From chatbots to predictive analytics and process automation, AI is revolutionizing the workforce by building on organizations’ existing cloud and digital journeys. Enterprise companies are adopting AI at a pace faster than they did the Internet or cloud computing, and Gartner predicts that more than 80% of all enterprises will have used generative AI (GenAI) by 20261. The technology’s ability to analyze data, recognize patterns and make decisions at speed and scale are already transforming industries.
Depending on existing processes and operations models, beginning your company’s AI journey can seem daunting. Tech decision-makers at enterprise companies are tasked with assessing the value of ever-expanding options. However, implementing AI across an enterprise’s core processes doesn’t have to be complicated. You can leverage AI blueprints for AI ecosystems for a structured starting point, with templates or frameworks that accelerate the process of creating AI-powered solutions to help expand your organization’s AI capabilities.
Starting your AI journey
By working to identify low-hanging AI opportunities, organizations can start building toward readily achievable ways to unleash the power of AI with minimal development effort. Examples of quick wins that can be implemented efficiently include everything from customer service chatbots and virtual assistants to AI-enhanced fraud detection. Automating routine tasks can save time and money — and in the case of delivering insights from deep data analysis, improve decision-making.
Enterprises should consider these key questions to understand where to prioritize AI efforts and how to increase readiness:
- Is the scenario, use case or problem to be solved, clearly defined?
- Are priorities set around where AI will deliver the most business value?
- Is the planned infrastructure architecture clear and appropriate?
- Are all necessary data sources clearly understood and accessible?
Having a clear grasp of the problems to be solved allows enterprises to easily identify potential solutions. Low-hanging opportunities could include help desk automation, AI-assisted call routing, automated reporting, lead scoring, fraud detection, or meeting transcription. The list is long, but it all starts with an understanding of the business issues at hand and the available infrastructure. Being able to index the full spectrum of an organization’s abilities allows them to develop a comprehensive view of how to crawl-walk-run toward an enterprise AI solution stack.
AI use cases showcase industry-specific ways that companies are using AI to solve problems and increase efficiency. Case studies provide detailed information on how enterprises leverage Intel products like the Intel® Gaudi® accelerator and Intel® Xeon® processor and illustrate how investing in similar implementations can help your business or your customers. Here’s how AI Sweden is prototyping an AI solution that helps public entities collaborate in healthcare settings utilizing on-site servers equipped with Intel® Xeon® processors, Intel® Gaudi® 2 accelerators and a retrieval augmented generation (RAG) architecture.
It's easy to fast-track AI
Open source collaboration can create frameworks that inform the evaluation, construction and connection of composable GenAI solutions — an important first step to AI adoption. Developers can collaborate, experiment and refine in real time with Open Platform for Enterprise AI (OPEA), a that aims to provide open source and broadly available solutions to help enterprises be successful with GenAI. Enterprises big and small can harness AI’s amazing capabilities with open solutions that minimize integration and maximize flexibility, fast-tracking mass input, refinement and testing through the open source community to develop faster, share information more freely and accelerate AI.
Getting started is simple with Intel tools that integrate with an organization’s existing infrastructure and support AI solutions. An AI-powered future is within reach – let’s get ready.
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