All posts tagged: enterprise

Redefining enterprise intelligence with autonomous AI

Redefining enterprise intelligence with autonomous AI

Published by Miika Tikkanen

The shift from AI as a tool to AI as an operating model—what we call the “agentic shift” in this report—demands something more fundamental than better models or faster infrastructure. It requires connecting people, processes, and data in real time, along with the governance and control to act on that intelligence reliably. This means rethinking both architecture and operating models simultaneously. First, rebuilding data infrastructure for accessibility rather than volume. Second, replacing fixed tech stacks with composable architectures that can evolve as models and tools change. And, lastly, resolving questions of AI sovereignty, including where intelligence runs, who controls it, and how it operates across organizational and jurisdictional boundaries. Key findings include the following: Enterprise AI’s scaling problem is structural. Process-first companies are pulling ahead. Global AI spending is rising sharply and model capabilities are advancing faster than most organizations can integrate them. Yet the majority of enterprises are still not growing revenue through AI or fundamentally rethinking how they operate. The companies generating sustained returns share a common discipline. They treat process redesign as …

Scaling agentic AI pilots across the enterprise

Scaling agentic AI pilots across the enterprise

Published by Miika Tikkanen

That shift requires organizations to treat agentic AI as a cohesive system. Agents need access to the data, knowledge, and context required to make effective decisions, as well as connections to back-end systems if they are expected to take action. Fragmented information can undermine those capabilities: “The efficacy of these AI agents is purely a function of the context, the knowledge, and the data they can ingest and use,” Chandra says. The organizational implications are equally noteworthy. Scaling agents can create a new form of fragmentation if teams build isolated systems that don’t connect with one another, while governance, privacy, security, and change management become more important as agents take on more consequential work. Chandra argues that AI agents should ultimately be held to the same standards as human workers, with organizations thinking of their workforce as a combination of humans and AI agents. Looking ahead, that connected approach could enable agents to work proactively and even communicate with other agents to resolve customer needs. For organizations making the transition from pilots to scale, the …

Forward-deployed engineering is how enterprise AI learns

Forward-deployed engineering is how enterprise AI learns

Published by Miika Tikkanen

Presented by Zeta Every forward-deployed engineering (FDE) pitch sounds identical for the first ten minutes: an engineer embedded on-site, a workflow encoded within weeks, a demo that finally works on the customer’s real data. What differs is what happens in the following months, and most vendors will not tell you until you ask directly. FDE has become one of enterprise AI’s most consequential operating models. Vendors are building entire go-to-market motions around engineers who embed with customers, wire products into operating environments, and make the demo real. Investors often read FDE headcount as a growth signal and buyers read it as a promise of speed. Neither tells you whether the work is becoming a product advantage or simply accumulating as delivery labor. The test is simple: after an FDE engagement, does the next customer start with more product and fewer unknowns — or just a new services team? FDE is not one thing. At its weakest, it papers over a product that cannot yet stand on its own, translating by hand what the software should …

Enterprise AI agents are only as reliable as the messiest documents behind them

Enterprise AI agents are only as reliable as the messiest documents behind them

Published by Miika Tikkanen

Enterprise AI has largely been built around context engineering. Teams connect enterprise systems, generate chunks and embeddings, build retrieval pipelines, and assemble the context needed by individual AI applications. While this approach works well for isolated assistants and copilots, it treats enterprise knowledge as application-specific context rather than a shared enterprise asset. As organizations deploy more AI applications and agents, this model begins to break down. Different teams process the same documents, maintain separate embeddings and indexes, and create inconsistent representations of the same business knowledge. The challenge is no longer simply providing context to AI systems — it is managing enterprise knowledge itself. Why building context is not enough for enterprise AI The common approach to enterprise AI today is to build context for individual applications. Teams connect enterprise systems, process the required information, generate retrieval representations such as chunks and embeddings, and assemble the context an agent needs at runtime. While this works for a single application, it does not manage enterprise knowledge as a shared enterprise asset. As organizations deploy more AI …

One interface isn’t enough for enterprise AI

One interface isn’t enough for enterprise AI

Published by Miika Tikkanen

Presented by Oracle NetSuite Every major technology transition produces a set of assumptions about where the market is headed. The assumptions are often directionally correct, but they tend to underestimate the degree to which organizations adapt new technologies to their own circumstances. AI is following a similar trajectory. Many current discussions about enterprise AI assume a future in which employees interact with business systems through a common interface. The details vary depending on the prediction, but the destination often looks similar: a conversational system that becomes the primary way people access information, complete tasks, and interact with software. The history of enterprise technology suggests a more complicated outcome. Organizations rarely adopt new capabilities uniformly because different parts of the business operate under different constraints. A finance team responsible for reporting accuracy, controls, and approvals approaches technology differently than an analytics group exploring operational data. Both groups have different requirements than a customer service organization focused on response times and case resolution. Even when there is broad agreement that a technology is valuable, the path to …

Box survey: Why enterprise AI leaders are outperforming their peers

Box survey: Why enterprise AI leaders are outperforming their peers

Published by Miika Tikkanen

Presented by Box Content access, governance, and platform flexibility are emerging as the dividing lines between AI leaders and laggards, according to the new State of AI in the enterprise report from Box, which surveyed 1,640 IT decision makers across the US, UK, France, and Japan. One of the report’s major findings is the speed of the shift: the combined share of organizations describing themselves as advanced or leading edge soared from 8% to 64% just over the past year, while the share calling themselves early stage or not yet started collapsed from 53% to just 9%. Eighty percent of organizations reported a notable return on their AI investment, defined in the survey as an improvement of at least 10%, and more than half saw measurable business impact within six months of getting a project approved. The swing is largely due to how enterprises are now organizing their AI use rather than to any single technical breakthrough, says Olivia Nottebohm, COO of Box. “We’ve moved from standalone experimentation that lived at the individual level into …

2022 Pacific Division Dewey Lecture: Philosophy as Personal Quest and Collective Enterprise

2022 Pacific Division Dewey Lecture: Philosophy as Personal Quest and Collective Enterprise

Published by Jenni Sidey

Below is the audio recording of Margaret Gilbert’s John Dewey Lecture, “Philosophy as Personal Quest and Collective Enterprise,” given at the 2022 Pacific Division Meeting. The full text is available on the APA website (member sign-in is required) as well as on JSTOR.  The audio of the lecture is available here: “Philosophy as Personal Quest and Collective Enterprise” by Margaret Gilbert Margaret Gilbert is Distinguished Professor of Philosophy Emerita at the University of California, Irvine, where she was the Founding Abraham I. Melden Chair in Moral Philosophy from 2006 to 2025. Prior to joining the faculty at UCI, she taught at the University of Manchester (UK) and the University of Connecticut, where she is professor emerita. She has a BA in classics and philosophy from Cambridge University, as well as a BPhil with distinction and a PhD in philosophy from Oxford University. Her research interests are in social phenomena such as acting together, collective beliefs, social conventions, agreements and promises, and related areas of moral and political philosophy and philosophy of law, including rights theory …

Learning to lead in a hybrid human-AI enterprise

Learning to lead in a hybrid human-AI enterprise

Published by Miika Tikkanen

Human employees have more time to focus on work “that requires a creative and imaginative mind and cross-functional collaboration, leveraging diverse ideas and thoughts to problem-solve,” says Jayaswal. The AI agent, meanwhile, handles rote administrative tasks like sorting timesheets or helping employees navigate policies and take actions in the flow of work.  When reallocating employee responsibilities, though, it is imperative that humans remain in the loop, Jayaswal caveats. When agentic AI is incorporated into enterprise technology, it must work with sensitive and personal data and therefore needs even more stringent guardrails and constraints than consumer applications. “When you expose an AI agent to organizational data, when you integrate it into multiple enterprise systems, then pathways around the AI agent become extremely important,” he says. “It’s an evolving space that leadership needs to have front-of-mind.” Governance should include robust data privacy rules and the establishment of governance layers, such as an AI council, he suggests.   At a fundamental level, the adoption of AI agents will force a re-evaluation of human roles, believes Jayaswal. Rather than employees …

Work IQ is Microsoft’s big bet on agent-first enterprise IT, and I have questions

Work IQ is Microsoft’s big bet on agent-first enterprise IT, and I have questions

Published by Miika Tikkanen

Follow ZDNET: Add us as a preferred source on Google. ZDNET’s key takeaways Microsoft is building Work IQ for agent-first enterprises. Agents can discover data structures dynamically at runtime. The biggest concerns are cost, governance, and exposure. Work IQ is a new offering from Microsoft that showcases two quintessential Microsoft skills: the ability to solve complex technical and infrastructure problems with an elegantly sophisticated solution, and the ability to make something almost impossible to explain. But I’m going to try. Work IQ is the result of Microsoft completely redesigning how enterprise software works. Yeah, it’s that big.  If you think about how the enterprise software ecosystem has worked for the past few decades, it’s consisted of applications and data (together, let’s call them “solutions”) that worked on their own, or passed data between one another. Also: Enterprise AI agents are multiplying fast, and Microsoft wants full control of them Those solutions were often linked either through data transfer protocols or APIs. But no matter what, some human had to code the connection between any two solutions. …

Why prompt debt, retrieval debt, and evaluation debt are quietly reshaping enterprise AI risk

Why prompt debt, retrieval debt, and evaluation debt are quietly reshaping enterprise AI risk

Published by Miika Tikkanen

Over the past two decades, technical debt meant outdated architecture, messy code, and poorly maintained documentation. That definition is no longer sufficient in the AI era, where failure modes are more subtle and often non-linear. AI systems are introducing new layers of technical debt that live across prompts, models, and data dependencies — making these layers less visible, harder to measure, and often more dangerous than traditional debt. A crisis hiding in plain sight The complexities of AI systems and their associated failures have been well documented. A 2025 MIT study found that 95% of AI projects fail to reach production or deliver value. A similar study by S&P Global Market Intelligence found that 42% of businesses scrapped multiple AI initiatives in 2025 — a sharp increase from 17% the previous year. Various reasons are cited for these failures, but most of them point to poorly designed and implemented systems that are complex to manage and have multiple hard-to-monitor failure points, leading to a rapid accumulation of AI debt.  Traditional technical debt was localized to …