Key takeaways
- AI adoption has surged from 55% in 2023 to 88% in 2025, making AI a standard tool across organizations.
- Innovation leaders remain concerned about security, implementation challenges, skill erosion, and poor-quality data.
- Large language models (LLMs) are only as effective as the context and sources they receive.
- The highest-value AI applications for innovation teams include information curation, structured brainstorming, and modeling and forecasting.
- Contextual AI, powered by trusted intelligence and expert insights, enables faster and more defensible innovation decisions.
AI for innovation teams: Moving beyond AI answers
AI has quickly become a standard part of the innovation toolkit. According to data highlighted during Lux Research’s recent webinar, Beyond AI Answers: Improving Strategic Thinking for Innovation Teams, organizational adoption of large language models (LLMs) increased from 55% in 2023 to 88% in 2025. Yet despite widespread use, many organizations are still struggling to turn AI into a meaningful competitive advantage.
The challenge isn’t access to AI. It’s access to the right information.
During the webinar, Anthony Schiavo, Senior Director and Principal Analyst at Lux Research, explained that innovation teams increasingly face an information management problem. While AI can rapidly generate responses, those responses are often limited by incomplete public data, biased sources, and a lack of business-specific context. As a result, organizations risk making decisions based on information they cannot fully validate.
Rather than treating AI as a conversational assistant, innovation leaders should focus on building structured information workflows that combine curated data, proprietary intelligence, and expert judgment. This approach allows teams to leverage AI for scalable analysis while ensuring decisions remain grounded in trusted evidence.
Source: Data source: Stanford 2026 AI Index Report
Three high-impact AI use cases for innovation teams
1. Automating information curation
Innovation teams must monitor news, patents, academic research, startup activity, and funding announcements. By combining APIs, machine-readable data sources, and LLMs, organizations can automate the collection and analysis of critical intelligence at scale.
2. Improving brainstorming with structured context
AI-generated ideas become significantly more valuable when grounded in curated industry intelligence and established ideation frameworks. Rather than generating isolated suggestions, contextual AI can continuously surface opportunities aligned with strategic priorities.
3. Accelerating modeling and forecasting
LLMs can help create preliminary technology and business models when supplied with the right data and assumptions. These models help teams assess scalability, economics, and feasibility while identifying areas that require deeper investigation.
Webinar Q&A highlights
What innovation activities are best suited for AI?
Lux recommends using AI to automate routine work, support ideation, and scale expert workflows. Human teams should remain focused on complex, high-stakes decisions that require creativity and judgment.
Why is context so important for AI?
Because LLMs predict text based on available information, the quality of their outputs depends heavily on the quality of their inputs. Providing curated, trusted intelligence improves both accuracy and relevance.
How does Lux AI improve enterprise AI?
Lux AI improves the quality and defensibility of AI-generated outputs by layering trusted research, expert analysis, and proprietary intelligence into existing AI workflows. It helps teams validate assumptions, compare perspectives, and strengthen strategic decision-making.
Is Lux AI for Copilot available today?
Yes. Lux confirmed during the webinar that its Copilot integration is available and actively being used to support client workflows.
The future of AI is contextual intelligence
The organizations that gain the most value from AI will not be those that simply ask better questions. They will be the ones that build better information ecosystems.
As innovation teams face increasing pressure to do more with less, contextual AI offers a path to scale expertise, manage information overload, and improve strategic decision-making. By combining trusted intelligence with AI-powered automation, organizations can move beyond AI-generated answers and toward defensible, evidence-based innovation decisions.
Watch the webinar on demand
Want to learn how leading innovation teams are using contextual AI to improve decision-making, streamline information management, and accelerate innovation?
Watch the on-demand webinar, Beyond AI Answers: Improving Strategic Thinking for Innovation Teams, to explore practical frameworks, real-world use cases, and Lux’s approach to contextual AI.