By Heiko Claussen
Implementing Industrial AI offers significant opportunities for operational efficiency and cost savings, but it can also present challenges. This article outlines six best practices for innovators, including prioritising customer needs, embracing an innovation funnel, and balancing AI innovation with practical application. It concludes that combining AI with human expertise is key to achieving long-term success.
The integration of industrial AI into an organisation presents significant opportunities for operational efficiency, cost savings, and even environmental benefits. However, while the benefits are clear, the implementation process can be challenging for innovators looking to adopt this technology successfully. As organisations increasingly evaluate the potential of Industrial AI, it is essential to follow best practices to avoid common pitfalls and ensure a smooth transition.
1. Prioritising the customer
One of the most critical aspects of industrial AI implementation is understanding that all innovation should ultimately benefit the customer. Instead of developing new AI technologies without a clear outcome in mind, organisations should focus on what matters most to their customers. In other words, working backwards from the customer allows them to create solutions that have direct, tangible benefits.
This customer-centric approach means examining customer pain points and then using AI to solve those issues. For instance, in asset-intensive industries, predictive maintenance is a game-changer. AI can predict equipment failures well in advance, reducing costly downtimes and enhancing reliability. By focusing on the customer’s needs, you ensure that your AI initiatives are relevant and impactful.
2. Embracing the innovation funnel
One common mistake innovators make is betting everything on a single, high-risk idea. While it’s essential to invest in groundbreaking technologies, it’s equally important to mitigate risks by diversifying your innovation efforts. This is where the concept of an “innovation funnel” comes in.
An innovation funnel allows organisations to evaluate multiple AI ideas at different stages of development. Instead of committing significant resources to a single initiative, you can track the progress of various projects, update risk assessments, and re-evaluate plans as necessary. This helps avoid the “all or nothing” approach, ensuring your company remains agile and can pivot if needed.
The funnel also helps identify when to stop projects that aren’t delivering expected value as it allows for tracking the progress over time and can thus be used to trigger stakeholder gate reviews. Not every idea will be a success, but having a process in place for evaluating them ensures that you make informed decisions.
3. Beware of the “Valley of Death”
A significant challenge in innovation is navigating the “valley of death” – the gap between a research proof of concept and successful commercialisation. Many great ideas fall into this void due to a lack of stakeholder involvement or unclear ownership.
To avoid this, involve all relevant stakeholders from the beginning. This includes technical teams like R&D as well as departments like cybersecurity, product management and leadership. After all, you can’t simply throw things over the fence and expect them to succeed. Everyone must agree on priorities, and there should be clear ownership in each stakeholder group.
When stakeholders are not aligned, innovative ideas can stall or fail due to conflicting priorities. By ensuring that everyone is on board early, you create a collaborative environment that is more conducive to success.
4. Appoint clear owners for each stage of the process
Another critical best practice is ensuring that each stakeholder group has a clear owner responsible for moving the project forward. Whether it’s technical details, business strategies, or compliance concerns, each part of the AI implementation process requires dedicated leadership.
Ownership is very important here. Organisations need owners who feel responsible not just for their piece of the puzzle but for the entire project. These owners should manage the day-to-day aspects of the project and act as champions for the initiative within their teams.
5. Balancing AI innovation with practical application
It’s easy to get excited about cutting-edge AI, but not every new development is suitable for your use case. Every algorithm has their advantages and disadvantages and overcomplicating the process with the latest algorithms can often backfire. In some cases, simpler solutions yield better results.
In line with this, it is important to be thoughtful and avoid complex AI models like deep learning or reinforcement learning in industrial applications where simpler e.g., linear relationships are involved. The newest, most complex models are not always the best choice. In many cases, simpler models that are more data efficient, explainable, extrapolate better to new regimes and provide robust results without the risk of overfitting or other complications.
The key is to focus on practical applications of AI that solve real problems within your organisation. Sometimes, sticking with well-established methods and technologies is more effective and scalable.
6. Combining AI with human expertise
Although AI can process vast amounts of data and generate valuable insights, human expertise is crucial for interpreting those insights and making strategic decisions. AI is a powerful tool, but it is not a replacement for human judgment and in many applications, it is key that operators stay in control.
Organisations implementing Industrial AI should have the right mix of technical experts, industry veterans, and operations professionals to oversee the AI initiatives. This collaborative approach ensures that AI outputs are not only accurate but also practical and aligned with broader business goals.
Furthermore, engaging external experts with experience in Industrial AI implementations can significantly enhance an organisation’s ability to deploy AI effectively. These experts bring valuable insights into best practices, identify the most promising applications, and help guide the scaling of AI initiatives across the company.
Setting the stage for success with industrial AI
The journey toward implementing Industrial AI is full of potential but also presents challenges. By prioritising the customer, embracing an innovation funnel, involving all stakeholders, appointing clear owners, and balancing innovation with practical application, organisations can navigate these challenges successfully.
Ultimately, combining AI with human expertise creates a framework for driving efficiency, innovation, and long-term success. As more companies look to capitalise on the benefits of Industrial AI, following these best practices is essential to ensuring an effective implementation process that delivers real value.
