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AI, like analytics, must lead to action. Too often, in both cases, too much of the exercise is left to the reader. We have tools to provide sophisticated analyses, including AI platforms that can be used to predict many types of behavior, but we fall short in helping the workforce know what to do with that information. Some examples are more obvious, such as fraud detection. If a transaction is predicted to be fraudulent, the transaction should be blocked. But even this example is not as cut and dry as you might think. I’m sure many of you have been frustrated standing at a hotel check-in desk or at a retail counter attempting to make a purchase when your credit card transaction has been denied. What is the appropriate action or set of actions that should be taken?

Agents and “agentic AI” are all the rage now, eclipsing last year’s focus on artificial intelligence (AI) and generative AI (GenAI). They are a way to automate work almost effortlessly so that repetitive and boring tasks get done with the least amount of effort and perhaps, more consistently. In business software, a broad range of software providers are claiming agents to be a panacea that can improve performance and lower costs. They are alluring, with an almost unlimited number of potential use cases. Agents are an important evolutionary step in the design of business software, similar to the transition from procedural programming to event-driven programming that accelerated in the late 1980s. That paradigm shift enabled business software to be more flexible and responsive in replicating how work is performed. Adding agents to the considerable body of well-developed business applications will take the capabilities of these applications to the next level.

Conversational automation leverages artificial intelligence (AI)-powered agents, chatbots and virtual assistants to automate both customer interactions and internal processes. These systems understand natural language, sentiment and intent, generating relevant responses and executing actions based on user input. The software provider landscape is analyzed in the ISG Buyers Guide for Conversational Automation.

Payroll is one of the most universal touchpoints within any organization, impacting every employee on a regular basis. It’s the one thing every employee notices if it does not work perfectly. Yet, despite its far-reaching presence, payroll often operates quietly in the background, treated as an administrative function focused solely on ensuring people are paid accurately and on time. This perception, while understandable, drastically undervalues what payroll can offer.

Based on the number of invitations I have received to speak on the topic, usage pricing (also referred to as consumption pricing) is hot. But as I have remarked elsewhere, it is not new. I recall a conversation with a customer over 30 years ago when I was involved with analytic software who asked, “Why can’t we just pay for the software we use?” And this of course makes sense: only pay for what you use. In fact, I assert that by 2027, over one-half of all enterprises will deploy a mixed revenue model that includes subscriptions and usage pricing in addition to one-time sales as enterprises make adjustments to remain competitive.

Enterprise Resource Planning (ERP) systems are comprehensive software platforms designed to integrate and manage all the core processes of an enterprise while recording transactions and their financial consequences to support the accounting and finance functions. ISG Software Research recently completed our Buyers Guide™ for ERP systems, designed to help enterprises that are replacing their existing ERP software to make the best choice, both in terms of the product’s performance as well as the customer experience. ERP systems are central to the operations of an enterprise, streamlining cross-functional operations using workflows and consolidating data created by departments such as finance, human resources, supply chain and customer relationship management into a unified database. This integration facilitates the accessibility of real-time data, enhancing situational awareness for executives and managers while boosting overall efficiency and productivity.

The manufacturing industry is undergoing a period of significant transformation, driven by a confluence of technological advancements, economic shifts and evolving consumer expectations.

The industry is being impacted in areas such as smart manufacturing, supply chain optimization, aftermarket services and sustainability. Manufacturers are leveraging technologies such as IoT, AI, ML, digital twins, AR, VR and cloud to optimize production processes, improve quality and enhance efficiency. They are also implementing digital solutions to streamline supply chain operations, reduce costs and improve visibility. Aftermarket services are being placed at the forefront and integrated into core business processes to drive customer satisfaction and revenue.

Advancements in AI applications are redefining the landscape of business service automation in the U.S., disrupting use cases and codifying service knowledge using generative AI (GenAI) technologies. These innovations, powered by large language models (LLMs), vision-language models (VLMs), small language models (SLMs), retrieval-augmented generation (RAG) and other appropriate finetuning techniques, are revolutionizing how organizations automate customer service, IT support and business operations.

The transformation of business designs and operating models is a fundamental necessity for North American insurance carriers aiming to sustain competitiveness and effectively navigate the complexities of contemporary market conditions. This imperative arises from several critical factors, including customers’ evolving expectations, volatile geoeconomic circumstances and the increasing frequency and severity of property and life losses primarily attributed to climate change.

The healthcare payer landscape is undergoing a significant transformation, driven by a shift toward value-based care (VBC), the integration of advanced analytics and AI, and a commitment to improved member engagement and interoperability. These trends reflect a broader movement toward patient-centered models that prioritize quality  outcomes and enhance the healthcare experience for all stakeholders.

The U.S. data analytics and AI market is undergoing transformative changes and a dynamic evolution driven by technological advancements, regulatory changes and enterprise demands. The market is also experiencing an accelerating need for enterprises to integrate advanced technologies, especially AI, into their business strategies.

The U.S. data analytics and AI market is undergoing transformative changes and a dynamic evolution driven by technological advancements, regulatory changes and enterprise demands. The market is also experiencing an accelerating need for enterprises to integrate advanced technologies, especially AI, into their business strategies.

Strategic consolidation and an emphasis on improving technology capabilities are driving a noticeable increase in M&A activity in North America’s telecom, media and entertainment (TME) industries. Compared to previous years, the number of deals increased significantly in 2023 and 2024 as private equity groups became more aggressive in purchasing media and technology businesses. As businesses look to safeguard their digital infrastructures in response to growing customer demands for individualized content and services, there is a noticeable movement toward cloud services and  cybersecurity. Furthermore, as businesses manage compliance while seeking expansion through mergers and  acquisitions, regulatory scrutiny continues to influence deal-making tactics. As the economy stabilizes amidst the lowering of interest rates and persistent inflation, there is cautious optimism about future M&A activity.

The North American O&G (oil and gas) services industry is undergoing significant transformation, driven by various factors, including technological advancements, shifting energy demands, environmental regulations and geopolitical events. This dynamic landscape is characterized by a growing focus on efficiency, sustainability and innovations as service providers seek to adapt to the industry’s evolving needs.

The power and utilities industry is witnessing a transition from traditional energy sources to cleaner alternatives such as wind and solar energy, leading to an increase in new asset types to deploy, connect and manage. Additionally, there has been a rise in distributed energy resources (DERs), leading to disruptions in energy production patterns. Moreover, as the industry increasingly adds renewable energy sources such as solar and wind to its energy mix, their integration into the grid continues to be a challenge. Global supply shortage due to the war in Ukraine has led to an increase in natural gas prices, making the purchase or production of power expensive.

The U.S. public sector, particularly state, local and educational (SLED) agencies, is finally witnessing the anticipated surge in cloud services adoption. This shift is transforming SLED IT organizations, spending and department operations, intensifying competition among cloud service providers and increasing the demand for FinOps capabilities.

Until last year, we saw public cloud services grow mainly due to innovation, faster time to market and improved customer experience. But, in the last four quarters, we saw AI technologies driving the growth of cloud services in the U.S. As enterprises increasingly recognize the transformative potential of AI, they are turning to cloud platforms to access the necessary computing power, storage and tools to develop and deploy AI applications. The ability to scale resources on-demand, combined with the wide range of AI-specific services offered by cloud providers, has made cloud computing an attractive option for enterprises seeking to leverage AI. This increased demand for cloud services, fueled by AI adoption, drives significant growth in the U.S. cloud services market, benefiting both cloud infrastructure providers and service providers.

The rapid evolution of cloud infrastructure marks a significant shift in how organizations approach their digital transformation initiatives. The demand for cloud technologies has surged as businesses strive to combine the security and control of on-premises infrastructure with the scalability, innovation and flexibility offered by cloud platforms. The shift is catalyzed by fast-paced technological innovation and driven by strategic business demands requiring heightened operational agility and efficiency. Over the last few years, the urgent need to meet evolving strategic goals has driven the rapid adoption of cloud-native technologies within multicloud environments. It is revolutionizing how organizations design, deploy and manage their applications.

As quantum computing advances at an unprecedented pace, the concept of Q-Day—a day when quantum computers can fundamentally undermine our current encryption methods—has entered discussions among cybersecurity professionals and business leaders alike. While there is no definitive date set for Q-Day, we are approaching a critical juncture where traditional cryptographic techniques may no longer suffice to protect sensitive data, digital communications and transactions. This impending shift not only poses significant risks for individuals but also presents a high-stakes event that every enterprise must anticipate and prepare for; inadequate preparation could lead to substantial data breaches, compromised systems and irrevocable damage to customer trust and organizational reputation.

The degree to which data platforms are critical to efficient business operations cannot be overstated. Without data platforms, enterprises would be reliant on a combination of paper records, time-consuming manual processes and huge libraries of physical files to record, process and store business information. The extent to which that is unthinkable highlights the level at which today’s enterprises and society as a whole rely on data platforms. The core persistence, management, processing and query requirements of data platforms have been well established since the emergence of the database market in the early 1970s. The data platforms market continues to evolve to keep pace with emerging capabilities and requirements. Two of the most significant trends driving the data platforms sector in recent years are cloud computing and artificial intelligence.