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Artificial intelligence is transforming the way organizations operate, innovate, and compete. But building an effective AI strategy requires more than just technology. It’s about leadership, a strong data-driven culture, and the right tools to reach success. As AI advances, developers and tech leaders need to assess their readiness to fully capitalize on its potential.
To help, IDC’s AI Maturity Assessment asks five key questions to figure out where you stand and what needs work. Answering these questions help businesses evaluate where they stand on their AI journey and identify areas for improvement. Real-time data processing, vector search, semantic caching, and scalable infrastructure are just some of the ways you can lay the groundwork for AI success.
Getting leadership on board and building a culture that prioritizes data is non-negotiable for making AI work. These projects aren’t always predictable—they’re all about experimenting, learning, and adjusting as you go. That’s why leaders need to think long-term, back innovation, and trust their teams to make decisions grounded in data.
A solid AI strategy starts with clean, usable, and actionable data.. A data platform that supports real-time processing and knowledge retrieval can help leaders surface relevant insights when they need them most. Cloud-based solutions also play a critical role in experimentation, giving you the AI tools and services for deploying, managing, and scaling AI models. Platforms with features like vector search and semantic caching allow businesses to contextualize data and drive more accurate results.
Using data insights effectively helps teams to identify opportunities, streamline workflows, and deliver better outcomes. For example, companies that use data-driven B2B sales-growth engines report above-market growth increases by 15-25%.
But getting there isn’t just about having data—you need to make sense of it quickly and use it in the right way. That’s where a lot of teams get stuck. Many don’t know how to surface insights at scale and integrate them into their decision-making processes.
Real-time data processing is the key to turning analysis into action. You should pick a tech stack that is future proof and flexible, and can be applied to multiple applications and use cases. Scalable AI infrastructure that supports automation and integration of insights across workflows will help your team to build dynamic apps, like recommendation engines or fraud detection systems. AI is a constantly evolving space and a lot is going to change in the coming years, so building agility as part of your stack, including in the data platform, is critical.
Organizations investing in AI need a cloud data platform that’s flexible enough for today and ready to scale tomorrow. Key features include open standards, seamless integration, and tools that enable a full data-to-AI workflow—from exploration to execution.
Your data platform should support vector search for knowledge retrieval, semantic routing to optimize queries, and caching techniques to reduce inference costs. These features make it easier to integrate AI into workflows while maintaining performance and scalability.
It’s also important that your cloud platform offers centralized AI/ML tools to give your developers the resources they need to test new AI solutions, deploy models efficiently, and integrate them into broader workflows.
AI maturity looks different for every organization, ranging from just starting out to running fully integrated AI systems. As businesses move forward, being able to access data, surface recommendations, and analyze connections between disparate datasets becomes increasingly important.
A robust data architecture helps businesses make progress. Tools like vector databases can power AI features like retrieval-augmented generation (RAG), while semantic routing and caching keep workflows fast and cost-effective.
For example, Asurion uses Redis as a vector database, semantic cache, and semantic router to overhaul their customer-facing AI chatbot. By using Redis as the vector database they were able to bring insights from the knowledge base to help add context to user queries.
AI is only as effective as the data behind it. Misinformation, errors, and outdated data can undermine the accuracy of insights and outputs. Organizations need to focus on tools and workflows that help teams to validate data and deliver high-quality, actionable results.
Semantic routing and caching make insights more accurate by keeping queries relevant to the context. AI workflows get a boost with tools that specialize in routing, making sure queries go to the right AI models or agents for the job. . Scalable cloud platforms like GCP further help teams to manage and validate their data pipelines at scale, reducing the risk of errors and improving reliability.
Building an effective AI strategy requires leadership, a data-driven culture, and the right technology to support real-time decision-making and scalable AI workflows. By asking these five critical questions, leaders can better understand their organization’s AI readiness and identify areas for improvement.
IDC’s full AI Maturity Assessment goes even deeper, with 11 key questions to evaluate your AI capabilities. Take the AI Maturity Assessment to see where your company stands today, and identify the best path forward for AI adoption.
