It’s incredible how artificial intelligence has drastically changed the way we experience technology. To say that it makes life easier is an understatement. Some may not be aware of it, but AI has become a part of everybody’s life. Here is a close look at how artificial intelligence helps in decision-making.
Those who have Amazon Echo or Google Home in their houses know how convenient it is to have these AI-powered devices, especially given their ability and accuracy. AI can seamlessly process voice commands and execute them or deliver results during voice searches and improve customer experience.
AI and machine learning statistics
If you want to know more about how substantial artificial intelligence has become, check out these statistics below.
- Voice assistants like Siri, Echo, and more have grown in popularity so much that 97% of mobile users are said to use them.
- Because of the competitive advantage, artificial intelligence brings, 80% of organizations plan to use AI for customer service.
- 61% of marketers claim that AI is a critical element of their data strategy.
- 65% of companies that plan to adopt machine learning (ML) believe it can help business decision-making.
- The bank system will automate up to 90% of its customer interactions using chatbots by 2022.
A closer look at AI’s decision-making capabilities
Before answering whether or not you can trust AI in making decisions, especially when the stakes are high, you must first understand what artificial intelligence is capable of now and know AI benefits and risks.
1. AI handles multiple inputs better
Compared to humans, machines are more reliable when handling different factors, all at the same point when making complex decisions. Machines can control and process vast amounts of data and deliver valuable insights in a matter of minutes, a task that would take humans much longer to accomplish.
2. Speed decision-making processes
Everything is always moving at an accelerated speed, no matter the field or location. Whether in eCommerce or other industries, you can use dynamic pricing and see how AI can optimize your margins.
4. Detect patterns
Buying patterns may not be that easy to detect when doing human analysis. AI-powered analyses can spot such patterns and impact businesses positively during the discovery of these patterns.
When you can better understand a customer’s buying pattern, you can align your products based on those patterns that show the customers’ needs. Even simpler predictive tools can easily outperform humans in this aspect, and there are predictions for AI being the future of growth hacking.
5. Algorithms are immune to decision fatigue
Unlike individuals who get tired after hours of processing data and making many decisions, you won’t have to deal with this concern when using AI.
As they’re capable of decisive decisions over a long period without tiring, the quality of the decisions made is not compromised. Businesses can mitigate the risk of being exposed to poor decisions caused by exhaustion.
What challenges are there with trusting AI decisions?
What is known now is that AI is already deeply integrated into many aspects of our lives. It’s imperfect and can still be prone to errors, especially when fed with the wrong information or insufficient training data. That said, here are some of the challenges that AI faces today.
1. Human values
As AI becomes more capable, the concern about whether humans can trust its “human values” grows. People were excited about the idea of autonomous cars until their decision-making process was brought into question how autonomous cars could deal with challenging and complex situations.
Say a truck is coming at a dangerous speed. If a driver swerves, this could result in a catastrophic accident.
- What would the autonomous car do?
- How will it arrive at a decision?
It is a complicated issue. Ultimately, the programmer’s bias could be a significant determining factor, and it is this bias can quickly erode people’s trust in AI decisions.
2. Transparency
Transparency is vital to trust. And until things are fully transparent, there will always be trust issues, and that’s how it has always been in organizations and businesses.
People will always want to know the hows and whys, requiring the same from AI systems. It’s fantastic and impressive that AI systems can arrive at certain conclusions and even provide personalized recommendations. Still, there will always be concerns as they cannot (for now) explain how they can derive a particular outcome.
In the military field, where the stakes are high, the focus has been on the issue of trust as well. It is perhaps why Defense Advanced Research Projects Agency (DARPA ) launched several projects aiming to find ways to explain, as close to how humans would, how an AI reaches a specific conclusion.
Another one aims to make their AI machines more like trusted partners to assess their performance and give accurate reports on the conditions that they do or do not do well.
3. Accuracy autonomy
AI makes decisions based on the predictions they have. In most cases, AI systems’ decisions are accurate enough when 95% or higher. That’s impressive and indeed good enough for essential daily AI uses, but that would be entirely different when it’s for high-stake situations. Should machines be given high-stake autonomy?
5 ways to drive better AI decision-making processes
AI can handle tedious tasks now, freeing time for employees to focus on more important responsibilities. But considering all their capabilities and the benefits that come with AI, would it be wise to trust AI decisions when the stakes are high? Perhaps only time can answer this. Here’s how AI can help drive better decision-making.
1. Customize AI to address specific needs
Artificial intelligence is yet to be a reality. Technical teams in charge of designing AI should collaborate with those who know and understand what the AI process would mean for the organization.
One crucial mistake is focusing solely on making the technology and the algorithms more advanced and losing sight of the needs of those who will use the generated insights. So don’t lose sight of the user’s goal when designing an AI system.
2. Formulate organizational data exchanges
AI’s oxygen is data, and they base their decisions on all the data. Most organizations have their IT infrastructures built by some distinct people or even different teams over the years.
As a result, this creates a fragmented landscape of data that has nothing to do with each other. It would work better if the entire organization had a more unified data architecture to improve its AI.
3. Create strategic partnerships
To make the most of AI trends in eCommerce, you have to work and partner with providers already proven knowledgeable when navigating the AI system from design to shipment.
These should be people with technical know-how skills and business understanding who can utilize AI in decision-making which can generate positive effects to your business and help you get through any hurdles along the way.
4. Focus on training
AI decisions are just as good as the data used to train its system, and organizations must unify the data to feed the AI system. On top of that, you should be mindful of the quality of data you provide to avoid bias.
As opposed to just sticking to data applicable to the majority of the population, including data from minority sectors to get a complete data representation to help address concerns on accuracy and inclusion.
5. Monitor AI regulations
Don’t ultimately strike out the idea of an AI “watchdog” that could supervise, regulate, or scrutinize audit algorithms, and this is especially helpful if there’s even the tiniest possibility of bias.
There have been previous instances when people got on the wrong side of a flawed AI system, and while some were just cases of nuisance. For others, the stakes were higher.
Some people lost jobs, were detained, and lost opportunities because of AI errors. Hence, third-party regulators challenge AI decisions. If you are new to this technology, you can learn more from experts about AI regulations from podcasts such as AI Nation, CNA, and Apple.
