Tag: Enterprise AI

  • AI Industry Leaders Share Insights on Strategy, Agents, and Transformation

    AI Industry Leaders Share Insights on Strategy, Agents, and Transformation

    Artificial intelligence continues to reshape industries, and a recent round of interviews with top AI executives reveals the key trends driving adoption. From infrastructure engineering to automotive manufacturing and energy management, leaders from Bentley Systems, Schneider Electric, Stellantis, and more shared their perspectives on how AI is being deployed responsibly and at scale.

    Julien Moutte, Bentley Systems

    Julien Moutte, CTO at Bentley Systems, discussed how AI agents are transforming infrastructure engineering. He emphasized that these tools augment engineers rather than replace them, enabling faster and more accurate design and analysis.

    Philippe Rambach, Schneider Electric

    Philippe Rambach, Chief AI Officer at Schneider Electric, highlighted the importance of critical thinking and a business-first approach when scaling AI across factories. He stressed that technology must align with operational goals to deliver real impact.

    Kaynaz Behdin, Stellantis

    Kaynaz Behdin, SVP of Digital, Data & AI at Stellantis, detailed how the automotive giant turns AI strategy into measurable business performance, focusing on data-driven decision-making across the enterprise.

    Johnson Agogbua, Kasi Cloud

    Johnson Agogbua, co-founder and CEO of Kasi Cloud, shared his vision for bringing hyperscale, AI-ready data centers to Africa, leveraging his three decades of experience building internet infrastructure.

    Emilio Tenuta, Ecolab

    Emilio Tenuta, SVP and Chief Sustainability Officer at Ecolab, discussed water resilience and AI-driven cooling, noting that data centers must rethink resource strategy to meet sustainability goals.

    Sunil Dadlani, Atlantic Health

    Sunil Dadlani, EVP at Atlantic Health, explained how putting human mission at the heart of AI innovation is redefining healthcare technology and improving patient outcomes.

    Moe Haidar, Nexthink

    Moe Haidar, Head of Agentic AI and Engineering at Nexthink, spoke about his passion for products and the breakneck speed of AI development, particularly in the agentic AI space.

    Ivana Bartoletti, Wipro

    Ivana Bartoletti, VP and Global Chief Privacy & AI Governance Officer at Wipro, detailed the foundational steps every organization must take to build trustworthy AI systems, emphasizing governance and ethics.

    Sasha Rubel, AWS

    Sasha Rubel, Head of AI Policy for EMEA at AWS, highlighted the urgent need for Europe to move from AI experimentation to large-scale transformation, addressing policy and innovation challenges.

    These interviews underscore a common theme: AI is not just about technology—it’s about strategy, governance, and human-centric implementation. As organizations across sectors embrace AI, the insights from these leaders offer a roadmap for responsible and effective adoption.

  • IBM Teams Up with ElevenLabs to Bring Natural Voice to Enterprise AI Agents

    IBM Teams Up with ElevenLabs to Bring Natural Voice to Enterprise AI Agents

    A landmark partnership between IBM and ElevenLabs is moving enterprise AI beyond text, delivering natural, secure, and scalable voice-first agents. The collaboration integrates ElevenLabs’ premium Text-to-Speech (TTS) and Speech-to-Text (STT) capabilities with IBM’s watsonx Orchestrate platform, enabling organizations to build voice-enabled AI agents that communicate with nuance, emotion, and rhythm across 70 languages.

    This strategic integration expands agentic AI from traditional text-based systems to voice-first interactions, offering enterprises the ability to replace robotic call flows with human-like conversations. The partnership addresses key enterprise needs for security and compliance, including PCI compliance for payment processing and HIPAA-compliant data handling through Zero Retention Mode.

    Industry applications span government services, banking, healthcare, insurance, and utilities, where AI phone agents can now converse in multiple languages with regional accents. Internal use cases include helping employees navigate legacy systems and retrieve complex compliance documentation via simple voice commands.

    ElevenLabs has achieved $330 million in annual recurring revenue (2025) and a valuation of $11 billion following a $500 million Series D funding round in February 2026. The company’s voice library contains over 10,000 voices.

    Nick Holda, Vice President of AI Technology Partnerships at IBM, stated: “We’re bringing a voice to AI Agents in the enterprise. As clients increasingly deploy agentic AI that interacts with their customers and employees, they want these experiences to feel intuitive, responsive and accessible.”

    Mati Staniszewski, Co-Founder of ElevenLabs, added: “AI agents are becoming central to everyday work, and voice is where AI either earns trust or loses it.”

    The collaboration underscores a shift toward human-centered AI interfaces that adapt to natural speaking habits, moving beyond rigid call flows and towards empathetic, efficient digital ecosystems that can scale globally.

  • AWS Co-Founder Matt Domo on Why Enterprise AI Pilots Fail and How to Scale

    AWS Co-Founder Matt Domo on Why Enterprise AI Pilots Fail and How to Scale

    According to a recent Forrester analysis, only 10-15% of artificial intelligence pilots successfully transition into long-term production. That means most enterprise AI investments stall before they ever deliver real impact. Matt Domo, a co-founder of Amazon Web Services (AWS) and creator of foundational Microsoft enterprise technologies, has a clear explanation: the problem is rarely the technology itself, but rather how organizations are structured around it.

    Now advising Fortune 500 companies, government agencies, and universities, Domo shared his insights with AI Magazine on moving from experimentation to enterprise-scale execution.

    Why Most AI Initiatives Fail at the Executive Level

    Domo identifies three root causes for AI failure: unclear ownership of outcomes, misaligned incentives across teams, and operating models that were not built for AI-driven decision-making. “Leaders fund pilots, but they don’t redesign how work happens. They treat them as technology projects instead of using them to change how the business operates,” he said. Without organizational alignment, AI simply gets layered onto existing processes, which looks like progress but yields no measurable results.

    Scaling AI from Pilot to Enterprise-Wide Deployment

    Scaling does not come from running more pilots; it comes from standardization. “Companies that succeed define a repeatable path from pilot to production, assign clear ownership of outcomes, and integrate AI into core workflows instead of layering it on top,” Domo explained. He warns that when every team starts from scratch, you end up with scattered experiments, not scale.

    ROI Metrics That Convince Boards

    To secure board-level investment, Domo advises focusing on metrics directly tied to financial results and strategic goals. “Boards aren’t convinced by activity. They’re convinced by measurable impact tied to the P&L,” he said. The metrics that matter include cost reduction, revenue lift, and faster decision cycles. Vague reporting like usage or engagement does not work; clear attribution of what changed, by how much, and how it ties to financial outcomes is essential.

    Avoiding AI-Washing and Misaligned Projects

    Organizations avoid AI-washing by starting with the desired outcome, not the tool. “Define the business result first, assign clear ownership of that outcome, and only then determine where AI actually improves the workflow,” Domo advised. Misalignment often happens when teams are incentivized to launch initiatives rather than deliver results. The fix is to tie every AI effort to a measurable objective and hold a single owner accountable.

    Speeding Up AI-Driven Decision-Making

    Speed in AI adoption comes from clearer ownership and fewer handoffs. In large organizations, decisions slow down due to fragmented accountability and excessive alignment steps. Domo recommends defining who owns the outcome, standardizing the inputs those decisions rely on, and reducing the number of required approvals. “AI can surface better insights, but unless the organization is structured to act on them quickly, those insights sit in dashboards,” he noted. Speed comes from aligning decision rights with the people closest to the outcome.