Navigating the Risks of Artificial Intelligence: A Strategic Approach

Businesses feel compelled to experiment with AI because of the perceived benefits it offers in terms of efficiency, innovation, customer experience, and competitive advantage. They are told that by integrating AI into their operations, they can unlock new opportunities, streamline processes, and stay ahead in a rapidly evolving technological landscape.

Nevertheless, businesses may be sceptical about adopting AI for several reasons.

• Technological – understanding AI capabilities and limitations, as well as the requirements for large amounts of high-quality data.
• Financial – the cost of investing in tools and capabilities, with uncertainty about the return on investment
• Ethics – the “black box” nature of AI algorithms makes it difficult to understand and explain how decisions are made, which may lead to questions about bias.
• Organisational – resistance to change and bridging the skills gap.

Automation has evolved since the Internet and Early Digital Transformation (1990s-2000s) where simple IT operational tasks were programmed to run. This evolution continued through the automation of workflows and cloud computing (2010-2020s), and now Internet of Things, AI and Machine Learning.

Before implementing AI tools, it’s important for businesses to consider starting with simpler forms of automation to demonstrate value and benefits. These simpler forms of automation can deliver significant efficiency improvements and pave the way for more advanced AI implementations.

Simple digital automation involves using software tools and technologies to automate repetitive, rule-based tasks and processes without requiring complex programming or advanced technical expertise. This type of automation can streamline operations, improve efficiency, and reduce errors, making it accessible and beneficial for businesses of all sizes. Process automation, for example, involves automating routine tasks and workflows within and across teams, which can increase productivity, and reduce manual input and errors, ensuring consistency.

To ensure that the desired outcomes are met, it is important to identify the right process or processes that are suitable for automation. This requires a thorough understanding of the workflow, the bottlenecks that need to be unlocked leading to potential benefits. In some cases, existing processes may need to be optimised before implementing automation.

From a change management perspective, employees may resist automation due to fears of changes in job responsibilities, increased workloads or job loss. It’s important to effectively communicate the benefits and impacts of automation to all stakeholders to minimise resistance. Providing proper training and development opportunities for employees to adapt to new technologies and workflows is critical.

Example 1: Reducing Manual Data Entry

One of the simplest yet impactful areas for automation is the reduction of manual data entry tasks, such as keying in information or copy-pasting data between systems. For instance, think about the finance department of a large organization that needs to pull invoices from one system and input them into another for processing. This manual task is time-consuming and prone to errors, like duplications or typos, which can lead to costly mistakes.

By automating this process through integrations or robotic process automation (RPA), the data can flow seamlessly from one system to another without the need for human intervention. Not only does this drastically cut down processing times, but it also improves accuracy and frees up employees to focus on more value-added tasks. For example, instead of spending hours on repetitive tasks, team members can focus on data analysis to spot trends or anomalies, enabling better decision-making.

Example 2: Automating Order Tracking in Supply Chains

Another excellent example of automation is in the tracking of orders through multiple systems within a supply chain. Managing the flow of an order from production to delivery often involves multiple departments, platforms, and steps, all of which must communicate effectively. Without automation, employees may manually update spreadsheets or send emails to relay an order’s status—a process that’s prone to delays, miscommunication, and a lack of visibility.

Automation can revolutionize this by connecting the different systems used across the supply chain. For instance, with an integrated warehouse management system (WMS), transportation management system (TMS), and customer relationship management (CRM) software, every stakeholder can access real-time updates on an order’s status. Automation ensures that when an order is picked in the warehouse, shipped, and delivered, each step is automatically updated across the platforms. This not only improves visibility for customers and supply chain managers but also helps identify and address bottlenecks quickly, ensuring timely deliveries and enhancing overall efficiency.

Final thoughts

Optimising business processes and operations through automation offers many opportunities to significantly enhance an organisation’s overall performance and competitiveness.

• Increased efficiency and productivity are achieved through streamlined operations and resource utilisation.
• Costs are reduced by lowering operational expenses and improving energy efficiency.
• Enhanced quality and consistency lead to better customer satisfaction.
• Increased agility and flexibility allow the business to scale more effectively.
• Better analytics and proactive insights lead to enhanced decision-making.
• Innovation and growth promote creativity, leading to a competitive advantage.

Trialling simple automation provides a wealth of valuable lessons that can pave the way for successful AI implementations. By understanding process requirements, evaluating operational impact, managing change, building a foundational skill set, and gaining stakeholder buy-in, businesses can better prepare for the complexities and challenges associated with AI. This incremental approach helps ensure that AI projects are more likely to succeed, providing maximum value and impact.