Why building user confidence is a cornerstone of AI success

In the previous instalments of our AI blog series, we focused on some of the strategic and technical requirements of a successful AI deployment. But getting the full value of your AI projects is about more than making the right technology decisions. User adoption plays a fundamental role in determining long-term success.

Without the buy-in and engagement of the user base, even the most sophisticated and well-targeted AI deployment can fail to deliver value. By addressing common hurdles to adoption and empowering your users to capitalise on AI, you can drive the achievement of desired business outcomes and help build a solid foundation for scaled adoption and further innovation.  

Lenovo Hybrid AI Advantage™ with NVIDIA is a flexible framework that changes the game with a library of ready-to-customise AI use case solutions. These are fully tested and adaptable by function or industry, providing the foundation to help you tackle complex business challenges with reduced risk and faster time-to-value – ensuring your AI strategy delivers measurable impact from day one.  

Without users who are fully engaged, any AI deployments impact on wider business productivity and operational efficiency will be dampened. If only a small number of users are willing, able and confident in their AI usage, overall uptake will be reduced. This can create a negative feedback loop, where a lack of users who are able to demonstrate the value of AI to their peers effectively turns others off.  

By focusing on user engagement and onboarding as a core part of your deployment process, you can address this, supporting the value that AI offers your business, and generating useful feedback that can help steer your future strategy. The expertise and experiences of internal teams is an incredible asset for AI deployment, helping to highlight unexplored AI use cases that can inspire innovation.

While there are many reasons for poor AI adoption, there are a few common barriers that should be addressed as part of any well-designed deployment. Considering and addressing these factors helps streamline the AI onboarding process, and ensure high engagement with new tools as they’re rolled out:

1. The confidence gap: While many users are curious about the potential of AI, and keen to understand who it can help within their role, a lack of confidence can become a blocker. Users can feel uncertain about how to use AI correctly or how to make best use within their existing workloads. This lack of confidence ultimately blocks the development of overall competence and familiarity which only extends the challenge. This often happens with wide-scale deployments of off-the-shelf AI tools like Microsoft Copilot, as users aren’t sure how using these should fit into their day-to-day, and decide to instead stick with what they know.
2. Lack of AI training: Designed to add value and ease workloads, the majority of AI tools are intuitive in nature, but this doesn’t mean you can overlook the importance of AI-focused training and user enablement. The issue isn’t necessarily an unwillingness to learn, but rather an absence of the necessary guidance and distribution of resources to ensure users are both equipped to use AI and understand how to do so effectively. Without this instruction, even those with a natural curiosity and confidence will struggle to unlock the full potential of any deployment and may see their own enthusiasm erode over time.  
3. Perceived risks: While many users are exposed to AI innovations outside of work, and while this coverage and attention has generated excitement around new innovations, it has also highlighted how incorrect usage can expose sensitive and confidential data, including some high-profile data breaches. Adding to that, some users may perceive AI as a threat to their livelihood, seeing it as a platform that will be used to replace them in their role, rather than support them. As such, users who are less familiar with AI will temper their usage to limit their exposure to these perceived risks.   

While these barriers are common, they’re far from insurmountable. By focusing on user adoption and engagement as a core part of your AI strategy, you can move beyond overarching user concerns and prepare your teams for success. The following tactics should all have their place in your wider deployment programme:

Positioning AI within your business: Often the best first step to drive AI adoption is to set out the purpose of AI within your business. By reframing narratives around AI to highlight it as a tool that augments a user’s own capabilities, rather than a technological replacement for them, users can more clearly see the benefits of AI and are more inclined to adopt it.

Identifying your use case: Focusing your AI deployment on a critical use case is often the cornerstone of success. By targeting time-intensive workflows and manual processes that cause challenges for users, they’ll be more inclined to view AI as a problem-solving tool, and see the value it can offer, increasing their level of buy-in.  

Lenovo Hybrid AI Advantage™ with NVIDIA provides a flexible framework and a library of validated, production-ready use cases to help you reduce risk and accelerate time-to-value, so you can move from AI ambition to proven outcomes quickly. From intelligent assistants that find answers up to 70% faster, to smart agents that reduce handling time by 20%, and tools that accelerate content creation by up to 80% - all with governance and security built in. These solutions are designed to deliver measurable impact.

AI training programmes: While giving some top-level training on how to use AI may be the fastest way to bring users up to speed, this approach can leave users feeling unsupported. Onboarding AI within your day-today workstreams can be a significant operational change, and some users need a higher level of support to grow their confidence. Regular check-ins can also provide an opportunity to collate user feedback and actions fixes or enhancements that further improve overall user experience.

Guidance and usage policies: Outlining guidelines for responsible use of any tool is vital to encourage user adoption – setting out clear usage policies helps to increase confidence and drive subsequent adoption. These policies should be carefully considered to line up with wider data governance and compliance requirements, which we explored in more detail in our previous blog. Crucially, any policies need to be easy to understand, properly communicated and easily accessible.

Finding AI champions: Peer-to-peer knowledge transfer is often the best way to train new users, and AI adoption is no different. Look to identify AI power users in your business and encourage them to offer advice to other users. These champions can also be an accessible point for the collation of user feedback, helping you gather diverse perspectives to guide the future of your AI strategy.

No two organisations have the same needs, and AI strategies should reflect that. By centring user engagement as a cornerstone of your deployment, you can ensure high levels of adoption, and, as a result, maximise the value these tools can offer. 

User adoption is the sink-or-swim test for many AI deployments – energising your users to adopt this technology is a critical success factor for any AI strategy. Our Ideation Workshops, backed by Lenovo and NVIDIA, are designed to ensure success for your first AI deployments, helping you target key use cases and prepare a roadmap based on expert guidance that ensures successful deployment and the achievement of business outcomes.

If you’re eager to get started, book an Ideation Workshop today to break through the barriers to ROI and ensure your AI strategy works for your users, delivering measurable impact and real value for your organisation.