When we published our Five Tips for Choosing a Statistical Computing Environment back in 2023, the conversation was about what to look for. If you’re evaluating how to select a statistical computing environment, start with this overview. The following post focuses on what has changed and what a modern SCE
Customer engagement is entering a new era that’s evolving very quickly. And it’s not defined by more messages or channels – it centers on intelligence, autonomy and trust. As organizations rethink how they connect with customers, a new model is emerging: engagement that predicts, learns and acts with purpose. In
Agentic AI has been heralded as a top tool for efficiently orchestrating customer engagement activities at insurance companies. It can: Automate repeatable, rule-based processes that affect customer experience (think claims processing and customer onboarding). Rapidly retrieve disparate data, analyze risks and recommend decisions. Help lower operational costs while keeping humans
A nagging question remains after asking, “What’s next after zero-click search?” How do frontier models – like OpenAI’s o3 series or Google’s Gemini 3 – learn? Garbage in, garbage out Large language models (LLMs) learn from massive amounts of text data, including billions of words, public information, and, you guessed it,
Trust has always shaped how we adopt new technology, and AI is no exception. While trust remains a real and unresolved challenge, today’s adoption gap also reflects a lack of AI literacy. As AI capabilities advance faster than most people can reasonably keep up, individuals are increasingly interacting with systems
Credit processes in banking are at a turning point. Volatile markets, new competitors, increasing regulatory requirements and an exponentially growing database are putting unprecedented pressure on traditional, largely manual and siloed processes. At the same time, customers expect fast and consistent decisions, while regulators demand transparency, traceability, and control. In
As a public sector leader, the pressure to resolve operational issues in your organization is constant, and you may be considering new or better AI models, AI agents or more broadly applied generative AI (GenAI) applications. At the same time, growing use of AI can raise issues related to accountability,
Generative AI showed up in the enterprise fast. In what felt like no time, it went from something people were experimenting with to something leaders were expected to have a plan for. Not just whether to adopt it, but how it fits into the future of their organization. Underneath all
Manufacturers operate some of the most complex machinery on the planet – from CNC machines and industrial robots to gas turbines with over 20,000 components. Keeping these assets running smoothly is mission-critical, yet maintenance teams are often buried under vague alerts, scattered documentation and time-consuming root cause analysis. Much of
Artificial intelligence is no longer a distant horizon – it’s here, reshaping how marketers engage with audiences and create experiences that last. Over the past year, generative AI (GenAI) has become the new status quo. More than 85% of marketing teams worldwide are already using it, with 93% having dedicated
