Rowan Curran

Loads of news came out of a hot Google Cloud Next 2025 in Las Vegas. The most notable announcements? Sovereign AI solutions on-prem, developer innovations that meet timely needs, very applicable multimodality for content and CX, and new elements for building the enterprise agentic AI stack.

San Jose was abuzz with excitement as AI enthusiasts gathered for the 2025 NVIDIA GTC AI conference. Find out what the key announcements were and what they could mean for your technology roadmap in this review.

To achieve seamless cross-platform functionality, AI agents need standardized frameworks addressing not just technical compatibility but also security, governance, adaptability, and intent. Read this post to learn the nine dimensions of agentic AI interoperability. 

As vendors come out of the woodwork announcing their “agentic AI” innovations, we explore what the term truly encompasses and what is hype.

In the ever-evolving landscape of artificial intelligence, businesses are moving beyond traditional AI agents toward something far more transformative: agentic AI.

For more than a decade, Forrester has been committed to researching AI and ML technologies and platforms. During my 13-year tenure at Forrester, I had the privilege of working alongside our talented AI analysts. Together, we have continuously refined our market definitions and research focus to stay aligned with emerging tech trends and business needs. […]

AWS may have been caught off guard by the sudden rise of generative AI, but now it’s coming for the competition from every possible angle. Get our key takeaways from AWS re:Invent 2024.

Sophisticated enterprise knowledge applications require advanced RAG architectures. Use these three concepts to build smarter RAG apps.

Enterprises experimenting with generative AI (genAI) are prioritizing use cases that enable internal and external users to better retrieve information they need, be it data, how-to pages, corporate policies, or return policies. Retrieval-augmented generation (RAG) is now the most common underlying approach for building this type of large language model (LLM)-based enterprise knowledge-retrieval application. What […]

In our inaugural evaluation, The Forrester Wave™: AI Foundation Models For Language, Q2 2024, we break down large language model (LLM) model offering, strategy, and market presence across 21 criteria for 10 providers. Get a preview of the report including four things you should be looking for in a model here.

It’s easy to trust generative AI implementations because of how human they feel. But when it comes to security, there are still some fundamental challenges to overcome with genAI. Learn more about those challenges in this post.

The market for AI foundation models is still in its infancy. While enterprises still rely mostly on pretrained large language models for AI needs, the market is evolving and changing quickly. Tech leaders must understand how to navigate it.

Google recently held its Google Cloud Next conference four months early to unveil new AI offerings and upstage its rivals. Get a detailed review of all of the announcements at the event in this post.

Recently, I had the honor of presenting Forrester’s predictions for 2024 to over a thousand tech and business leaders. There were, unsurprisingly, a ton of questions about generative AI. I’d like to answer some of the more technical questions that we received.

Learn how leveraging synthetic data can help with the critical time to realize AIOps value, such as autoremediation capabilities and enhanced decision-making.

A year after the release of ChatGPT, we take a look back at where it all started, where things stand today, and where we can expect generative AI to go in 2024 and beyond.

AI was center stage at Google I/O 2023 and much of it was generative AI (GenAI). Get a detailed analysis of the event including why enterprises need to focus on the launch of PaLM 2 and Vertex AI.

As regulators, courts, and even the White House scrutinize generative AI ethics, learn five things your AI governance programs should be addressing today.

Tools leveraging ML models in dynamic environments must be continuously fed new data in order to adapt to changing conditions and goals. They need to learn as much as possible about their new environments before being fully able and safe to provide service and ultimately deliver value. Synthetic data can help address these and other AI/ML challenges.