Reporting projects evolve into AI solutions. Data and LLM expertise will be key.
As enterprises accelerate their adoption of AI, the partner ecosystem is entering a phase of structural change. Revenue models associated with traditional analytics, reporting, and large-scale data warehousing are evolving, while demand for AI-driven applications, domain expertise, and modern data architectures is increasing.
This change is no longer about adding AI as another service line. It’s about redefining where the value lies in data, analytics, and application delivery.
“When we talk to our customers, and our partners know this, AI applications are where they’re most focused and spending right now, but they also understand that data is at the core of building these agents and applications,” said Srikanth Gokulnatha, Oracle’s senior vice president of AI data platforms, analytics, and analytical applications products, in a conversation with CRN India.
B2B use cases require companies to combine private enterprise data with frontier large-scale language models, creating challenges beyond traditional data warehousing, Gokulnatha said.
Unlike previous data warehouse projects that focused on structured datasets and gold-layer environments, AI-driven use cases require unstructured and transactional data. In many machine learning deployments, data remains in object stores rather than being fully cleansed and moved to a centralized warehouse.
Gokulnatha points out that nearly 70% of the effort in building AI agents involves data engineering, and data is the first hurdle that organizations must overcome.
For partners, its importance will never diminish and will change as organizations replace traditional data workloads with enterprise lakehouse models to modernize their architectures and support AI applications.
Customers want to implement AI, but often struggle to identify specific use cases, creating an opportunity for partners to step into domain-focused agent portfolios and structured go-to-market approaches, Gokulnatha added.
From IT consultants to avant-garde engineers
The Partner Handbook in AI does not have data preparation as its main focus. Traditionally, partners prepared enterprise data first and then defined use cases, but now that order has changed. Partners work at the business requirements stage, tackling the problem first and then building an AI solution around it.
“There are two approaches,” Gokulnatha says. “One is a bottom-up approach where you prepare the data, identify the use case, and build the solution. But more and more partners are starting with the business problem first.”
This change reflects the speed at which AI capabilities are evolving. Solutions built on the constraints of last year’s model may no longer work.
As frontier models improve, especially in areas like coding and natural language processing, functions that were once considered complex, such as NLP to SQL translation, are becoming more automated. This means that partners cannot afford to have a static solution blueprint and must continually adapt.
As a result, new operating models will emerge.
Gokulnatha said large companies are no longer positioning themselves as IT or functional consultants.
“Instead, many describe themselves as ‘forward deployment engineers,’ teams that work closely with customers to define AI-driven business outcomes and build, test, and iterate on domain-specific agents.”
This structural change requires re-education.
“Almost 70% of the work on AI projects is still rooted in data engineering, an area in which most partners already have deep knowledge, but the real investment is in agentic AI capabilities and advanced data science capabilities,” Gokulnatha said.
Our partners have built in-house training programs and work closely with platform vendors to strengthen these skills.
“What our partners need help with and are investing in building in-house capabilities is how to use agent AI and how to deepen and expand their knowledge in data science and related areas. That is a key area of investment for them,” Gokulnatha said.
He said more partners are establishing centers of excellence (CoEs) as part of their AI strategies.
These CoEs serve as both lab and demonstration environments. Partners use these to build, test, and introduce agents developed for specific business use cases.
Gokulnatha added that this is changing the way partners enter the market. Rather than leading with an army of trained professionals and certifications, partners present off-the-shelf agents designed for specific sectors and vertical industries.
Customers can access these centers to see agent performance and evaluate use cases directly.
He said this model is increasingly becoming the preferred go-to-market approach for AI-driven efforts.
Where partners should invest next
Traditional analytics and reporting projects aren’t slowing down, even though they’re changing shape. Instead of existing standalone data warehousing and dashboard efforts, these capabilities are increasingly being incorporated into broader AI applications.
Gokulnatha explained that large companies typically have around 50 core reports that are widely used throughout the organization. However, over time, a long tail of reports used by only a small number of individuals often increases the total number of reports to 1,500 or more. Maintaining a long tail creates operational overhead.
AI-driven conversational interfaces allow organizations to replace the long tail with chat-based access to insights while retaining standardized core reporting. Users can query data directly instead of relying on static dashboards.
“The fundamental demand for analytics is not going away. Instead, reporting is being transformed into an AI-powered experience. As a result, standalone dashboard projects may become less important, but analytics remains central and is now embedded in AI agents and enterprise applications,” Gokulnatha said.
This change has a direct impact on partners’ investment priorities. Gokulnatha said partners should focus on two areas over the next 18 months.
The first is modern data architecture.
He pointed to the increasing adoption of open standards, noting that an open lakehouse approach to data management is becoming a fundamental requirement for AI agents and applications.
The second is a deep and continuous understanding of how large-scale language models are evolving.
Gokulnatha said assumptions about how the model will work could change in the coming months.
Partners need to understand not only the model’s capabilities, but also architectural tradeoffs such as context window limitations, latency impacts, and accuracy considerations.
“Patterns like multi-agent orchestration are becoming increasingly relevant as organizations seek to bring AI into mission-critical workflows,” said Gokulnatha. “Deploying AI into these environments requires architectural depth, not just experimentation.”
As AI adoption increases, partner revenue will increasingly depend on reusable domain IP and architectural depth, rather than standalone reporting projects and human-driven delivery.
Companies that combine a modern lakehouse foundation with powerful agent design capabilities are well-positioned to participate in mission-critical AI deployments, delivering long-term value and benefits.
