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 MoreI am struck by the blizzard of software announcements this year describing new features for CX tools that are “agentic,” meaning autonomous tools take actions without (much) human intervention. Industry conversation about agentic AI has proceeded ahead of clarifying definitions and a sense of how it fits on a continuum of rapid AI development.
The IT department of any enterprise is integral to implementing and managing the execution of its data objectives, just as the finance department is integral to implementing and managing financial objectives. Few enterprises would allow the finance department complete autonomy to define financial strategies; however, too many enterprises allow the IT department to define data strategies. Treating data as a business discipline—rather than a technical one—is a critical component of delivering competitive advantage through investment in data processing, analytics and artificial intelligence. This can be facilitated by adopting the most appropriate organizational approach, depending on the data activity.
Frontline work is being reshaped from every direction: tighter labor pools, unpredictable demand and a workforce that expects more than just a paycheck. Yet too many organizations are still treating workforce scheduling like a game of Tetris—moving blocks around and hoping they fit. It’s time to admit that smart scheduling isn’t just a nice-to-have—it’s a frontline imperative. And if the enterprise is still thinking of it as “just logistics,” it's already falling behind.
Conversational 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.
Traditional AI typically handles things like organizing information, predicting what will happen next and making processes more efficient. Generative AI can produce original content like text, images and even computer code. Agentic AI isn’t simply a tool; it’s more like a digital colleague that can independently solve problems, take action and continuously improve its performance.
AI empowers businesses to operate more efficiently, reduce costs, and improve customer experiences. Generative AI can create content, but agentic AI goes beyond that – it proactively works to achieve goals independently. Enterprises are seeing the positive impact of AI in use cases that have to do with providing excellent customer service, simplifying access to information, streamlining content creation and automating everyday tasks.
Make sure you have a well-thought-out plan, reliable information, a designated owner for the data and basic safety precautions ready to go before you begin. Assess where you are now – looking at your data, technology, team's skills and willingness to adapt – so you can identify where value is most attainable and create a straightforward plan to achieve them.
Start with the outcome you want and your data and work backwards. When choosing a solution, consider its security and compliance, how easily it integrates with what you already use, its total cost, how accurate its results are and the vendor’s reliability for the future. Try it out with a small group initially, and roll it out more widely if it goes well.
Make sure everyone understands the system’s purpose, how it will be run and that people – and not the system – are ultimately in charge. Ensure fairness, document decisions, manage access and monitor performance. Before letting AI act independently in your environment, thoroughly establish safety protocols and review procedures, then consistently monitor them to ensure continued effectiveness.
