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...

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,...

By now it should be obvious that artificial Intelligence (AI) and agents in all of their forms are on the brink of changing how finance and accounting departments operate. The basic outlines are already in place, but it’s not clear how or how rapidly day-to-day operations will evolve, as well as (by definition) what surprises are in store.

If you’ve been around for more than a few years, watching some emerging trend in information technology can seem like coming full circle. Something new sounds a whole lot like something old. This may be true from one perspective, like looking down, but I’ve long thought that this progression is helical. That’s because, viewed sideways, we actually are at a higher level of capability, lower cost...

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...

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...

Enterprise IT leaders are committing seven-figure budgets to "AI-powered" platforms across ITSM, security and cloud management categories. These contracts promise autonomous remediation, intelligent triage and predictive insights. The problem: most CIOs and CISOs can't articulate what the embedded AI actually does or whether it delivers measurable ROI beyond the software provider's deck.

The growing strategic importance of sales and operations planning (S&OP) reflects the confluence of two major trends evolving outside of enterprise operations. One is the ongoing disintegration of the post-World War 2 liberal trade environment that began in the early 2010s and has accelerated since. The second is the increasing sophistication and approachability of S&OP software. These...

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 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...

It’s clear that the buyer requirements for Contact-Center-as-a-Service (CCaaS) products have moved well beyond traditional notions of “cloud” or “omnichannel” and now focus on how well software providers can make artificial intelligence (AI) workflows real. The differentiation among CCaaS platforms is not so much about how well interactions are handled, but how the platforms embed within complex...

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...

Since Alteryx was acquired by Clearlake Capital and Insight Partners in March 2024, the company has taken the opportunity to revamp its product portfolio and refresh its executive team. Alteryx has always offered a combination of functionality that spans the data and analytics lifecycle, including data integration, data preparation and data quality, as well as the development of analytics...

The term “sovereign AI and data” became increasingly prevalent in recent years, initially driven by cloud infrastructure providers responding to the need to support regional regulations with sovereign cloud offerings. However, the use of sovereign cloud infrastructure is not required to deliver compliance with data sovereignty regulations. In fact, our research indicates that having the autonomy...

I have been using the term “data pantry” (somewhat tongue in cheek) to describe a curated, governed and readily accessible collection of enterprise data that business users can draw on to support the use of core business software. This includes ERP, CRM and supply chain management software, as well as essential business processes such as analytics, forecasting and planning. This form of data...

I previously stated that too many enterprises allow the IT department to be wholly responsible for data and analytics, with the risk that strategies become divorced from business objectives and KPIs. I also stated that a pragmatic approach to organizing and operating data, analytics and artificial intelligence (AI) initiatives is essential to treating data as a business discipline. There are...

Digitally transforming finance operations has been a priority since 2020. Especially with the application of artificial intelligence in all its forms, software can streamline purchasing and the procure-to-pay (P2P) cycle, shorten process times, reduce unnecessary costs, provide greater visibility into cash flows, increase control and improve results. The application of AI will increasingly...

Prophix One is a finance and accounting platform that provides enterprises a single point of entry and common authentication for financial applications. It uses in-memory cube technology to enable enterprises to handle complex planning tasks built on very large datasets to eliminate barriers to integrated business planning. The platform and its dedicated data store support the use of artificial...

I previously described data intelligence as fundamental to providing data analysts and business users with governed self-service access to data across an enterprise by delivering information about how data is produced and consumed across the organization. Data intelligence relies on a combination of technical and business metadata and functionality for knowledge graph, data inventory, data...

The reason why artificial intelligence (AI) has been the focus of endless keynote speeches, product roadmap announcements by ERP providers and development efforts across all categories of business software is that AI-embedded systems promise (and will ultimately deliver) software that more closely molds its operation to the needs of a specific enterprise and individual user. The technology is...