Info-Tech Research Group

Info-Tech Research Group published new research findings showing that agentic AI implementations have moved beyond pilots to autonomous systems triggering workflows, accessing sensitive data, and making operational decisions across enterprises, revealing integration and governance risks.

Leadership read: Info-Tech Research Group's launch shifts talent demand before revenue catches up. In the sector, taking a release to scale rewards product leaders with a commercial edge and operators who build the post-launch motion. Watch whether Info-Tech Research Group backs it with senior go-to-market hires across Oceania. That separates a platform move from a one-off.

curated · 2026-08-19 · context →

Info-Tech Research Group hosting major AI conference Info-Tech LIVE 2026 in Las Vegas with focus on AI enterprise execution and business outcomes

Leadership read: Info-Tech Research Group's conference is not primarily a product launch; it is a demand-generation and positioning event built around a specific thesis: that AI is moving from proof-of-concept into operational accountability. The strategic weight here is the framing. By centering the programme on enterprise execution and business outcomes rather than capability showcases, Info-Tech is effectively telling its CIO and IT leadership audience that the evaluation criteria for AI investments have shifted. Research and advisory firms that nail that framing at scale can shape procurement conversations and vendor shortlists for a full buying cycle. The related-signal set is broad. This is one of 12 product-launch signals we tracked across the period, but the directly comparable activity is thin: the set spans everything from LiFePO₄ batteries to healthtech wearables, with no clear cluster of analyst-firm or enterprise-AI conference signals. The one thematically adjacent note is OpenAI's Private Safety Processing launch, which points to the same underlying pressure: enterprises are now asking how AI performs reliably and accountably, not just what it can do. That pressure is creating demand across organizations for leadership at the intersection of AI governance, enterprise product strategy, and commercial translation. The market is moving toward operators who can convert AI capability into auditable workflow outcomes. Functions under pressure include enterprise product management, technical pre-sales and solution architecture, and the policy and risk layer that sits between AI deployment and regulated business operations.

curated · 2026-05-15 · context →

Info-Tech Research Group launched 'Build a Sustainable Knowledge Transfer Strategy' blueprint addressing critical knowledge preservation challenges as AI adoption accelerates

Leadership read: Info-Tech's blueprint is not a research publication in the ordinary sense; it is a codified methodology, which means the firm has moved from diagnosing a problem to selling a repeatable operational process for solving it. That shift commits the firm to a different commercial posture: the product now has to hold up against IT teams that will actually run it, not just read it. The underlying bet is that knowledge preservation has crossed from a governance nicety into a live operational risk, driven by the combination of AI-accelerated role change and workforce churn compressing the window in which institutional knowledge can be captured before it walks out the door. The related-signals set is too diffuse to build a tight category count around this specific signal. Of the twelve product launches tracked in the last 90 days, the majority sit in fintech, biotech, consumer, and media workflows, with no direct comparables in IT research or knowledge-management tooling. That thinness is itself a data point: the knowledge-transfer problem is being surfaced loudly by analysts and consultants, but the product response from dedicated vendors is still sparse. Where the pattern does concentrate is at the intersection of AI adoption and enterprise workforce change. Companies operating at that intersection face rising demand for leadership in enterprise learning architecture, change management at scale, and the product management discipline needed to translate tacit operational knowledge into systems that AI tools can actually work with rather than around.

curated · 2026-05-05 · context →