Which processes are ripe for AI transformation?

In Transformation Consulting, process redesign methodologies are a common practice. However, how has our approach changed due to the rise of AI? How do we determine which processes can benefit from AI enhancement or replacement? This largely depends on the industry to which the process belongs, as not every process within a specific domain is suitable for AI integration. Typically, the following criteria can be used to assess processes:

- Basic Automation: These processes involve repetitive, rule-based tasks that can be easily automated using robotic process automation (RPA) tools.

- Enhanced Data Processing: These are processes where machine learning can provide value, such as predicting outcomes or identifying anomalies.

- Advanced Decision Support: Look for processes where cognitive computing can aid complex decision-making, moving from simple automation to generating strategic insights.

- Process Optimization: Identify workflows that could benefit from AI-driven process mining and optimisation algorithms to streamline operations.

- Strategic Transformation: Consider processes that, when integrated with AI, could transform your business strategy in real-time.

Which processes to be automated by AI?

Identifying the right processes for AI automation is crucial for maximizing benefits. This section outlines the key steps to identifying and prioritizing automation processes. It includes assessing current workflows, identifying repetitive tasks, and evaluating the potential impact.

AI Process Readiness Assessment

1.  Assess Current Workflows

   Thoroughly map out existing processes, pay attention to bottlenecks, and gather employee input.

2.  Identify Repetitive Tasks

   Create a checklist to identify high-frequency, low-complexity tasks for AI automation.

3.  Evaluate Potential Impact

   Use a scoring system to prioritize tasks based on their potential impact.

4.  Conduct a Feasibility Analysis

   Evaluate technical requirements, costs, and organizational readiness for automation.

5.  Develop an Implementation Plan

   Outline steps for implementing AI automation, set clear goals and timelines, and establish metrics for success.

6.  Monitor and Optimize

   Continuously review and optimise automated processes based on feedback and performance evaluations.

We can use AI to automate additional processes to keep the business running smoothly and optimally. AI is helping organisations in various ways, such as enhancing self-service applications, predicting trends and buying behaviours, helping decipher radiographs to predict medical outcomes, responding to requests, routing information to the appropriate individuals for processing, and more.

Some common areas where AI is already helping organisations include:

- Automating high-volume and repetitive tasks

- Enhancing the ability to understand unstructured data within company assets

- Improving customer engagement

- Providing holistic views to improve decision-making

- Improving data quality and insight

Once you have identified the target processes through the AI Process Readiness assessment, you can consider AI workflow automation software, an innovative combination of AI and automation technologies. It involves using AI algorithms within workflow systems to automate complex tasks. AI workflow automation utilizes machine learning, natural language processing, and other technological advancements to accomplish intricate tasks. For example, in a human resources department, automated AI workflows can accomplish this much faster than manually sifting through a pile of applications, selecting the best candidates, and scheduling interviews. This allows HR professionals to focus on higher-level tasks such as ensuring the right fit and successful onboarding. Other examples of AI workflows in action include customer service chatbots and virtual assistants, personalised automated email marketing, predicting supply chain demand and optimizing inventory, and automatic data gathering, analysis, and reporting.

What is AI Workflow Technology?

AI workflow technology encompasses various components, but three are particularly crucial:

1. Algorithms

Algorithms are at the core of AI workflow automation. These sophisticated models are designed to learn and improve over time, enabling the automation of increasingly complex tasks.

2. Data

The quality and quantity of the data fed into AI algorithms determine how effective the tool is, as AI systems rely on data to thrive. This data could include numbers, dates, emails, social media posts, and more.

3. Integration

Integration involves linking AI tools to user interfaces or other business systems to ensure smooth and continued operation.

There are three main types of artificial intelligence:

• Generative AI: This type of AI generates text, images, or other content using specially designed models. These models are trained by learning patterns and details from training sets, and then they create new data that follows similar patterns.

•  Predictive AI: Predictive AI uses machine learning (ML) to analyse historical data and identify trends and patterns. It then uses this information to predict future events.

•  Assistive AI: This type of AI uses machine learning to assist humans in completing tasks, providing detailed instructions, recommendations, and more.

Each of these types of AI can help improve and enhance business processes. One of the initial ways that AI can assist with business processes is by working with data to find, interpret, and act on the data. This includes tasks such as:

- Interpreting data to route requests automatically

- Automating credit scoring and risk management

- Finding information in audio, video, and other media formats

- Locating and redacting Personal Identifiable Information (PII)

- Matching data for faster processing

- Predicting trends, issues, and detecting fraud

Let’s delve into each of these in more detail. AI is often used to analyse data, positively impacting customer interactions. For example, AI can expedite the analysis of incoming customer data, leading to faster decision-making and improved overall customer service.

Consider a situation where you receive numerous customer requests daily, some requiring human interaction and some not. This is an ideal opportunity to implement artificial intelligence to streamline request handling. Using AI to analyse request context can guide the routing of requests to the appropriate team or even generate customer responses, including links to self-service instructions. This AI-driven response enhances the customer service experience. In the following BPMN example, you can see a service task that utilizes an AI engine to interpret a customer request and determine its nature for proper routing.

This example illustrates how optimizing the business process for request handling can save time and staff effort and improve the customer experience through quick and efficient service. This approach can be applied to various customer service areas.

Camunda Process Flow Example

AI can analyse historical performance, behaviour, and metrics to improve and streamline business processes and results. This is especially important when examining customer buying behaviours, predicting trends, and detecting fraudulent activities. For instance, in our online interactions, AI often predicts potential purchases. If we purchase an outdoor grill, AI, with the help of machine learning, can predict that we might also be interested in accessories or cooking utensils to complement it. This functionality can be integrated into the purchasing process, where additional recommended products are suggested to be included in an order based on the original purchase.

Moreover, predictive analysis can be used to tackle other business challenges. For example, suppose a product has received numerous issue reports related to a specific component. In that case, a pattern may emerge, allowing for early detection of a manufacturing defect or other quality issue. Armed with this information, we can proactively contact existing customers to provide a fix or replacement to prevent potential dissatisfaction. Finally, reviewing historical data can help identify fraudulent activities. For instance, recurring requests for assistance from the same address using different names and details or identification numbers could indicate potential fraud, prompting further investigation.

AI Worklflow Automation Software Selection

By following these steps, businesses can successfully implement AI workflow automation and reap the benefits of increased efficiency, cost savings, and improved accuracy. Remember, the key to success is continuous monitoring and optimisation to ensure the AI tools remain effective and aligned with your business goals.

1.  Identify Business Needs: Determine the tasks and processes needing automation.

2.  Research AI Tools: Evaluate different AI solutions and select the best fit your needs.

3.  Develop an Implementation Plan: Create a detailed plan outlining the steps for integrating AI into your workflows.

4.  Train Employees: Conduct training sessions to ensure your team is well-versed in using AI tools.

5.  Monitor Progress: Regularly review the performance of AI tools and gather feedback for continuous improvement.

6.  Optimize Processes: Make necessary adjustments based on feedback and performance metrics to enhance efficiency.

Outcomes of Implementing AI Automation

A Manufacturing Company in Italy

Franke Group Sassoferrato Plant in Italy

The Franke company is a global leader in supplying products for kitchens, food service systems, and professional coffee making. At their Sassoferrato Plant in Italy, they have implemented AI systems to improve problem-solving and increase reliability in their production processes. MyNeXT created an AI system called AIOCAP (Automatic Intelligent Out of Control Action Proposal) to support operators in solving quality problems. The system detects quality issues, provides a diagnosis, allows operators to add data, and proposes a solution plan. The company aims to continue perfecting production management methodologies and has won the Franke Manufacturing Trophy for excellence in maintaining high standards of quality and innovation.

A manufacturing company in Switzerland:

ABB Ltd., a global enterprise headquartered in Zurich, Switzerland, faced numerous challenges in enhancing the efficiency and automation of its manufacturing processes, particularly in the robotics division. The company needed to optimize its production lines to reduce costs and increase output without compromising the quality of its robots. Another major challenge was maintaining high precision and safety standards in manufacturing electrical components. ABB was also focused on boosting energy efficiency and reducing carbon emissions throughout its worldwide operations.

To address these challenges, ABB utilized AI across its operations. The company implemented AI-enhanced robotics in its production lines, capable of self-optimization for performance efficiency and accuracy. AI also improved predictive maintenance capabilities, minimising operational disruptions and repair expenses by detecting equipment issues before they occurred. In quality control, AI-driven systems were used to conduct thorough inspections and adapt operations dynamically based on real-time data. Furthermore, AI managed and optimised facility energy use, significantly reducing energy costs and supporting ABB’s sustainability goals.

The deployment of AI technologies led to significant improvements across ABB’s manufacturing operations. AI-enhanced robotics not only increased production efficiency but also maintained the high quality of the products, meeting customer expectations and regulatory standards. Predictive maintenance strategies were critical in reducing operational halts and prolonging the operational life of essential machinery. AI-driven quality control systems ensured that all products met the stringent safety and performance standards. Energy management initiatives reduced consumption by 15%, demonstrating ABB’s commitment to environmental stewardship. Overall, ABB’s integration of AI into manufacturing processes positioned it as a forward-thinking leader in industrial automation.

 A manufacturing case study in Japan

Established in 1948, Honda Motor Co., Ltd. is a globally recognised Japanese multinational conglomerate primarily producing automobiles, motorcycles, and power equipment. Honda faced significant challenges adapting its production lines to meet changing consumer preferences and environmental standards. The company needed to increase manufacturing flexibility to produce a wider variety of models efficiently. Additionally, Honda aimed to enhance the sustainability of its operations by reducing waste and improving energy efficiency.

Honda Manufacturing Production Line

In response, Honda integrated AI into its manufacturing strategy. The company used AI to enhance flexible manufacturing systems, allowing quicker switches between vehicle models on the same production line. AI was also implemented to automate the logistics of parts and materials, reducing waste and downtime. Sophisticated AI algorithms were utilised to effectively oversee and regulate energy consumption, greatly enhancing energy efficiency. Moreover, AI-powered robotic systems were introduced to assist in precision tasks, ensuring high quality and reducing human error.

Honda’s integration of AI led to notable improvements in manufacturing efficiency and sustainability. The flexible manufacturing systems enabled Honda to respond swiftly to market changes and consumer demands, enhancing production agility and efficiency. AI-driven logistics optimisations reduced waste and minimised costs, while AI-controlled energy management significantly cut energy consumption, supporting Honda’s environmental goals. The precision provided by AI-assisted robotics ensured consistent quality and reliability in Honda’s products. Honda reinforced its commitment to innovation and environmental responsibility through these AI initiatives, solidifying its position as a leader in global automotive manufacturing.

As we conclude our in-depth exploration of AI automation, it's evident that leveraging AI workflow automation can revolutionise business processes. By identifying and automating repetitive tasks, companies can significantly enhance efficiency, reduce operational costs, and improve accuracy. The benefits are wide-ranging, from increased productivity to improved customer experiences and the potential for operational scalability and flexibility. To begin your AI automation journey, you can start by evaluating your current workflows and identifying repetitive and time-consuming tasks. These tasks are ideal candidates for automation. Then, assess the potential impact of automating these tasks, considering factors such as time savings, cost reduction, and improved accuracy. Perform a feasibility analysis to ensure the identified tasks can be automated using available AI tools and technologies. Implementing AI automation requires a well-planned approach. Choose the right AI automation tools that suit your business needs, train your employees to work alongside these new technologies, and continually monitor and optimise the automated processes. This ongoing optimisation ensures that the AI systems remain efficient and effective, driving continual improvements in productivity and performance.