability and governance of the underlying data. Legacy systems, fragmented pipelines and inconsistent business definitions can therefore become AI problems as much as data problems.
That is changing the role of consulting firms as well. Enterprises increasingly need help connecting data architecture, cloud infrastructure, analytics and AI with actual business processes. As AI adoption moves beyond experimentation, the question is becoming less about who can build a model and more about whether organizations have the data foundations and operating processes needed to use it at scale.
digital-transformationaiEnterprise
