Rowan Curran

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.

The promise of AI in knowledge management has lingered on the fringes for years — the application of AI was just outside of possible and not quite ready for prime time. And then into the room walked our new friend, generative AI. It has turned the knowledge management world upside down.

Despite all the talk about generative AI, one largely ignored area where generative AI will have a disruptive impact is enterprise analytics, specifically business intelligence. Learn six areas where vendor capabilities are emerging.

Explore these emerging use cases where generative AI might help — but not replace — customer experience professionals.

There may be a revolution coming on the heels of generative AI — but tech leaders should maintain a realistic perspective and understand its potential and limitations.

Plenty of people had fun with ChatGPT when it released, but I’m not sure any industry had more fun than cybersecurity. When first released, it turned out that ChatGPT could write code, convert code from one programming language to another, and write malware. Sure, the coherent nonsense problem persisted, but overall, it produced solid stuff. […]

Generative AI can make doing a lot of things easier, including the wrong things. Learn the risks of AI-generated content and how to avoid them.

Tools such as DALL-E Mini and Stable Diffusion are proving immensely useful for generating visual content. But there are some limitations.

It’s a hazy Saturday morning in Southern California when a struggling actor gets a call. “They want me to do what?” he asks his agent incredulously. “OK, tell them I’ll be there.” He spends the next 24 hours doing everything he can to stay awake, per the instructions of his agent. Finally, the time comes, […]

Artificial intelligence applications are beginning to hit their stride, delivering end-to-end experiences for customers and employees that match or exceed human capacities. To help enterprises decide which AI/machine-learning (ML) platform to invest in, Forrester evaluated vendor platforms offered by Amazon Web Services, C3 AI, Cloudera, Databricks, Dataiku, DataRobot, Google, H2O.ai, IBM, Microsoft, Palantir, RapidMiner, RStudio, […]

As artificial intelligence charges forward on many fronts, computer vision continues to be one of the most, if not the most, critical method of connecting the real and digital worlds. Computer vision is now well out of niche implementations and use cases and has mass-market appeal across industries and applications. Despite its usefulness, computer vision […]