AI Scientist
Every week, we filter the AI chaos to bring you fresh innovation insights and actionable takeaways.
Top Insights
1. AI scientist is already here
In August 2024, a team of machine-learning researchers launched “AI Scientist” that aims to fully automate the scientific process. Now it has produced a machine-learning paper that survived peer review in a top academic conference. This is the first credible proof that AI can execute entire knowledge workflows, not just assist. The system can generate ideas, run experiments, and write papers autonomously. The system produces work that is “borderline acceptable” but not highly novel.
AI Scientist is not one model, it’s a system of coordinated agents: literature review, hypothesis generation, experimentation, and writing.
For now, the scope is still narrow, only in computational domains. The system works best in areas where experiments are code-based and feedback is measurable and fast. Even creators emphasize AI should augment humans, not replace them.
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🎯 What to do:
Look through the R&D portfolio and identify where AI might help, probably in fully digital domains. Set up an AI-driven research initiative and humans only review its top outputs.
Build an originality filter to remove incremental ideas. Build a feedback loop where AI’s research ideas get tested, improved, and the methods are enhanced over time.
Have regular sessions to define new interesting research problems for AI to tackle.
Reference: How to build an AI scientist: first peer-reviewed paper spills the secrets (Nature)
2. There are common barriers for AI impact
Recent BCG research shows that only 5% of companies are generating sustained profit and loss impact while roughly 60% have seen little material benefits. Failure is rarely about weak tech, but rather weak transformation discipline.
AI delivers quick, visible wins, but the real value depends on harder, hidden changes (decision rights, incentives, workflows). For example, a team uses AI to speed up reports (visible win), but decision-making processes remain unchanged. There is no real business impact.
90% of companies are experimenting, but doing so in fragmented, small-scale ways. About 70% of AI value sits in core workflows, not isolated use cases.
The illusion of early success often causes leaders to limit funding before the transformation is complete. The last mile requires a disproportionate effort and investment to strengthen data foundations, industrialize processes, resolve cross-functional interdependencies, and embed responsible AI and risk controls.
🎯 What to do:
Identify workflows where decisions + cost + revenue intersect (e.g., pricing, underwriting, supply chain planning). Redesign them AI-first, not AI-assisted. Set a goal: “This workflow will never operate the old way again”
Ringfence a multi-year transformation budget, instead of annual approvals. Explicitly allocate 30–50% of funding to the last mile: data quality, process integration, change management. Treat early wins as proof of direction, instead of proof of completion.
Put AI on the same agenda as revenue, margin, and cost. Review AI initiatives in business performance meetings, not innovation updates.
Reference: Five Barriers CEOs Must Overcome for AI Impact (BCG)
Innovation Radar
Google introduced Gemini 3.1 Flash Live as a real-time audio model aimed at more natural, lower-latency dialogue and access via the Gemini Live API in Google AI Studio. Google also states that audio from 3.1 Flash Live is watermark‑embedded to reduce misuse, indicating a product-level shift toward built-in provenance controls for voice interfaces. → Real-time, tool-using voice agents are a practical path to automating phone-based customer experience and internal service desks.
A new paper shows that prompting an LLM to “act like an expert” can systematically reduce performance, even if it improves some generative behaviors. The article describes PRISM as a routing strategy using lightweight adapters to switch persona behavior on only when beneficial, rather than always enforcing an “expert” persona. → Many teams still rely on prompt folklore; this underscores the need for governance and evaluation, because bad prompting practices can silently degrade accuracy in high-stakes workflows like finance, compliance, or operational decision support.
Microsoft integrated Copilot across Power Platform’s model-driven apps (e.g. Dynamics 365). Users can now ask the Copilot assistant to query business app data, generate charts or summaries via Code Interpreter, and trigger actions across Microsoft 365 directly within Power Apps. →This brings AI automation into routine business workflows (for instance, generating a sales report or approving an expense) without leaving the application, boosting productivity for non-technical business users.
Anthropic shipped “Claude Code Channels,” enabling developers to message Claude Code from Telegram or Discord and receive results back, shifting interaction from synchronous sessions to persistent background work. Anthropic’s documentation describes channels as MCP servers that push events into a running Claude Code session, supporting Telegram/Discord/iMessage and requiring explicit enablement plus allowlists for security. → “Always-on” agents turn AI into an operations layer: work can be dispatched and completed while staff are offline, which is the practical prerequisite for real automation beyond chat-based assistance.
Cohere launched Transcribe as an open-source, state-of-the-art speech recognition model positioned as production-ready and available via Hugging Face. Cohere also claims top accuracy on Hugging Face’s Open ASR Leaderboard and describes a 2B Conformer-based architecture trained across 14 languages.
-> High-accuracy transcription is the foundation for searchable calls, automated QA, and compliance workflows, enabling “speech-to-operations” pipelines for support, sales, and internal meetings.
Grateful you’re here. If this sparked something for you, pass it along: good ideas grow when shared.