Selecting a GenBI Solution Based on Your Team’s Analytics Value Gaps

As the confluence of generative AI and business intelligence, “GenBI” is driving excitement throughout the business world. Many are eager to adopt it, but with numerous solutions jostling for your attention, it’s not easy to choose the one that’s the best fit for your circumstances.

Part of the problem is that GenBI means different things both to different organizations and to different vendors. Each approach offers its own combination of key functionalities and benefits for line-of-business (LOB) users and for data teams. The focus ranges from on-demand conversational GenBI which responds to naturally worded queries, to embedded GenBI which surfaces insights within workflows across environments, plus narrative GenBI which turns data into easy-to-understand explanations.

Each approach can be transformative, but it has to be implemented in the right way in an organization that needs the capabilities it brings. Additionally, the organization has to match the solution to its level of data tech maturity, key requirements for data insights, and the stakeholders who need those insights.

This makes it crucial to be clear both about what you need in a GenBI solution, and in your understanding of what each provider is really offering. When you know which of your crucial analytics needs are not being met, and you’re fully informed about the benefits of varying GenBI approaches and what kind of tech infrastructure they need for effective implementation, you can make a clear-eyed choice that delivers ROI and sharpens your competitive edge.

This article takes a deep dive into the main approaches to GenBI, exploring what they involve, their pros and cons, and the organizations that are best suited to each approach.

1.  On-Demand Conversational GenBI

Conversational GenBI makes data insights accessible to all. These solutions draw on NLP to respond interactively to naturally-worded user questions. This approach removes the barriers to data insights for LOB users, but it can produce inconsistent answers unless you have a strong semantic layer.

Data teams spend less time responding to data requests. For data teams, conversational GenBI eases the burden of building one-off dashboards and running custom coded database prompts in response to ad-hoc analysis request tickets. By propagating self-service analytics across the organization, these solutions reduce dependence on SQL skills.

The main drawback is that unless they’re closely integrated with the rest of your data management workflows, conversational GenBI platforms can be hard to control or standardize at scale.

LOB users gain self-serve data access. The biggest beneficiaries of conversational GenBI tools are LOB users. The NLP interface allows them to ask questions in their own words, and to drill down, filter, and refine queries conversationally. They don’t have to wait for data teams to get to their request; they can access charts, tables, and summaries instantly, exploring data without dashboards or SQL skills.

As long as the underlying data quality is ready for AI-powered analysis and as long as metrics are vigilantly managed in a manner that’s aligned with business model logic, then everyone comes out ahead.

Conversational GenBI is ideal for organizations that want broad, self-service access to data, with many ad-hoc questions and an overloaded data team. The best-fit companies have:

• Hundreds to thousands of business users

• Sales, marketing, product, ops, and executive leaders as data insight consumers

• Modern cloud warehouses, a basic semantic layer or curated models in their analytics stack

• Low to mid analytics maturity, moving from dashboards to self-service

• Small to medium size data teams

• Are in SaaS, ecommerce, tech, and digital-first industries

Conversational BI vendors have a UX that’s built on natural language queries and self-serve insights. Examples include ThoughtSpot, Microsoft Power BI Copilot, and Tableau Pulse.

ThoughtSpot stands out, because it supports conversational querying as its primary UX, not an add-on, with natural language replacing dashboards as the main interface. It has a strong semantic layer for governed self-service, and a clear focus on self-service exploration at scale.

2.  Embedded GenBI

Embedded GenBI moves data from insights to actionability. With embedded GenBI (also called operational GenBI), insights appear just in time within business workflows or apps, using RAG data retrieval and automated visualizations. It makes insights actionable, but requires strong governance and data models that are integrated into the architecture.

Data teams spend less time building dashboards and models. Embedded GenBI allows data teams to reuse core data models across many workflows, reducing the need to build separate BI tools and dashboards. Because insights appear directly where decisions are made, operational teams send fewer ad-hoc requests, while the data team is able to impact real business actions without additional analyst effort. On the downside, embedded GenBI requires careful control of data governance.

LOB users gain actionable insights within workflows. LOB users can see insights inside the tools that they already use, like their CRM or ERP, without the friction of context switching to a separate BI tool. With recommendations or explanations surfacing automatically in connection to a task, they can act directly on insights for tasks like contacting a customer or adjusting an order.

Embedded GenBI is best for organizations where analytics has to live inside workflows or products and insights are needed inside business processes, not in separate BI tools. The best-fit companies have:

• Hundreds to tens of thousands of internal or external users

• Frontline staff, partners, customers, and operational teams as data users

• Core operational systems (CRM, ERP, product apps) plus governed a data platform in their analytics stack

• Mid to high analytics maturity

• Medium to large (20–200+ people) size data teams

• Are in manufacturing, healthcare, financial services, and SaaS industries

Embedded GenBI vendors hold embedding analytics inside products as a core value, delivering insights within operational workflows. Examples include Pyramid Analytics, Logi Analytics, and GoodData.

Pyramid Analytics stands out because it offers end-to-end analytics and GenBI in a single platform that’s designed to operate inside enterprise workflows for embedded, governed, context-aware insights. It natively combines modeling, analytics, and GenAI, instead of adding a chat layer onto another BI tool.

3.  Narrative GenBI

Narrative GenBI boosts AI understanding and explainability. Narrative or insight-centric GenBI solutions produce summaries or explanations that make data insights understandable for everyone, especially non-data experts, using NLP, automated visualizations, and RAG data retrieval. However, deeper data exploration can be limited.

Data teams are freed from repetitive tasks. Narrative GenBI eliminates repetitive report writing by automating weekly and monthly summaries. This standardizes the way that insights are communicated, while freeing data teams from explaining numbers to stakeholders. The drawback is that the quality of insights depends heavily on underlying data logic, and if you aren’t connected to a proper BI engine, this will be missing.

LOB users gain insights they can understand more easily. Narrative GenBI explains trends in simple language and turns dashboards into engaging narratives, helping LOB users to understand the insights they receive. This makes it simple to share data stories with other stakeholders, and delivers automated performance summaries that ease the process of tracking key drivers of change.

Narrative GenBI is best for organizations focused on reporting and executive communication, with a heavy manual report burden and recurring updates. The best-fit companies have:

• Dozens to a few hundred users

• Executives, finance, marketing, and regional managers as data users

• Traditional BI dashboards plus a curated metric layer in their analytics stack

• Low to mid analytics maturity

• Small to medium size data teams

• Are in retail, media, and consumer goods industries

Narrative GenBI vendors focus on automated written insights, emphasizing explanation over exploration. Examples include Narrative BI, Arria NLG, and Yseop.

Narrative BI stands out not because narrative generation is the core product, not a feature. It’s optimized for clarity of communication, replacing manual reporting and stakeholder updates with AI-generated summaries and an emphasis on explanations and drivers.

The Best GenBI Solution for You

There’s no single “best” GenBI solution, but there is one that’s best for your organization. By paying attention to your data analytics needs and considering the core approach of each vendor, you can find a GenBI data analytics platform that helps you to achieve your business goals.