Optimizing WalkMe’s AI features
WalkMe’s digital adoption platform has many AI-driven features. Clients can optimize them to personalize the software for their needs.
What’s more, by rolling out workflow automation which makes use of AI, these intelligent processes can learn and adapt as they go.
Research shows that organizations see the benefits of this technology. A survey by Harvard Business Review reveals that 80% of respondents see using intelligent automation (IA) as vital.
Let’s take a closer look at what WalkMe has to offer:
ActionBot
WalkMe’s ActionBot automates tasks for users. It allows them to avoid struggling through hard processes by knowing what they are trying to do. Clients can build and improve the tool on their own web applications. They can use the WalkMe Editor to do so. What’s more, you can customize ActionBot to meet specific needs. For example, you can build custom conversations. These define how the bot interacts with users. More optimization comes when clients set up triggered actions for the AI tool. For example, when a user asks about a topic, the bot can launch a relevant Smart Walk-Thru. This helps users and gives immediate support. You can track the ActionBot as well. This lets clients analyze usage. They can find pain points and track success. Also, they can keep adjusting conversations, keywords, and triggered actions over time.AI recommendations
AI-driven insights in WalkMe’s platform let organizations check user interactions. They can watch how people use WalkMe features across digital platforms. These insights help in recognizing patterns, preferences, and pain points. AI algorithms process data to provide actionable insights. Clients can use this data to optimize workflows. This allows for higher employee success with digital tools.AI for in-app guidance
WalkMe has added AI to its in-app guidance. The guidance provides on-screen support as the user moves through a workflow. AI helps those who build in-app guidance. It helps them optimize processes with automated visuals, copy, segmentation rules, and charts. As a result, the app rolls out personalized in-app guidance to users. Over time, the guidance can be improved to become more effective.Predictive analytics
The platform can perform predictive analytics with AI. The feature analyzes historical data and patterns to predict users’ needs and behaviors. This proactive approach lets organizations address challenges early. It also lets them recommend actions based on trends. This ensures a smoother adoption journey for AI technologies. Businesses see the benefits of this technology more and more. The Insight Partners found that the predictive analytics market will grow to $30 million by 2028.Use cases for AI optimization
AI optimization in business
Here are three examples of how AI optimization can appear in business scenarios.Customer service enhancement
In customer service, AI can improve interactions. It can make them better and faster. Businesses can use AI to analyze customer inquiries and sentiment. This lets them optimize chatbot responses to be more accurate and helpful. Also, AI-driven sentiment analysis can find patterns in customer feedback. This helps companies refine their products and services to better meet customer needs.Supply chain optimization
AI optimization can revolutionize supply chain management. It does this by improving forecasting and inventory management. AI can also improve demand forecasting. It does this by studying past sales data and factors like weather and market trends. It can predict future demand more accurately, which helps businesses optimize inventory levels. It cuts stockouts and lowers carrying costs, which all boosts supply chain efficiency.Financial fraud detection
In finance, AI optimization is crucial. It helps with fraud detection and prevention. By improving AI algorithms, banks can find suspicious patterns and behaviors which are signs of fraud. They can also adapt to evolving threats as they improve. They can detect fraud with greater accuracy. As a result, they help businesses reduce losses and prevent fraud.Success stories with AI optimization
WalkMe has been key in helping companies use and improve AI to further their goals. Here’s an example:GOJO
Gojo made use of the ActionBot feature from WalkMe to make sales leaders’ lives easier. Workers can have a ‘conversation’ with the ActionBot. They can use it to build their own campaigns. These campaigns will align with special sales initiatives. This empowers employees. They can now reduce reliance on getting tasks from the marketing team. They can also stop waiting for the support team to help with campaign setup. GOJO has saved thousands of hours of employee time and boosted productivity. It did this by adopting the mantra “Do it for me, don’t teach me” and using WalkMe as a virtual assistant.AI optimization vs machine learning
AI optimization and machine learning are related. They both connect to the broader field of artificial intelligence. But, they refer to different aspects of AI’s development and use.- AI optimization is the process of improving AI systems’ performance and efficiency
- Machine learning is an approach within AI. It focuses on letting computers learn from data. The computers use that knowledge to make decisions or predictions.
| AI optimization | Machine learning | |
| Definition | Refining and improving the performance, efficiency, and effectiveness of AI systems. | Developing algorithms and models. Computers learn from data and make decisions or predictions. |
| Focus | Enhancing various aspects of AI, such as accuracy, speed, resource use, and scalability. | Enabling computers to learn from data. They make predictions or decisions without being explicitly programmed. |
| Techniques | Fine-tuning algorithms, models, parameters, and processes to achieve better results. | Developing algorithms and models for supervised learning, unsupervised learning, and reinforcement learning. |
| Goal | Maximizing the utility and impact of AI technologies across various applications and domains. | Allowing computers to learn patterns and relationships from data. They use this knowledge to make informed decisions or predictions. |
| Examples of use | Personalized recommendationsImproving chatbot response timesEnhancing predictive analytics | Image recognitionNatural language processingMaking recommendations Predictive analytics |
Advantages of AI optimization
The advantages of AI optimization include:
Better performance. AI systems can make more accurate predictions. They also process faster and more efficiently.
Better decision making. Large amounts of data can be analyzed more effectively. As a result, decision-making is more informed and data-driven. This is particularly useful in finance, healthcare, and marketing.
Personalization. Tailoring recommendations, content, and services to people’s preferences is beneficial. It leads to higher user satisfaction and engagement.
Reduced manual effort. Repetitive tasks and processes can be cut down and assigned to AI. People can spend more time on strategy and creative tasks. This means they become more productive and efficient.
Scalability. Once algorithms are optimized, they become more scalable. They let businesses handle more data and growing user bases. And, they do this without sacrificing performance or efficiency.
Competitive advantage. Businesses that use AI optimization gain a competitive edge. They are able to innovate and make their products stand out.
Challenges of AI optimization
While AI optimization offers significant advantages, it also presents several challenges:
Complexity. The process involves intricate algorithms and models. They need deep expertise to understand and use well. Complexity can pose challenges for businesses lacking talent or resources.
Resources. Optimization may need lots of computational resources. This includes fast hardware and big datasets. Getting and managing these resources can be expensive. It’s hard for organizations with limited budgets or infrastructure.
Data quality and availability. We heavily rely on high-quality data for training and validation. But, getting clean, relevant, and diverse datasets can be hard. This is especially true in fields where data is scarce or low quality.
Ethical and bias considerations. Biases in training data may be inadvertently perpetuated. This can lead to unfair or discriminatory outcomes. Organizations face a big challenge. They must address ethical concerns. They must ensure fairness, transparency, and accountability in AI systems.
