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AI has moved past the experimentation stage. For insights teams, the question is no longer whether generative AI can speed up a task. It is whether AI can change how trusted market knowledge flows into decisions, repeatedly, securely, and at scale.
Most organizations are not short of information. They are surrounded by consumer insight research, customer insights research, trackers, dashboards, transcripts, competitive intelligence, syndicated data, and past studies. But the knowledge is often fragmented across portals, SharePoint folders, supplier outputs, and local team systems. When a business stakeholder needs an answer, the evidence may exist, but the path from evidence to action is still too slow.
AI pilots are not the operating model
The execution challenge for most insights teams is to discern which AI can lead to growth and to drive the organization forward.
They may find that AI pilots may create impressive demos. Search-based repositories may make documents easier to find. But neither is enough if market intelligence still sits outside the workflows where decisions are made.
The next wave of AI in insights is not another chatbot on top of a static library. It is a trusted operating layer for enterprise AI: intelligence grounded in proprietary research, continuously updated, source-backed, governed, and available where teams already work.
This is how DeepSights was purposely designed: as a new system of trusted active intelligence, that transforms enterprise knowledge into decision-ready intelligence. It’s powered by always-on agents that help Insights anticipate change. It empowers them to act with confidence, based on consumer insight.
Trust is the entry requirement for market intelligence solutions
Insights teams are clear about what stands in the way:
- They want faster answers, but not at the expense of reliability.
- They want AI, but not generic outputs.
- They want automation, but still need human judgment, source transparency, and governance.
That is why active intelligence has to be trusted from the start:
- Claims need to be traceable.
- Data sources need to be clear.
- Source watchouts and evidence quality matter because they give business users confidence and give insights teams control.
Why Mars chose purpose-built AI
Leading CPG organization Mars’ experience demonstrates why organisations need purpose-built AI rather than relying solely on generic assistants. While general-purpose AI can help employees work more efficiently, it often lacks the business context, institutional knowledge, and research intelligence required to drive strategic decisions.
By combining DeepSights with a decade of consolidated research, Mars transformed a fragmented knowledge base into an always-on intelligence capability that understands business terminology, surfaces trusted insights in context, and enables teams to explore complex questions in natural language.
The result was not just greater efficiency, but a fundamental shift in how knowledge was activated across the enterprise, increasing adoption, amplifying the return on research investments, and helping teams make faster, more informed decisions. As Mars’ example shows, the real value of AI comes when it is purpose-built to understand and operationalise an organisation’s unique market intelligence, not simply generate content or retrieve information.
The bottom line
The winners in the AI race will not be the teams running the most AI pilots.
They will be the teams that redesign how intelligence reaches the business.
To do this, they need access to specialized AI for market intelligence and insights. Trusted, active intelligence gives insights leaders a way to connect consumer insights, market intelligence, and enterprise knowledge management into a governed system for action.
It’s very different from LLMs. General-purpose AI assistants like Microsoft Copilot, ChatGPT, Claude, and Gemini excel at navigating the knowledge within your organisation. They can connect information across documents, emails, meetings, and workflows, helping teams draft content, summarise information, and streamline everyday tasks with impressive speed and accuracy.
However, the strategic decisions that shape business growth require more than an internal view. Determining where to compete, identifying new opportunities, and anticipating customer needs all depend on understanding the external market landscape.
This is where general-purpose AI reaches its limits. These tools were designed primarily to work with broad information and organisational knowledge, not to interpret the complexity of market research and intelligence data. As a result, businesses relying on generic AI pilots and generic large language models can be left with a critical blind spot when supporting strategic decision-making.
Market intelligence is essential to making a decision that is contextually relevant to complex and rapid-changing markets.
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