Even before the age of AI, humans have always used data to power decision-making.
Whether we’re deciding on what to eat for dinner or what jacket to wear, our innate decision-making process relies on looking at the available information: How hungry am I? What’s the weather like?
As the scale and complexity of decisions increases, say, for deciding whether or not a financial transaction is fraudulent, we need to consider a lot more information. In fact, it’s estimated that there are over 1 billion credit card transactions a day. In today’s big data world, the sheer size and scale of data makes manual analysis methods inadequate.
This is where AI comes into play, which can automatically make decisions at the scale necessary to handle all this information.
Simply put, AI decision-making is using machine learning algorithms to help make decisions. There are many reasons to use AI to help make decisions.
First off, more and more industries are dealing with more and more data than ever before.
Gathering and, more importantly, understanding that data is therefore becoming harder for humans to do alone.
It’s even harder for humans to understand the different interdependencies between data points that contribute to a given outcome.
For example, when trying to detect credit card fraud, there can be nearly 30 different pieces of information that come along with any given transaction. It simply wouldn’t be possible to sort and understand this data manually, especially with the exceptionally high volume of transactions being processed every day.
The idea of “big data”—or high speed, high volume data—impacts a number of other areas of decision-making as well, such as lead scoring, text classification, and forecasting.
Business decision-making relies on manual analysis, intuition, and even past experience, which can all come with various conscious or subconscious biases.
But in an ever-changing world, past experience and intuition can go out of date very quickly.
Humans may not always have access to the latest data, or rather, not understand how the latest data actually reflects an underlying shift in their previously assumed patterns of decision making.
AI can identify patterns, and even more importantly, identify new patterns in data - taking advantage of more data points - much faster than a human can, and therefore help inform strategy changes and optimal decision making.
Ultimately, business executives can use AI to make better decisions, support digital transformation efforts, and aid problem-solving across verticals.
AI can support decision-making wherever there’s data—from sales and marketing to the financial industry.
Marketing is home to many of AI’s top use-cases, as marketing today is more data-heavy than ever. To take advantage of this data - whether it’s gathered from Google Analytics, Facebook Ads, Mailchimp, Salesforce, or any other marketing tool - marketers can use AI.
Sales forecasting means predicting what, when, and how much your customers will purchase.
This helps optimize your production chain and better predict how the market will turn out next, allowing you to focus your business accordingly.
In short, sales forecasting is a great way to be proactive. If sales are projected to increase, you can decide ahead of time how to re-allocate that capital back into the business to compound growth. If sales are projected to decline, you can take steps to change the tides, whether it’s changing your sales incentive structures, increasing your ad budget, or something else entirely.
If you’re selling a wide assortment of products, sales forecasting will also help you predict the winners and losers in your product portfolio, letting you optimize your offerings early on.
Churn is a huge killer of businesses.It’s important to remember that if growth doesn’t outpace churn, your business is headed towards failure.
However, there are countless potential causes of churn, and different users have vastly different likelihoods of churning.
With AI-powered churn prediction, you can accurately predict which customers are likely to disengage from your product or leave for a competitor, letting you work out whether it’s worth spending the money re-engaging them.
This will also help you find the ideal customer profile to target in future marketing efforts.
Let’s face it: business success hinges on focusing on the right leads. If equal attention is given to all leads, then your sales team is destined to lose time and opportunities.
Some leads are a waste of time, while other leads can seriously move the needle.
Often, high-value opportunities require high-touch sales efforts, and the only way to separate the wheat from the chaff is by looking at the data.
Rather than burden your sales team with the additional task of scoring leads based on their probability of conversion, you can use AI to do this automatically.
Not only will the automation of lead scoring improve your sales conversion rates, but it’ll enable you to adjust your marketing funnel and spending quickly and efficiently, rather than needing to wait for customers to go through the entire funnel.
This is particularly useful for things like predicting how well a given PPC campaign will work, before risking your entire budget on something that may not pan out.
Logistics and transportation companies are always looking for ways to improve their services, reduce costs, and better serve their customers. While many companies have successfully turned to technology and analytics to address these goals, others are still relying on more traditional methods.
In recent years, artificial intelligence has been introduced as a solution to save costs and time-consuming human procedures.
Predictive analytics technology can optimize shipping routes by calculating the most effective order of shipment. It can also calculate the most efficient transport routes by taking into account factors such as fuel consumption, time spent in transit, and driver hours.
While 90% of logistics firms expect AI to improve their market position, more than half lack the know-how to implement AI. Fortunately, no-code tools like Akkio make it effortless for anyone to build and deploy AI systems.
Healthcare is seeing an exponential increase in technological innovation. 90% of hospitals already have an AI strategy.
Lately, artificial intelligence has been making strides in analyzing diagnostic data points and understanding possible underlying causes, thus helping to diagnose patients more quickly.
Further, healthcare providers are often inundated with patient visits, making it difficult for them to provide everyone the care they need when they need it.
Artificial intelligence is being used to address this problem by helping doctors and nurses triage and diagnose patients and decide whether or not they need to see a GP, a specialist, or another medical professional.
Retailers use customer feedback to make informed decisions about their products and services.
There are many ways of gathering this feedback, but one popular method is through social media. This information can help retailers adjust their marketing strategy or create new products.
Artificial intelligence has been developed to sift through social media data and find out what consumers want in a product that the company doesn't offer yet. It is an inexpensive way for companies to get a feel for what they need to produce and how they need to put it together.
Another common application of AI in retail is intelligent customer relationship management.
A retailer could use CRM software to identify customers' interests and target them with customized promotions or content. It could also be used to analyze past purchasing behavior and offer suggestions for personalized shopping lists.
In short, AI can help retailers tailor their offers and messaging to individual customers, which can be a valuable tool in the competitive retail environment. By offering more personalized, tailored services, customer experience will also benefit.
The retail industry is somewhat behind in terms of AI adoption, with just over one-quarter using AI, which means that retailers adopting AI today have a huge opportunity to build competitive advantages.
Energy production and consumption is a complicated endeavor, with a myriad of variables to consider. Artificial intelligence can use datasets about these aspects as well as others to predict demand and help decision-makers.
AI can use data from previous years of production and consumption, as well as other sources like real-time usage and weather forecasts, to predict future needs.
As the World Bank’s International Finance Corporation writes, AI can help solve the energy industry’s growing challenges related to efficiency issues, by using data from smart grids, smart meters, and Internet of Things devices.
AI can even help with the adoption of renewable energy by improving the planning, operation, and control of the power system.
In short, AI can help optimize operations in the energy sector because it's able to rapidly analyze large amounts of data quickly and accurately.
By 2023, financial fraud is expected to be a $63 billion problem in the United States alone.
Artificial Intelligence for financial fraud detection looks for anomalies in transactions and flags them as suspicious. It's not an easy task for humans to do this, but for an AI, it's much more straightforward.
The AI will look at patterns of activity across different accounts as well as credit history to determine whether or not the transaction is fraudulent.
The vast majority of financial services experts recognize that AI applications can help reduce payment fraud. With Akkio, financial teams can build and deploy fraud detection models, with no technical expertise needed.
We can't stop all fraud, but we can certainly take steps to reduce it by using AI technologies like deep learning.
Risk assessment is another area where artificial intelligence can have a tremendous impact.
One area where AI is already being used to great effect is in insurance. A typical insurer will have data on dozens or even hundreds of different aspects of their customers that predict the likelihood of them making a claim against their policy.
The insurer can then take all this data and use predictive modeling to assess how likely it is that the customer will make a claim against their policy.
Using this system, they can adjust their premiums to ensure they don't lose too much money but still offer cheap insurance rates to the people who are less likely to make a claim.
By using AI, they can analyze far more data points and far faster than any human could, making data science a powerful decision support tool.
As Deloitte reports, the insurance industry is dramatically falling behind in terms of AI adoption, but this presents a great opportunity for savvy insurers to get ahead of their competition.
The phrase “AI decision-making” is a bit of a misnomer. While AI models can provide a compelling reason to make a certain decision, that decision often needs to be supported by a trained professional with experience.
This is particularly the case with high-stakes AI use-cases like medical diagnoses.
While everyday AI models like churn prediction or lead scoring can be done automatically, complex tasks like stroke prediction still require human decisions, including from data scientists, as well as potentially other solutions like expert systems.
AI decision-making, therefore, isn’t always a silver bullet. Suppose that you wanted to start a FinTech startup that was 100% fraud-proof, but you had zero financial experience. AI wouldn’t magically make your startup successful.
Where AI does shine is in harnessing existing experience and taking it to the next level, allowing experts to spend more time on the issues that matter and less on tiresome data entry and ground-level analysis.
Ultimately, implementing AI helps with “data-driven” decision-making. You need to have a good understanding of what you need to solve, and the capabilities you can deploy to solve and understand it.
Traditionally, building and deploy AI models was an arduous, manual process, requiring computer science and data analytics experts.
Akkio automates these tasks and makes the development process faster and more reliable. Businesses can access our no-code AI toolkit to handle all of the grunt work, and improve their decision-making with accurate predictive models.
Our solution ecosystem uses neural networks, natural language processing, and other optimization-based AI technologies to solve real-world problems.
Humans make thousands of decisions a day, from the clothes we wear to our dinner choices. When it comes to making business decisions, a lot of different factors come into play, which makes manual decision-making slow and ineffective. AI can help by making smarter and faster decisions than humans can so that businesses can focus on more creative, client-oriented work.
AI can be used for marketing tasks like lead scoring and churn prediction, to financial tasks like fraud detection, and even HR tasks like predicting and preventing employee attrition.
With Akkio, businesses can create models for any of these tasks, test them, and make predictions in a fraction of the time it would take to do it by hand. This means that businesses can use more of their time to focus on their core strengths.