AI is reshaping the way enterprises think about competitive and market intelligence—but for many organizations, the promise is still out of reach. In Northern Light’s recent webinar, The Real Cost of AI in Market and Competitive Intelligence: Why Most AI Fails and How to Make It Work, President Rob Trail and Chief Product Officer Sheri Larsen offered a candid look at where AI initiatives are breaking down and how teams can move beyond shallow pilots to meaningful, enterprise-ready outcomes.
Across the conversation, one theme stood out clearly: AI can only deliver value when the underlying data foundation, governance, and workflows are strong enough to support it. Without that, even the most sophisticated models quickly fall into irrelevance, inconsistency, or outright hallucination—and CI and research teams are left double-checking every output.
Why AI Is Struggling Inside the Enterprise
Trail opened with a frank assessment of what most enterprises are experiencing today: fragmented content ecosystems, shallow AI outputs, and governance friction that slows or stops meaningful innovation. These challenges aren’t new, he emphasized—but AI has amplified them.
1. Fragmented content remains the root cause
Before AI entered the picture, organizations already struggled to unify syndicated research, internal primary intelligence, business news, and other critical sources. AI hasn’t solved that—it has exposed it.
Trail described teams asking AI to synthesize insights across datasets that aren’t connected, licensed consistently, or governed properly. The predictable result: incomplete answers and unreliable conclusions.
2. First-generation AI tools deliver shallow or unverified results
Many organizations rushed to deploy chatbots, copilots, and narrow point solutions. While these tools can summarize documents or answer simple questions, Trail explained that they too often rely on generic training data or incomplete context:
- citations pulled from Reddit or Wikipedia,
- unverified sources shaping conclusions,
- missing or inaccessible enterprise datasets.
For organizations where “accuracy matters,” especially in life sciences and financial services, this is unacceptable.
3. Governance challenges are slowing adoption
Trail noted that enterprises are rightly cautious. High-profile lawsuits and unclear compliance boundaries have led many organizations to stand up AI governance committees—but these teams often lack the frameworks needed to approve AI uses confidently.
This combination—high demand, unclear rules, and immature tooling—has left many AI projects stuck in pilot purgatory.
Efficiency Isn’t the Same as Insight
Even with these challenges, teams are seeing productivity gains. AI can summarize content, accelerate routine tasks, and reduce drafting time. But as Trail emphasized, speed does not guarantee quality: “Yes, we’re working faster—but that doesn’t mean the quality is there.”
The real failure point is trust. Without domain context, curated sources, or authenticated content licensing, organizations simply cannot rely on AI to support meaningful decision-making.
The Path Forward: Agentic AI Built for Real Research
The most significant shift discussed in the webinar is the move from generic AI chatbots to agentic AI workflows—systems that break down complex research tasks into discrete steps, each executed by specialized agents working in sequence.
Instead of asking a model to produce a single answer, agentic AI:
- Conducts interviews to clarify the research question
- Selects the appropriate enterprise-approved datasets
- Builds sophisticated Boolean queries
- Evaluates coverage and identifies gaps
- Pulls and validates sources
- Produces a cited, auditable research report
Larsen demonstrated this in action with two live examples—one in pharmaceuticals, the other in mobile device strategy.
Example 1: A complex pharma competitive landscape analysis
Larsen walked through a scenario where a CI professional asks: “How are competitors diversifying beyond GLP-1 monotherapy, and what timelines signal the next disruptive class?”
The agent workflow:
- Interviewed the user to refine scope and required detail.
- Proposed a research plan.
- Queried multiple licensed and internal datasets using optimized search strategies.
- Assessed whether the coverage was sufficient.
- Compiled a detailed, fully cited report—including timelines, emerging mechanisms of action, and competitive risk indicators.
Example 2: AI adoption among mobile manufacturers
In a lighter scenario, the system synthesized competitive strategies related to on-device AI, proprietary models, hardware acceleration, and market share projections. The final output was a concise executive summary with source-linked evidence.
Across both examples, the key benefits were clear:
- trustworthy insights grounded in enterprise-licensed and internal research
- consistent methodology
- repeatable workflows
- Level of depth not possible from a simple “chat with your data” experience
The Four Requirements for Making AI Work in CI
Trail outlined the four essential elements organizations must have in place before agentic AI—or any meaningful enterprise AI—can work:
- Governance & Risk Management: AI solutions must respect licensing, copyright, data privacy, and model-training constraints. Northern Light’s longstanding discipline here is a major advantage.
- Security & Resilience: Enterprise AI can’t rely on brittle or experimental infrastructure. Reliability must match the rigor of the existing enterprise platform.
- Unified, Curated Data Access: AI is only as good as the data it can see. If news, internal research, financial records, and licensed content live in different silos—or worse, are inaccessible—AI simply cannot deliver.
- Executive Sponsorship: Without leadership support, AI initiatives stagnate. Trail described multiple examples where senior champions enabled governance approval, platform adoption, and measurable impact.
A Real-World Transformation Story
One of the most compelling moments came when Trail shared how a Fortune-100 financial services company rebuilt its entire research workflow before launching AI initiatives. The organization:
- Consolidated licensed and internal research
- Standardized access controls
- Brought all content into a governed, unified framework
Because the foundation was strong, the company became one of the first within its enterprise to earn governance certification for AI use, ultimately saving millions in productivity gains.
Preparing for the Next Phase of AI in Intelligence Work
As Larsen explained, the future of AI-driven research isn’t simply better chatbots—it’s goal-oriented, multi-agent workflows that adapt to how analysts actually work. And to prepare, organizations should:
- Understand their AI governance requirements
- Strengthen data readiness and content aggregation
- Recruit senior champions
- Identify repeatable research workflows that would benefit from automation
- Measure outcomes—not just outputs
AI will increasingly support proactive intelligence delivery: dashboards, newsletters, alerts, and eventually multimodal outputs like presentations and podcasts. But none of that is possible without trusted data at the core.
Closing Thought: AI Won’t Replace Analysts—But Analysts Who Use AI Will Outperform Those Who Don’t
The webinar underscored a critical truth:
AI isn’t here to replace competitive intelligence teams. It’s here to eliminate the manual research burden so analysts can focus on higher-order thinking: context, judgment, and strategic insight.
The organizations that thrive over the next 12–24 months will be those that build the right foundation—governance, security, unified content—and deploy AI not as a novelty, but as a true research partner.
If you missed the broadcast, request access here.
.jpg)