The core question that has consumed analytics and business intelligence leaders for years is, “What is the one best way for business users to access data?” The answer has at times been reports, dashboards, or low-code GUI-based self-service analytics. The latest answer is generative AI-based natural language prompts.

Maybe we have the question wrong. Perhaps we should ask how to optimize the emerging ecosystem of multiple complementary patterns for different decisions, workflows, and user personas.

Organizations should prepare for four complementary data consumption models that will coexist:

  • Visual and low-code analytics. Despite generative AI excitement, visual exploration remains one of the best ways to understand complex relationships, spot patterns, and answer questions that are hard to express in natural language. As one tax advisory client told Forrester, data questions are often several pages long. Point-and-click and drag-and-drop experiences will remain critical for analysts, domain experts, and business users investigating multifaceted problems.
  • Operational analytics. Users will continue to consume insights inside systems of work such as customer relationship management, enterprise resource planning, and industry-specific applications. Analytics will become embedded, contextual, and action-oriented. The goal in this pattern is not just to inform decisions but to influence actions at the moment decisions are made. Often, the best analytics experience is the one users never consciously recognize as analytics.
  • Natural language with a shared semantic foundation. Natural language is becoming a primary data interface, but queries will not come from one place. Users may ask questions through business intelligence assistants, enterprise copilots, domain-specific agents, workflow tools, or other agentic AI platforms. What matters is grounding them in the same semantic layer so that every agent interprets business terms, metrics, relationships, and context consistently.
  • Agentic-based subscription analytics. More organizations are adopting a subscription model for analytics consumption. Instead of hunting for insights, users subscribe to outcomes, metrics, events, or responsibilities, while agentic AI systems monitor the environment for them. These agents can deliver alerts when thresholds are crossed, anomalies emerge, trends shift, or opportunities arise — and eventually recommend or initiate next-best actions within governance guardrails.

If you look at what is new in these patterns, the most important shifts are:

  • The growth of more “push” analytics, where outputs are delivered when an agent, rule, or algorithm detects something important.
  • The centrality of context in every analytical product, where semantic layers, ontologies, and context graphs clarify definitions, reduce hallucinations, and improve trust.
  • The expansion of deeply embedded experiences, where dashboards, conversational interfaces, embedded analytics, and proactive subscriptions will coexist because each addresses a different persona or different mode of decision-making.

For data and technology leaders, the strategic challenge is not choosing among these models. It is creating a common semantic and contextual foundation that supports all of them consistently. The organizations that succeed will be those that treat semantic layers and context graphs not as analytics features but as enterprise infrastructure for data, analytics, and AI.

With this in mind, Forrester has launched coverage of semantic layer platforms, with a Q4 2026 landscape report and a Q1 2027 Forrester Wave™ evaluation planned. We are also conducting extensive primary research on how Forrester will cover knowledge and context graph technologies and markets.

If you have questions on any of these topics, please set up a call with me.

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