Insights

Our views on AI strategies, best practices, and real-world applications.

Can GPT-4 Outsmart Wall Street Stock Pickers?

I provided a large language model with last year's market outlook reports and asked it to generate aggressive growth portfolios. Next, I backtested the portfolios against historical market data. Here are the results.

Market Segmentation and Lead Enrichment with LLMs

Market segmentation is an essential part of any go-to-market strategy. Segmenting a large list of prospects can be very challenging. Fortunately, it is precisely the kind of work that large language models excel at, but it does take a few tricks.

AI Takers vs. Shapers: Harnessing Generative AI for Competitive Edge

With AI assistants popping up in every tool and service, it's easy to be an AI taker. However, using generally available tools does not build a lasting competitive advantage. One must learn to shape AI to make a difference.

Automating Workflows with Multi-modal AI and PyAQ

If you were impressed by the first generation of large language models, wait till you interact with a multi-modal one, such as Gemini, GPT4V, or LlaVA. In addition to text, these models can process and understand images, videos, and speech, which presents new opportunities for workflow automation.

Announcing AnyQuest PyAQ

Today, December 6, 2023, we announce the availability of AnyQuest PyAQ, an open-source low-code platform for cognitive applications.

Generative AI Maturity Model

According to OpenAI, over two million developers and more than 92% of Fortune 500 organizations are experimenting with or deploying generative AI. To help them assess their progress, we created a simple maturity model.

Cognitive Applications and Semantic Brokers

There are many practical examples of cognitive applications: intelligent workflows, agents, and chatbots. In this article, we describe features shared by all cognitive applications. We also introduce the notion of a semantic broker, a platform that accelerates their development and deployment.

Why Every Company Needs a Generative AI Center of Excellence

Generative AI solutions enrich user prompts with proprietary data, and grass-roots adoption of generative AI poses significant security, privacy, and compliance challenges. To manage these risks, companies must create Generative AI Centers of Excellence.

Security Considerations for Generative AI Applications

Information security and risk management are the top concerns for companies deploying generative AI solutions. Most companies focus on risks presented by AI models while paying insufficient attention to other solution elements. In this post, we paint the whole picture.

Democratization of Business AI

Training, deploying, and managing AI models used to be prohibitively expensive. Large foundation models changed everything. Suddenly, every business application can be AI-enabled with relatively little effort. But there is a catch.

Semantic Brokers - A New Class of AI Software

We are witnessing a historic moment in enterprise IT – the emergence of semantic brokers, a new category of business-critical enterprise software responsible for AI enablement and risk management.

Top 10 Business Use Cases for Generative AI

We present a list of the top 10 business use cases that were recently considered too difficult or impossible and can now be enabled with generative AI.

Generative AI - What It Is and Why It Matters

Generative AI is very different from other types of AI. It will transform every sphere of human activity. Fortunately, experiments are inexpensive and can be plentiful. Business and tech leaders would be wise to start now.