Empowering Enterprises: AI for Non-Technical Staffs

The world is tending towards AI-driven operations and any enterprise that isn’t already incorporating AI into its operation will be up for a rude awakening in a few years. But how do you carry along the non-technical staff and ensure that everyone is able to get creative with AI?

33% of the barriers hindering enterprises from both exploring or deploying AI are limited AI skills and expertise, according to IBM.

Obviously, the majority of enterprise employees with no AI skills are non-technical staff. However, the success of an enterprise does not only depend on the IT or technical teams. Non-technical employees, with their in-depth understanding of specific business areas, can help ensure that AI solutions directly address real business needs.

This article will discuss ways to empower non-technical enterprise staff to leverage AI effectively

Why Non-technical Staffs Are Crucial For Successful AI Implementation.

Implementing AI isn't just about being tech-savvy. While technical knowledge forms the backbone of AI development, the true measure of an AI project's success lies in its integration into broader business use cases. This is where the significance of non-technical staff becomes important Non-technical employees bring domain expertise. Their in-depth knowledge of specific business functions offers insights into the challenges and opportunities associated with AI implementation. Non-technical staff are mostly end users. Involving end-users in the development process enables enterprises to bridge the gap between theoretical AI concepts and practical applications. When employees feel invested in an AI project, they are more likely to embrace and adopt the new technology. Moreover, a diverse team, including non-technical members, brings a broader perspective to the table, helping to identify and address the potential ethical implications of AI.

Consider a financial services enterprise for example. While AI specialists can build sophisticated models to predict market trends, it is the seasoned investment banker who understands client behavior, regulatory landscapes, and risk appetite. Collaborating with the banker can provide insights into data selection, model validation, and the interpretation of AI-generated outputs. This ensures that the AI model not only produces accurate predictions but also delivers actionable recommendations that resonate with the business context.

A human resources professional, for instance, can provide great feedback on an AI-driven recruitment tool. Their insights can help refine the tool to better identify qualified candidates, improve candidate experience, and ultimately enhance the hiring process.

How to Accelerate Adoption of AI for Non-Technical Enterprise Staff

Let's face it, AI can be intimidating for nontechnical staff but it is just a tool. Like any tool, it's most effective when it's in the hands of those who understand the problem to be solved.

Imagine a marketing manager struggling with campaign performance. An AI tool can help analyze data, identify trends, and even predict customer behavior. However if the marketing manager doesn't understand the basics of AI, they might be hesitant to trust the results or might not know how to apply the insights.

So, how do you get everyone on board?

Accelerating AI Adoption with Workflow Automation

Start by making it accessible and focus on real-world examples. Then, make it easy to connect various AI tools, and visualize and build domain-specific workflows using nocode drag-and-drop automation software like Activepices . No one wants to become a data scientist overnight. Next, promote a culture of experimentation. Encourage people to try AI tools and share their experiences. Create a safe space for failures, because let's be honest, not every AI project will be a win. Celebrate small wins and learn from setbacks. Many workflow automation tools also come with pre-built integrations for AI tools, allowing users to easily connect AI capabilities to their processes. Another benefit of workflow automation for nontechnical staff includes rapid testing and refinement of AI workflow . Non-technical users can provide feedback on AI outputs, leading to continuous improvement.

Leveraging Non-Technical Roles in AI Projects

For example, a retail enterprise is aiming to improve customer satisfaction. Instead of just throwing data scientists and AI experts at the problem, they recognize the value of involving their store managers. Store managers have a goldmine of insights. They understand customer behavior, product placement, and seasonal trends in a way no data set can fully capture. Involving them in the AI project can help identify the most critical questions to answer. For instance, a store manager might suggest, "We need to predict which products will be most popular during the holiday season." Once the AI model starts churning out predictions, these same store managers can test its accuracy. If the model suggests a surge in demand for red sweaters in December, the store manager can verify if this aligns with historical sales data or customer feedback. This real-world testing is crucial for refining the model and ensuring its practical application. Another example can be used in banking. While the back-end systems might be complex and heavily reliant on data scientists and engineers, the front-line relationship managers and customer service representatives have a lot of insights. Relationship managers have a deep understanding of customer needs, financial goals, and risk profiles. They can help identify patterns in customer behavior that can be leveraged by AI. For instance, a relationship manager might notice a correlation between certain investment products and specific customer demographics. This insight can be used to train an AI model to make more targeted product recommendations. Leveraging non-technical domain expertise in AI projects will inspire non-technical staff to be more involved in AI initiatives

Upskilling Non-Technical Staff

Offer digestible sessions on core AI concepts like machine learning, natural language processing, and computer vision. Equip employees with skills to understand data sources, quality, and interpretation. Teach staff how AI can be used for customer segmentation, predictive modeling, and campaign optimization. Demonstrate AI tools for lead scoring, sales forecasting, and customer relationship management (CRM) enhancement.

Pair non-technical staff with AI experts for project guidance and provide safe spaces for experimentation with AI tools and datasets. You can also promote knowledge sharing through internal AI communities or participation in external AI forums. Encourage attendance of workshops and trainings to stay updated on industry trends and best practices. Create clear career paths for employees who demonstrate AI proficiency.