Alternatives to modern AI approaches including machine learning include rule based systems and manual labor. AI systems can surpass both alternatives in terms of cost, speed and effectiveness in many areas.
Rule based systems are better than modern AI solutions where it is possible to formulate a fixed set of rules to solve a problem/make the optimal decision.
Manual labor is more preferable to modern AI solutions where AI solutions do not yield a sufficient ROI. This is likely to happen in cases where
- humans have lower error rate and errors are costly
- process completion speed is not important
- process costs are so low that AI investment does not make sense
In business, three metrics are important: speed, cost and effectiveness (quality of the result). We classified AI’s advantages according to these buckets:
Faster process completion
Speed is crucial, especially in customer facing processes. Machines enable faster processes by completing tasks faster and by working 24/7.
Unlike humans, machines can work without any breaks. Therefore, for tasks that require long working hours, these agents can continuously work to finish their tasks. Thus, machines and AI-powered agents would be more preferable than humans for such jobs.
Manufacturing systems are an example which can make use of this advantage. Fully automated smart factories would accelerate and provide continuous production. Ericsson will run the first one in early 2020. This will enable Ericsson to run its businesses without any breaks.
Most AI models are almost free to run once they are built. So they are cheaper than manual labor at scale.
Despite low cost to run, AI systems can be costly where they introduce more errors than alternatives and where cost of errors are significant. Thankfully, in most business processes, errors already exist and are tolerated, making AI-powered systems economically feasible.
Individuals can make quite accurate personalized suggestions but manual suggestions are costly and time consuming. AI-powered alternatives are faster and more cost effective.
AI agents can collect their users’ information to serve them personally. service. Most popular examples are from mobile applications, social media, and e-commerce industries. Businesses can track their customers’ preferences and make accurate recommendations for more transactions. These accurate recommendations bring more profit and improve customer satisfaction, as the users enjoy a personalized experience.
As an example, Netflix uses AI algorithms to find shows that its users haven’t seen before based on their content history. As a result, Netflix’s vice president of product innovation Todd Yellin indicates that 80% of watched contents come from these recommendations.
Overcoming hazardous conditions
AI + robotics unlocks the most significant savings as machines can take over tasks which are harmful for humans.
Some tasks can be dangerous for humans to perform. This may cause health issues for humans and prevent them from performing these tasks properly. Yet, machines can handle these tasks without difficulty. Thus, using machine intelligence is more preferable than humans to complete such tasks.
Space research is one use case for this advantage. Since machines have metal bodies, they are more resistant and have a greater ability to endure the space and hostile atmosphere. This helps NASA to analyze the space environment more precisely. As another example, a company called Diakont inspects fuel tanks to prevent any irregularities like leakages with AI.
AI can exceed human capabilities in several areas through error reduction or quality increase:
Companies require many processes that include repetitive tasks. These tasks are usually tiresome for humans, but not for AI. The machine intelligence can easily handle these tasks fast since these jobs don’t require high-level skills. They also perform multi-tasking to obtain the best results. The automation of these tasks provides workers to do higher-skill tasks and brings higher profits to the company.
PwC estimates a potential contribution of $15.7 trillion by 2030, as firms aim to improve labor productivity with AI technologies by task automation. This number means that global GDP can be 14% higher in 2030 as a result of AI.
Other error reductions
Manual interferences can always cause errors in business processes. As humans are prone to making errors, AI-powered tools can be a powerful solution to reduce the possibility of such errors. AI can automate certain tasks in businesses and handle them without any distraction. This enables error rates to decrease and saves companies from additional costs that are caused by these errors. According to a study from GE Digital and sponsored by ServiceMax, 23% of unplanned downtime of the processes are caused by human errors.
AI can improve the capabilities of the current processes. With the application of AI tools, processes can give more insights to companies and provide a better user experience.
Companies can use AI for forecasting tools. To predict the demand or future financial budgets, AI-powered algorithms are powerful solutions and can beat manual estimates. As this technology uses sales data and financial information, it makes accurate predictions and helps companies for better decision-making.
A houseware retailer in Australia had used AutoML tools to forecast the demand and adjust its prices accordingly. As the company had reach greater than 90% accurate forecasts, it achieved a 23% shelf gap reduction in its stores.
You can read our related article to have more information about AutoML tools.
Companies can use AI to optimize their processes and enhance their performance. AI can understand the bottlenecks in the processes and find ways to reconstruct their processes for more efficient work. To have more information, you can read our article about process mining, a subfield that benefits from AI algorithms.
Alibaba had benefited from AI algorithms to create more efficient delivery routes. This provided them with a 10%-decrease in-vehicle use and a 30% reduction in travel distances.
Whether to adopt an AI solution is a decision that needs to be revisited
While your analysis today may indicate that a process is not feasible to automate today, that may not be true in 1 year. As the advance of digital photography demonstrated, a system which can not be improved dramatically (i.e. manual labor) is no match for a digital system capable of orders of magnitude of improvement.
First the cost of the rapidly improving service rapidly drops:
And once it beats the slowly improving alternative, the whole industry switches:
If you wonder what AI technologies wait for us in the future, feel free to read our article about the future of AI technologies.
You can also read how you can apply AI to a mobile application from this article.
To learn about AI applications by industry, you can also take a look at our AI in business article.
For general use cases of AI, feel free to read our top AI use cases/applications article.