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.

Adopting and implementing digital automation technologies, including artificial intelligence (AI) models such as ChatGPT, robotic process automation (RPA), and other emerging AI technologies, will revolutionize many industries and business models. It is forecasted that the rise of AI will impact a wide range of job functions and roles. White-collar positions such as administrative, customer service, and back-office roles will all be impacted by AI-fueled digital automation. The adoption of digital workers is currently positioned in the early adopter phase of the product lifecycle.1 AI-driven digital workers are expected to substantially alter many white-collar tasks, including finance, customer support, human resources, sales, and marketing.42 A study from Oxford University and Deloitte predicts AI is a significant risk to the white-collar workforce.

Are you scared yet, human? Artificial intelligence (AI) has proliferated with transformative effects in recent years, in sectors from autonomous vehicles to personalised shopping. But the latest deployment of AI to generate content such as text, images or audio has caused quite a stir. ChatGPT, a particularly superior language model, even passed the US medical speciality exam. That's not to say there haven't been some bloopers.

Business software makers in financial management, design and other areas are rolling out generative artificial intelligence tools that pack troves of industry-specific data into customized applications, aiming for an edge in an already crowded market. By leveraging data gathered from specific business functions--in some cases stockpiled from decades of commercial use--software firms can offer AI tools fine-tuned for distinct applications, industry analysts said. They can also keep underlying algorithms free of extraneous data scraped online from unknown sources, which can produce unreliable results, they said.

March 8, 2023It's no secret that companies that integrate artificial intelligence (AI) into their business practices have a leg up when it comes to understanding customer needs and developing new products. For insurers, using AI could help deliver services seamlessly while breaking down silos internally and externally. McKinsey spoke with Violet Chung, a partner in the Hong Kong office, to understand more about what it takes for insurers to adopt AI into their business models and the benefits that come with adoption. McKinsey: What are the latest trends with AI that insurers should be aware of? Violet Chung: The latest McKinsey Global Survey on AI found that AI adoption continues to grow and deliver significant benefits to businesses: 50 percent of respondents reported using AI in at least one business function across their organizations.

Every sector and business function is being transformed by AI, which has raised interest in AI, its subfields, and allied topics like machine learning and data science. A recent McKinsey survey found that 56% of companies use AI in at least one business function. First, you need to understand how AI can benefit your company and what applications or use cases are possible to be integrated. This article highlights 15 ( a bonus) of the most interesting AI and ML use cases for Finance in 2023, divided into 5 groups. Those use cases give you an idea of what is possible in finance and where to use AI and ML.

Digital transformation (DX) is not just a differentiator but a matter of survival in the modern business world. And while a maelstrom of technologies, including cloud computing, mobility, IoT, and AR-VR, are delivering value and creating better experiences for customers, studies show that Artificial Intelligence (AI) will play the most crucial role in driving businesses through DX. That said, while leading tech companies such as Amazon, Netflix, and Google use AI and Machine Learning (ML) at scale in their core business processes, small and medium-sized enterprises often struggle to expand their machine learning projects beyond a small pilot scope. In a Deloitte survey involving 2,875 executives from 11 top economies, 79% of leading digital transformers (as against 49% of the starters) stated that their AI initiatives are essential to their market competitiveness. As a catalyst for digital transformation, AI helps companies become more data-driven, accelerate innovation, strengthen decision-making and increase productivity with better resource utilization in crucial business functions and domains.

In the grand scheme of things, artificial intelligence (AI) is still in the very early stages of adoption by most organizations. However, most leaders are quite excited to implement AI into the company's business functions to start realizing its extraordinary benefits. While we have no way of knowing all the ways artificial intelligence and machine learning will ultimately impact business functions, here are 10 business functions that are ready to use artificial intelligence. Not only can AI help to develop marketing strategies, but it's also instrumental in executing them. Already AI sorts customers according to interest or demographic, can target ads to them based on browsing history, powers recommendation engines, and is a critical tool to give customers what they want exactly when they want it.

Your next job application may be screened by a bot. Companies are already testing and using AI in recruitment. Advanced decision making recruitment engines are helping make hiring easy, fast and transparent. Digitization across business functions accelerated during the pandemic and the shift to a more digitized and automated workplace continues in the post pandemic world. AI-powered recruiting tools can streamline the hiring process, from resume screening to onboarding candidates.

Talent shortages are not an impediment to AI adoption, a Gartner survey of almost 700 business leaders found. More than seven in ten executives reported they currently have or can source the necessary AI talent. Companies are deploying AI strategically, to support decision-making and automation across a broad array of business functions, rather than just tactically within tech units. Four in five respondents believe that AI-powered automation can be applied to "any business decision." Demonstrating the effectiveness and the value of AI remains a challenge, despite widespread adoption.