How companies are freeing themselves of legacy data systems to power AI agents that deliver trusted, autonomous action. Business and technology leaders need no convincing that the time of agentic AI is here. Organizations are rapidly adopting agents, and few executives doubt the technology's potential to transform work. But many organizations find that realizing the desired return on investment (ROI) from AI hinges on having the right foundation, with inadequate infrastructure and data being major blockers . The shift from answering questions to taking actions means AI agents need data from across the enterprise, in all its structured and unstructured forms, and with the right business context. To make decisions and act in real time, agents also need frictionless access to the organization's operational systems--for example, those storing its supply chain, point-of-sale, or human resources data.

A ranking of 101 agent tasks reveals where workflows are trending and where connected intelligence is critical. Enterprise investment in AI is booming. Gartner is calling 2026 an " inflection year " for organizations to align their AI projects with strategic business objectives. As the pressure to prove ROI mounts, executives and technology leaders are looking to agentic AI to drive the measurable financial outcomes their businesses seek. A prime opportunity for AI agents exists in the tech function, where IT infrastructure costs are projected to grow two to three times by 2030, even as budgets remain unchanged, according to McKinsey . And in the last 18 months, tech teams--the engineers, developers, architects, and other practitioners who are building, deploying, and continually improving their organizations' infrastructure and applications--are clearly putting agents to work.

As companies race to adopt agentic AI to spur innovation and gain efficiency, building the right enterprise data infrastructure has become a critical component of success. In the race to adopt and show value from AI, enterprises are moving faster than ever to deploy agentic AI as copilots, assistants, and autonomous task-runners. In late 2025, nearly two-thirds of companies were experimenting with AI agents, while 88% were using AI in at least one business function, up from 78% in 2024, according to McKinsey's annual AI report . Yet, while early pilots often succeed, only one in 10 companies actually scaled their AI agents. One major issue: AI agents are only as effective as the data foundation supporting them. Experts argue that most companies are seeing delays in implementing AI, not because of shortcomings in the models, but because they lack data architectures that deliver business context to be reliably used by humans and agents.

In large-scale software development, understanding the functionality and intent behind complex codebases is critical for effective development and maintenance. While code summarization has been widely studied, existing methods primarily focus on smaller code units, such as functions, and struggle with larger code artifacts like files and packages. Additionally, current summarization models tend to emphasize low-level implementation details, often overlooking the domain and business context that are crucial for real-world applications. This paper proposes a two-step hierarchical approach for repository-level code summarization, tailored to business applications. First, smaller code units such as functions and variables are identified using syntax analysis and summarized with local LLMs. These summaries are then aggregated to generate higher-level file and package summaries. To ensure the summaries are grounded in business context, we design custom prompts that capture the intended purpose of code artifacts based on the domain and problem context of the business application. We evaluate our approach on a business support system (BSS) for the telecommunications domain, showing that syntax analysis-based hierarchical summarization improves coverage, while business-context grounding enhances the relevance of the generated summaries.

Individuals and organizations cope with an always-growing amount of data, which is heterogeneous in its contents and formats. An adequate data management process yielding data quality and control over its lifecycle is a prerequisite to getting value out of this data and minimizing inherent risks related to multiple usages. Common data governance frameworks rely on people, policies, and processes that fall short of the overwhelming complexity of data. Yet, harnessing this complexity is necessary to achieve high-quality standards. The latter will condition any downstream data usage outcome, including generative artificial intelligence trained on this data. In this paper, we report our concrete experience establishing a simple, cost-efficient framework that enables metadata-driven, agile and (semi-)automated data governance (i.e. Data Governance 4.0). We explain how we implement and use this framework to integrate 25 years of clinical study data at an enterprise scale in a fully productive environment. The framework encompasses both methodologies and technologies leveraging semantic web principles. We built a knowledge graph describing avatars of data assets in their business context, including governance principles. Multiple ontologies articulated by an enterprise upper ontology enable key governance actions such as FAIRification, lifecycle management, definition of roles and responsibilities, lineage across transformations and provenance from source systems. This metadata model is the keystone to data governance 4.0: a semi-automatised data management process that considers the business context in an agile manner to adapt governance constraints to each use case and dynamically tune it based on business changes.

We're seeing AI projects shift from hype to impact, largely because the right roles are getting involved to provide the business context that has been missing previously. Domain expertise is key; machines don't have the depth of context that people have, and people need to know the business and data well enough to understand which actions to take based on any insights or recommendations that are surfaced. When it comes to scaling AI, many leaders think they have a people problem--specifically, not enough data scientists. But not every business problem is a data science problem. Or at least, not every business challenge should be thrown at your data science team.

Recent years have seen many breakthroughs and discoveries in artificial intelligence (AI), machine learning (ML), and data science. These fields intersect so much that they have become synonymous. Unfortunately, it has caused some ambiguity. This guide aims to clarify the confusion by defining the terms and explaining how they are applied to business and science. We won't cover them in-depth; however, by the end of this article, you should be able to distinguish between these concepts.

Editor's note: This article originally appeared in Forbes. What does it mean to think of AI as a team sport? We're seeing AI projects shift from hype to impact, largely because the right roles are getting involved to provide the business context that has been missing previously. Domain expertise is key; machines don't have the depth of context that people have, and people need to know the business and data well enough to understand which actions to take based on any insights or recommendations that are surfaced. When it comes to scaling AI, many leaders think they have a people problem--specifically, not enough data scientists.

According to IBM, finding and hiring staff with the right mix of skills and experience is a painstaking process. Around 69 percent of organisations struggle to recruit quality candidates, an Accenture study showed. "Good data scientists are good at solving word problems," said Nitesh Shende, data science lead at Porter. He said most data scientists struggle to situate machine learning models in a business context. "The ability to identify where and which data science techniques to use will only come through case studies," said Shende.

It took me a long process of reflection and self-adjustment to transcend all that. In this article, I detail the course of my first five years of experience, which started with grotesque innocence and led me to a satisfying form of maturity at the end. My engineering background gave me a fairly consistent and enriched algorithmic, mathematical and computer-science-based arsenal. I was trained to model a complex problem, in whichever domain I was involved with. My interlocutors until then were my professors. I trained myself and got used against my will to associate problems with complexity. Each time I was confronted with a new problem in a different subject, I added a new layer of complexity, in a view to maximizing my gratification in the form of an evaluation grade.