Nuances of Build-or-Buy Decisions

The intricacies of the time-honored question of whether to build or buy take on new dimensions when it comes to enterprise AI. Organizations must not only account for traditional considerations like cost, time-to-market, and proof of concepts but also for those that are unique to advanced machine learning and language model deployments.

Vibe Coding: When Intent Becomes the Interface

For the last 40 years, the history of enterprise software has been a history of translation. Business leaders had intent; engineers had the syntax. The gap between them was bridged by requirement documents, product managers, Jira tickets, and the inevitable friction of misinterpretation.

The AI Stack: What Decision Makers Need to Know

Organizations are at vastly different stages with respect to their AI stacks, which is not surprising given the rapid pace of advancement in this field within the past few years. Those that are just getting into AI may have experimented but not gone beyond the parameters of their existing enterprise stack. Others may have elements of the AI stack in place but need to expand it in order to build out AI applications.

AI Across Markets and Industries

With all the noise around generative AI (GenAI) since the advent of ChatGPT, has the signal gotten lost? Are we missing the one "killer app" that will make GenAI indispensable? What is more likely is that multiple killer apps will surface across many markets and industries.

The Rise of Agentic AI: Why Your AI Agent Is Clueless

Enterprises are rushing to adopt so-called "agentic AI," systems that promise to not just answer questions, but also take actions, automate tasks, and drive decisions. In theory, these agents can draft emails, update records, generate product specs, or flag anomalies without human involvement. In practice? Most deployments don't make it past the demo.

The Power of RAG

Think back a few years to OpenAI's initial introduction of its generative AI (GenAI) chatbot—the excitement, the promise of marvelous opportunities, the sense of a change for the better. It wasn't long before the euphoria of GenAI wore off and people started noticing that large language models (LLMs) could produce absolutely fabulous results, but they could also deliver flawed information that looked plausible but was untrue.

Architecting a Modern Data Stack for AI Agents

Even if you've got a brand-new business model based specifically on the capabilities of a language-centered AI agent, you still have to deal with time-honored challenges such as disparate and scattered data, slow response, finding necessary information, and trusting that information.

Building Enterprise-Grade AI Applications

As the C-suite continues to marvel at AI, many companies are settling into the early stages of undertaking enterprise AI adoption in targeted areas now that the "winner takes all" rush to apply AI technologies to outpace the competition is mostly gone.

AI and Data Governance: Ensuring Compliance, Security, and Trust

Data governance has evolved. What once meant checkbox compliance now demands fundamental organizational transformation. AI systems permeate every business function, and traditional frameworks cannot handle this reality.

The Explainable AI Imperative: Why Curation Is the Missing Architecture

You've witnessed the spectacle. Large language models (LLMs) trained on the internet's vast corpus—Reddit threads, Wikipedia edits, forum arguments, and digital detritus—demonstrating seemingly magical capabilities. Yet when enterprises attempt to deploy these systems for critical decisions, they discover an uncomfortable truth—they've built Ferraris without windshields, dashboards, or steering wheels.

OpenAI Faces Wrongful Death Lawsuit

Now, OpenAI faces its first known wrongful death lawsuit after a teen died by suicide further spurred on by ChatGPT, The New York Times reports.

Exploring the ‘SIX D’S’ Framework for Language Model Training and AI Agent Creation

The integration of AI into customer service and support is by no means a futuristic vision—it is a present-day reality. AI is reshaping how organizations engage with their customers.

AI Techniques Powering Enterprise Productivity: From Automation to Augmented Intelligence

AI stands beyond experimental status—it is a founding power for corporate productivity delivery. From automating repetitive workflows to enhancing complex decision making, AI transforms how work gets done.

AI Enhances Customer Service in Several Ways

Although the initial seeds of generative AI (GenAI) were planted in the 1960s, for the most part it didn't come into full bloom until the introduction of ChatGPT in November 2022. Before that, GenAI had all but the most nascent use in business. As the technology entered the public consciousness, it spread across industries from entertainment to manufacturing to healthcare for a wide variety of uses, including customer service.

The Rise of GenAI and LLMs

In 1950, Alan Turing suggested a test to determine if computers could mimic human intelligence well enough that an impartial observer could no longer tell the difference. We are still talking about the Turing Test almost 75 years after its inception.

The AI-Driven Transformation of Enterprise Data Architecture

The past year has been an exhilarating one with AI and more specifically, generative AI (GenAI), quickly emerging as a transformative force, reshaping how businesses will operate, innovate, and interact with customers. As AI continues to gain prominence, its impact on enterprise data architecture is becoming increasingly apparent.

Developing a Strong AI Governance Framework

The exponential advancement of AI technologies necessitates the implementation of robust governance frameworks to guide their responsible development and deployment.

Transforming Customer Service with AI and Knowledge Management

Customer service and customer support operations are no strangers to AI technologies. AI has long been embedded in the interactions between companies and their customers.

Adding Meaning to Data: Knowledge Graphs, Vector Databases, and Ontologies

This article discusses the importance of context in enterprise data for generative AI (GenAI), and, in fact, for any AI initiative.

Elevating Enterprise Search With AI, ML, NLP, and LLMS

Information retrieval, now more commonly known simply as search, is one of the earliest manifestations of using AI. Consequently, enterprise search is inseparable from AI and one of the most meaningful beneficiaries of the exponential advancements recently achieved by machine learning (ML)—the most accomplished expression of statistical AI.