Understanding the business impact of AI

Understanding the business impact of AI

Even though the impact of artificial intelligence (AI) is one that provides businesses with significant short-term benefits, it can also ensure a vital competitive advantage. While reducing costs and increasing profits are key drivers for applying the technology, AI also provides for new business approaches. When the AI recommendation engine of Amazon launched in the 90s, it offered online shoppers something truly unique in suggesting potential items based on their purchase history. Today, no online store or content streaming site can afford not to have a recommendation engine. However, Amazon gained a massive competitive advantage from it initially. 

Business sense

There are literally unlimited business cases for AI, but like any other technology innovation, the excitement must be tempered with a cost benefit analysis. If AI does not solve a specific business problem that results in increased value somewhere, then there is not a case for it. 

Microsoft talk about four areas of digital transformation; empowering employees, engaging customers, optimising operations and transforming products. AI can help in all these areas. For empowering employees, AI can use image recognition to automatically convert photographed receipts into payroll expense claims. From a customer engagement perspective, AI can recommend additional products that are appropriate for an existing basket at either an online store, or at point-of-sale at a traditional shop. 

When it comes to optimising operations, AI can be used for predictive maintenance to proactively monitor machines and predict failures before they occur, saving costly production line issues, as well as freeing up capital by avoiding stockpiling unnecessary spares. For transforming products, AI is often used to help companies move from selling products to offering a service, such as the often-mentioned case of General Electric moving from selling turbines to offering an always-on electricity generation service – and predictive maintenance also plays a part in this. 

App-driven

Already, people use mobile apps that embed AI capabilities in their daily lives without even realising it. For example, Google Maps or Waze automatically reroute drivers as traffic congestion changes, and banks SMS client’s seconds after an unusual credit card transaction occurs. 

Even classic business apps are being enhanced with AI-based functionality. For example, Microsoft Excel has been enhanced with AI capabilities to import a spreadsheet from a photo taken from a mobile device. Chatbots, cybersecurity, anti-spam software and marketing-related solutions (such as targeted adverts) are probably the most-used AI applications in business today. 

However, the apps that are currently catching people’s attention are self-driving vehicles, tumour-identifying algorithms, and collaborative intelligence that allow robots to work alongside human workers on the manufacturing shop floor. 

Process for change

With so many vendors and service providers offering AI services and solutions, companies can evaluate the quality by putting themselves in the shoes of the customer. If the AI enhancement improves the customer experience in some way, then it is good. However, if the AI model used does not learn from its mistakes and continually asks a customer if they want a specific service then it should best be avoided. 

And when it comes to effectively implementing AI-led solutions, decision-makers must first decide on what the business goals are. Once done, they need to examine the various role players that will be involved in the process both internally and externally. 

A company must also get executive sponsorship from the top. Budget allocation and technology choices are imperative to align to the existing organisational solution architecture and product stack. 

These are all considered basic steps before getting to the more complex aspects of implementation. For example, the business must select the top five impact versus feasibility questions that need to be solved. Over time, a methodology will be required that enables the organisation to more easily identify the next priorities. 

Overcoming obstacles

The biggest problem that organisations seem to have when implementing AI is often the deployment of the answer into a production scenario. It is one thing to find an insight that could change the business from a data science perspective but turning that into an AI-powered process along with the operational KPI that supports it, is another thing entirely. 

So, a business needs a process to bring organisational change management into the mix. Of course, AI can help with this by digitally gathering as much data as possible on people using the new AI solutions. This will enable the company to use AI models to pinpoint where champions (or protesters) are forming, and feed that back into the change loop. 

Irrespective, AI is something that must be embraced if a business is to remain competitive in the digital business environment. 

This article was originally published in Business Day Focus 4.0.

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