Avoid AI Pilot Failure

AI Investment in the enterprise has reached unprecedented levels, with organizations pouring resources into ambitious projects. However, a recent MIT report delivers a sobering reality: 95% of AI pilots fail to produce measurable business impact, due to issues such as lack of executive sponsorship, unclear workflow integration, and challenging change management.

These gaps leave enterprise leaders to question, how do we avoid being the next statistic? The good news is this challenge can be overcome. Based on firsthand experience with customers around the world, we know what it takes to be successful. Here are five best practices to validate the business feasibility and expected outcomes from a pilot and successfully transition to production, unlocking the ROI you seek in record time.

Best Practice #1: Focus your pilots on core business priorities

AI offers opportunities for every business function; however, you need to think about which functions matter most to your business and focus AI pilots there. All AI pilots have to align with these core functions. What's core to your business? We find in our work with customers that IT can't answer this question in a vacuum, but must directly engage business stakeholders. Our customers have found that an AI Accelerator Workshop is a great way to get aligned among IT and business executive teams on expected KPIs and use case priorities.

Best Practice #2: Get clear about the impact of AI on your workflows

Once you've identified your core business priorities, you'll need to focus on the business pain points in your workflows to determine how AI solutions will improve productivity for specific tasks. With the extensive AI partner ecosystem of the Dell AI Factory for critical use cases such as code generation and digital assistants, you can select the AI solution optimized for your workflow. Through proven workflow modeling techniques delivered by Dell Services, which we call value stream mapping, you can effectively define business workflows, identify pain points and demonstrate solution impact.

Best Practice #3: Don't get tripped up by data silos

While the first impulse of many developers is to pilot new applications in the cloud because they are familiar with cloud development tools, building in the cloud can take longer and pose executive concerns around data boundaries. Figuring out how to integrate enterprise data into cloud models can be very time-consuming since most data is not already located there. A better approach is to use a data lakehouse to quickly integrate cloud and enterprise data for AI models. The Dell AI Data Platform is a great way to break down these silos, while also simplifying the task of preparing all types of structured and unstructured enterprise data for AI. One financial services customer achieved this level of enterprise AI data integration firsthand when we analyzed millions of data points of customer feedback and bank branch reviews. Through an on-premises proof of concept of an AI-powered chatbot, we refined actionable insights delivered through an interactive dashboard.

Best Practice #4: Make sure your pilots are consuming real data

We see many customers who are so focused on getting the business functionality of their AI pilots right that they use synthetic data. But this approach risks a longer pilot phase when the inevitable need to integrate enterprise data appears. Dell Services has developed an innovative approach to pilot deployment that safely integrates clean customer data at your location or at a partner colocation facility, giving a full view of how well AI systems perform on real data, while addressing executive concerns about data boundaries in the cloud. This is also a more cost-effective approach: a recent study by Principled Technologies found that inferencing and fine-tuning with the Dell AI Factory is up to 63% more cost-effective than public cloud.¹

Best Practice #5: Drive adoption with effective change management

One of the top issues raised in the MIT report is challenging change management. Change management is crucial for AI projects because these bring significant shifts in how organizations operate, make decisions and interact with technology.

Dell Services can help in several ways. Our consultants have deep and longstanding experience helping IT organizations succeed when they roll out new technologies to the workforce, with tools and techniques for such concerns as workforce sentiment analysis. We can even go a step further to fully manage AI solutions, freeing resources so you can focus on business-facing concerns. Dell Managed Services for the Dell AI Factory with NVIDIA provide full-stack management for the full solutions lifecycle, backed by SLAs. Worley is a customer that's taking advantage of these managed services so that they can build out their use cases instead of worrying about AI infrastructure.

Get on the fast track to enterprise scale

To maximize AI's potential for your organization, you need a strategic partner like Dell Technologies to accelerate outcomes and drive progress at every stage. Backed by an end-to-end portfolio of AI services, comprehensive solution ecosystem, deep AI expertise, and worldwide scale, and recognized for AI innovation by Fast Company, you can be confident that you will end up on the right side of the AI pilot trajectory to enterprise success.

Reach out to your Dell representative for more information about an AI Accelerator Workshop or check our AI services web page.

¹1Based on Principled Technologies' paper commissioned by Dell, "Make GenAI investments go further with the Dell AI Factory," comparing the TCO for on-premises Dell infrastructure using a Dell AI Factory solution versus a similarly configured public cloud solution, July 2025. AWS Sagemaker and Azure ML cloud managed services were used for the comparison. Estimated costs were modeled utilizing Llama 3 8B LLM for inferencing and model fine-tuning workloads by organizations over a 4-year period. Server models used were XE9680s with 8 x H100 GPUs. Actual results may vary.