From selecting the right use cases through introducing robust orchestration to reduce risks, AI adoption at an enterprise scale can't be done in a silo. To get the leapfrog benefits of AI, you need the talent, tools and expertise of services and software providers. But knowing which provider(s) to rely on, how to build the contract and what's needed to ensure the results meet your expectations takes something extra.
That's where ISG comes in. We're independent sourcing experts who know the intricacies of AI adoption and the software and IT services market.
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The success of AI adoption relies on creating robust organizational and governance structures that accelerate AI adoption while managing risks. Cognitive infrastructures incorporating hybrid cloud, security, privacy, intelligent edge, and purpose-built AI solutions are changing the IT landscape, making technology adoption more difficult. Integrating model-aware computing is crucial for optimized AI performance and responsiveness. We help you select the right use cases and build the business case to accelerate adoption.
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GenAI has dramatically changed the delivery of core services. Your current partners may excel in various areas, but they might not be invested in AI or passing those savings to you. Our specialized research and proven methodology identify the true savings potential and partners that align with your AI needs. This strategic approach ensures you collaborate with the right experts who can drive your AI initiatives to success.
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To fully realize the potential of your AI investment, ISG offers a comprehensive suite of services, including organizational change management, a strategy realization office, and robust orchestration across your AI environment. These elements ensure effective risk mitigation and governance, guiding your journey from initial implementation to sustainable, large-scale AI integration. Our approach ensures that AI becomes a mainstream component of your operations, delivering continuous value and innovation.
The market is rapidly evolving, with some use cases beginning to provide ROI in 2024 and 2025. But enterprises also face big challenges in integrating GenAI solutions into their existing workflows, ensuring data quality and managing costs. ISG is providing a full, complimentary report on the AI market current state and trends to help you make your GenAI moves count.
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Learn MoreConversational automation is one of those software segments that means something different depending on who you are or your role in an organization. According to my colleague Jeff Orr, the core of the idea is that conversational automation tools benefit from artificial intelligence (AI), allowing software agents, chatbots and virtual assistants to automate customer interactions and internal processes. This broad definition hits the mark, I think, because it identifies the core functions without putting too tight a straitjacket on the technology itself, which is developing very quickly. The software provider landscape is analyzed in the ISG Buyers Guide for Conversational Automation.
Ten years have passed since artificial intelligence (AI) first appeared in sales technology, and the results are mixed. Early tools applied rudimentary machine learning (ML) models to customer relationship management (CRM) exports, assigning win probability scores or advising on the “ideal” time to call. The mathematics was sound, the demos impressive, yet adoption faltered because little thought was given as to how sellers should use this information.
In an earlier Analyst Perspective, I discussed data democratization’s role in creating a data-driven enterprise agenda. Building a foundation of self-service data discovery, data-driven organizations provide more workers with the ability to analyze and use data. I’ve also examined how generative artificial intelligence (GenAI) could revolutionize business intelligence software by using natural language interfaces to lower the barriers to working with analytics software. Today, however, data democratization ensures that access is not limited to analytics software. Users in different roles should be able to use data through whatever applications or tools best align with their business workflow and objectives. This is the driver behind growing interest in a new category of products that enable headless BI.
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.
