Analyst Perspectives

Agentic AI is rapidly emerging as the next phase of enterprise automation, moving beyond static workflows and copilots toward systems capable of autonomous reasoning, decision-making and action. Enterprises are increasingly experimenting with AI agents to augment customer service, IT operations and business processes, yet many struggle to operationalize these systems at scale. The challenge is no...

The emergence of cloud computing has had an enormous impact on all segments of the IT industry, including data platforms. All providers of data platform products have enabled their products to be deployed in the cloud and/or consumed as cloud-hosted managed services. To date, the cloud has arguably had the largest impact on analytic data platforms, where cloud infrastructure led to the emergence...

To achieve an autonomous enterprise, a strategy alone is not sufficient. Organizations need software architectures that can support artificial intelligence (AI) at full scale within defined governance and security boundaries. This must be foundational, not an afterthought.

I have recently written about the importance of context in relation to agentic artificial intelligence, including the growing use of Model Context Protocol to enable LLMs, agents and applications to communicate with data platforms, file systems and development and productivity tools, and the importance of semantic data modeling to provide agreed definitions that reflect the meaning of entities,...

I recently explained how providers addressing the full combination of artificial intelligence and data requirements through integrated AI and data platforms are increasingly attractive as enterprises look to accelerate initiatives and reduce costs and complexity. One example of a software provider that has combined AI and data capabilities is Microsoft with its Fabric data platform. First...

The software industry has entered one of its most consequential pricing and value inflection points since the shift to cloud computing. AI is not an incremental capability layered onto existing software; it is fundamentally redefining how software should create value and, in turn, how that value must be priced. The traditional model of seat-based subscription pricing was designed for systems that...

Agentic commerce represents the next phase of digital commerce evolution, where AI agents powered by large language models (LLMs) mediate, decide and execute transactions on behalf of consumers and enterprises. The rapid adoption of generative AI platforms now reaching hundreds of millions to billions of monthly users has accelerated a fundamental shift from search-driven engagement to...

I have previously explained the critical importance of data to successful artificial intelligence (AI) initiatives, including generative and agentic AI. While enterprises have demonstrated the value of AI through small-scale initiatives, scaling these efforts has highlighted the need to coordinate AI and data programs more effectively. Providers that can address the full combination of AI and...

This shift in operating model directly reshapes how energy enterprises prioritize software investments and platform architectures.

The world’s population is projected to reach 9.7 billion by 2050, driving increased energy demand while accelerating the shift toward a more digital, diversified and technologically advanced energy system. Electrification is expanding, renewables are scaling and geopolitical pressures are reshaping supply chains. Oil and gas operators and power and utilities providers must simultaneously...

I have previously written about the impact that artificial intelligence (AI) and agentic AI are having on the requirements for data platforms. AI and data platforms are intrinsically linked, with the latter providing the underlying data persistence and data processing capabilities that support model development and training, as well as the inferencing capabilities of intelligent operational...

Healthcare organizations are increasingly investing in customer relationship management (CRM) technologies to support patient engagement, service operations and care coordination across complex healthcare ecosystems. As care delivery expands across hospitals, outpatient services, digital health platforms and virtual care channels, organizations must manage interactions across scheduling systems,...

The healthcare industry is increasingly modernizing its customer relationship management (CRM) platforms and software investments to support patient engagement, care coordination and service operations. As care delivery expands across hospitals, outpatient services, digital health and payer-provider ecosystems, managing patient relationships across fragmented systems has become more complex....

I previously wrote about the potential for generative artificial intelligence (GenAI) to change the face of analytics and facilitate data literacy and data democratization by enabling business users without specialist analytic skills to discover and analyze data. At the time of writing, GenAI-based interfaces were already being adopted by business intelligence (BI) software providers to...

As established in my foundational perspective on AI Orchestration, the defining enterprise challenge is no longer model performance but the design of a control plane that governs multi-agent systems, tool access, policy enforcement, and cross-platform interoperability.

The software economy has reached its greatest inflection point since the dawn of the Internet. Enterprise software now operates across dynamic cloud environments, with release cycles accelerating to weekly or even daily in a continuous delivery model. Software providers have evolved into platform ecosystems, seamlessly integrating applications and data while unlocking new opportunities through...

As I previously described, context is everything for generative (GenAI) and agentic AI. Enterprises moved in large numbers to adopt foundational GenAI since its emergence into mainstream consciousness during 2023. As they did so, it quickly became clear that establishing trust in GenAI output would require enterprises to augment the realistic content generated by foundation models with real-world...

The mission to enable an autonomous enterprise, as I have articulated, remains aspirational for most organizations. Few have aligned business and technology leadership, strategy and execution to support autonomous operations at any scale. A shared definition is essential. An autonomous enterprise follows an operating model in which intelligent systems can sense conditions, decide actions, execute...

For most enterprises, the problem with artificial intelligence (AI) today is not a lack of ambition. It is confusion. Organizations are overwhelmed by pilots, tools, providers and hype, and they lack a clear path to autonomy and measurable outcomes. The way forward is to cut through the noise and reframe AI not as technology experimentation with generative AI (GenAI) and agentic AI, but as a...

I have previously described the critical importance of context for enterprise adoption of generative artificial intelligence (GenAI) and agentic AI. Establishing trust in the content generated by GenAI is facilitated by grounding the models with real-world context from enterprise content and data, while AI agents designed to make context-aware decisions and take automated actions based on...