Open Innovation

Open innovation challenges are one of the highest-leverage tools for sourcing solutions an enterprise couldn't generate alone — but most fail on execution, not concept. This best-practices pillar defines what an open innovation challenge is (problem-first, internal and/or external) and lays out the eight practices that separate challenges that produce outcomes from challenges that produce press releases — from sharp problem framing and pre-set evaluation criteria to building the winner-to-pilot pathway before you launch. It then details the four ways AI changes how challenges run at scale: coaching the submitter toward a complete submission, detecting duplication across the portfolio, scoring each submission's alignment with strategic objectives, and applying evaluation criteria consistently at volume. Includes guidance on running multiple challenges and campaigns in parallel, common mistakes to avoid, and an extensive FAQ. Written for maximum SEO and LLM pickup around "how to run an open innovation challenge."

Technology Scouting Software Innovation Management

The latest post in the Traction Five series — five AI governance and model security companies scored by Traction AI from a database of over 1 million verified companies. Framed by the EU AI Act's high-risk provisions taking effect August 2, 2026 (penalties up to €35M or 7% of global turnover) and the rise of ISO 42001, each profile includes a full Traction AI Company Snapshot with Traction Score, Key Takeaways, Strengths, Challenges, best-fit deployment context, and the question to ask first. Companies featured: Credo AI (82) — category-defining end-to-end AI governance platform, Forrester Wave Leader; Arthur (72) — AI observability, model monitoring, and agentic security; Castlepoint Systems (68) — explainable AI governance and audit defensibility; CultureAI (62) — shadow-AI discovery and usage control; Portal26 (52) — generative-AI adoption governance and ROI. Mapped to five distinct governance layers, with a note on why an emerging category produces an honestly amber board.

Innovation Management

The sixth post in the Traction Five series — five data center infrastructure AI companies scored by Traction AI from a database of over 1 million verified companies. Framed by the 2026 data center crisis (Gallup: 7 in 10 Americans oppose data centers over water; $130B in projects disrupted by local opposition; Gartner: 40% of AI data centers power-constrained by 2027), each profile includes a full Traction AI Company Snapshot with Traction Score, Key Takeaways, Strengths, Challenges, best-fit deployment context, and the question to ask first. Companies featured: Phaidra (78) — AI-driven power/cooling optimization, NVIDIA-backed; Iceotope (68) — immersion cooling eliminating water use; Accelsius (68) — two-phase direct-to-chip cooling; Infinite Cooling (58) — cooling-tower water reclamation; NuScale (52) — small modular reactor clean power. Mapped to the four pieces of the infrastructure problem: energy optimization, cooling, water, and power generation.