When ChatGPT first hit the market its human-like responses were astonishing, yet somewhat eerie. Many thought, “It communicates just like me.” It follows that the next logical thought was, “It’s going to take over my job.” However, the fear that AI-driven tools will replace human software testers is unfounded. AI will certainly impact testing jobs, including market expectations, skillsets, and required knowledge.

Artificial intelligence (AI) can transform the way your enterprise does business—but if you can’t quickly implement AI in your business processes, you’ll just as quickly fall behind competitors. This is where low-code AI tools can help. Over the past decade, low-code platforms have enabled software engineers, professional developers, and employees with minimal coding experience to build new digital, automated solutions using drag-and-drop interfaces.

The business of governance is not easy. Public sector organizations face a range of obstacles from corruption to lack of transparency to red tape—obstacles that have the potential to erode public trust in institutions and hinder economic development. That’s probably why, in virtually every country around the world, popular culture lampoons the intricacies of government bureaucracy.

Last year, we held our first Accelerate event, to explore industry trends, data and technology innovations, and data strategy case studies in financial services. This year, we are expanding to five industry events, featuring leaders in financial services; retail and consumer goods; manufacturing; media, advertising and entertainment; and healthcare and life sciences. Accelerate Financial Services and Accelerate Retail are one-day virtual events brought to you by Microsoft.

Local LLMs are becoming mainstream with sites like HuggingFace promoting open sharing of trained LLMs. These LLMs are often very small but still extremely accurate, especially for domain-specific tasks like medicine, finance, law, and others. Gemma is a multi-purpose LLM and, while small, is competitive and accurate. Local LLMs also have the advantage of being completely run inside your own environment.

95% of insurers are currently accelerating their digital transformation with AI-driven claims processing. Traditionally, this process involved manual steps such as claim initiation, data entry, validation, decision-making, and payout, consuming significant time and resources. However, the introduction of AI has replaced tedious manual work, enabling companies to streamline their tasks efficiently.

It’s no surprise that ever since ChatGPT’s broader predictive capabilities were made available to the public in November 2022, the sprawl of stakeholder capitalization on large language models (LLMs) has permeated nearly every sector of modern industry, accompanied or exacerbated by collective fascination. Financial services is no exception. But what might this transformation look like, from practical applications to potential risks?

Retrieval-augmented generation (RAG) is an AI framework and powerful approach in NLP (Natural Language Processing) where generative AI models are enhanced with external knowledge sources and retrieval-based mechanisms. These appended pieces of outside knowledge provide the model with accurate, up-to-date information that supplements the LLM’s existing internal representation of information. As the name suggests, RAG models have a retrieval component and a generation component.

Over the past year, interest in artificial intelligence has surged due to the proliferation of generative AI and large language models. These tools captured imaginations, demonstrating a technology brimming with possibility. While many focused on the potential of these tools, some companies made AI practical. For example, last year, Appian released packaged AI tools for processing content at scale and quickly building interface forms.

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