Enterprises will need a full AI technology stack to maintain competitive advantage, but few have one in place today. The key question is: How do you begin building an enterprise AI stack with tools and technologies that are optimized for AI without disrupting critical operations?
Beyond just deploying AI, organizations must first define what AI means for their industry. Does AI adoption mean large language models (LLMs) for customer interactions, computer vision for quality control or machine learning for predictive analytics? Without this clarity, AI initiatives risk becoming fragmented or misaligned with business goals, as well as failing to deliver real value.
AI readiness isn’t about simply using the latest models. Instead, it’s about strategically integrating AI into existing workflows, aligning AI initiatives with business priorities and ensuring measurable impact.
Five steps of auditing an existing tech stack for AI readiness
- Take stock. Make a comprehensive list of your current hardware, software and data architecture to identify overlaps, redundancies and, eventually, holes. For example, start by reviewing the existing tools your organization uses for its daily operations. Look for any software or systems that could be improved or optimized with AI integration, such as customer relationship management (CRM) platforms or workflow automation tools.
- Evaluate your tools for AI readiness. Assess things like infrastructure capacity for AI workloads, compatibility with AI tools, ability to make data accessible for AI, processing power and computational capacity. This evaluation process is useful to identify whether your current infrastructure can handle the computational needs of AI workloads, as well as if your data is siloed across departments or stored in legacy systems, which might mean integrating it to your AI application may be challenging.
- Identify gaps. Pinpoint the specific software, tools and processes that may be hindering AI adoption. You might discover that your current data pipelines aren’t optimized for feeding real-time data into AI models or that your internal teams lack experience in shaping AI for your specific business needs, like content generation or sentiment analysis. Fill these gaps with appropriate tools and platforms or by hiring skilled professionals.
- Make a time-bound, realistic plan. Don’t try to close all the gaps at once. Focus instead on starting with small-scale investments to test feasibility before making larger AI investments. Instead of launching an enterprise-wide AI project, begin with a single use case. For instance, implement a personalized marketing campaign using AI to tailor email content or product recommendations to individual customers. Measure the effectiveness, refine the implementation and then expand AI use across additional areas like product design, sales forecasting or customer personalization.
- Find best practices — don’t reinvent the wheel. Look for proven best practices that can save time and resources while avoiding the need to start from scratch. Leverage industry standards, frameworks and successful case studies to ensure your AI integration follows a well-established path. This approach can help accelerate the deployment process and minimize risk.
Small-scale AI investments can make a big difference
Rather than embarking on a huge project, start with a smaller specific problem and discrete solution. That could be an associate-facing HR chatbot that answers employee handbook questions immediately or a retrieval augmented generation (RAG) system that delivers recommendations tailored to customer preferences. Or it could be a predictive analytics tool for anomaly detection that improves machinery maintenance.
No matter what your specific enterprise challenge is, small-scale AI investments can deliver significant value, helping your organization refine AI implementation processes and gain momentum for higher-stakes projects.
Regardless of your organization size or where you are on your AI adoption journey, Intel® can help you unlock AI results for your business.
Learn how
You must be a registered user to add a comment. If you've already registered, sign in. Otherwise, register and sign in.