Decision intelligence is currently attracting plenty of investment around the world. But why? Read on to discover more about this burgeoning AI-powered strategic tool
In our hyper-connected digital world, it’s hard to get through a single day without hearing the word ‘data’ at some point. Whether it’s being bought, sold, stolen, or protected, one thing’s for sure – everyone wants it. And why wouldn’t they? More data means more knowledge and more knowledge means more power.
By 2025, it is projected that there will be a staggering 181 zettabytes of data created, captured, copied, and consumed around the world. And it’s only when you consider that one zettabyte is equal to a trillion gigabytes that you start to appreciate the seemingly endless number of decisions this amount of data could inform.
Increasing access to these ever-growing oceans of information has prompted many organizations to change the way they do business and adopt a digital data-led strategy – and this has given rise to the most modern form of business intelligence (BI).
What is business intelligence?
Modern business intelligence (BI) describes the process of using technology to collect, analyze, and display digital data in a digestible format, before using these analytics to make better-informed business decisions.
The BI process is widespread throughout the business world. In the 2020 Global State of Enterprise Analytics report from analytics software provider MicroStrategy, for example, 94% of respondents said data analytics is important to their business growth.
The same report showed just how useful BI is proving to be for the organizations that are able to use it. 64% of respondents said that the use of analytics had improved efficiency and productivity, with 56% saying that it had led to faster and more effective decision making.
What’s the problem with business intelligence?
While it’s one thing to gather and view this data, understanding and manipulating it, then presenting it in a way that a layperson can understand, is another.
This gap in technical know-how, combined with a lack of data culture within businesses, creates significant issues around accessibility. The aforementioned report also showed that while 80% of managers have access to data and analytics, only 52% of front-line workers can say the same.
Furthermore, a survey conducted by New Vantage Partners (NVP) in 2021 found that 92.2% of respondents identified people, business processes, and culture as the principal challenges to becoming data-driven.
Without company-wide visibility and accessibility, data can become siloed within departments, which can limit a business’s overall view and hinder its agility when reacting to changes in the industry.
So how can businesses harness the true power of big data quickly and effectively enough to respond to trends as they happen – and even predict future market behavior – without the need for a data scientist? The answer is artificial intelligence in decision making, otherwise known as decision intelligence.
What is decision intelligence?
Decision intelligence describes the concept of applying artificial intelligence (AI) to available data for the purpose of making predictions, recommendations, and decisions.
While business intelligence concerns the use of technology to gather, process, and present data, which is then used to inform a decision made by a human, decision intelligence goes one step further by also using technology to provide recommendations and predictions, and even make the decisions themselves.
How does decision intelligence work?
In a business context, the decision intelligence process works by feeding all available data into a central AI-powered application. The more extensive and diverse this data is, the more accurate and reliable the outcome will be. So, for example, information should not only come from all departments, but it should include both transactional and behavioral data, and, where possible, be sourced internally and externally.
The decision intelligence application uses this collected data to consider possible scenarios and, in turn, construct actions and alternatives in the context of the business’s overall capabilities.
Suggested actions are then presented for decision makers to act on as they wish. This process avoids the compartmentalization of data within different areas of a business and ensures that even the smallest decisions are made in a company-wide and even industry-wide context.
Furthermore, the decision intelligence application then tracks the outcome of decisions that have been made, learning from consequences and using that knowledge to inform future modeling.
In describing its own product, the following video from decision intelligence provider Peak capably explains the overall concept in simple terms.
Why use decision intelligence?
As the digital world becomes increasingly interconnected, it grows in unpredictability and complication. This more complex business landscape creates tougher business decisions, so automating part of the decision-making process helps in two main ways.
Efficiency
The volume of data that companies can gather has become so great that using it to make well-informed decisions by traditional methods is inefficient.
By the time the information has been presented and considered by decision makers with busy schedules, hours, days, or weeks could have passed. This is far too long in today’s agile business environment, where staying ahead of the competition often means making key decisions in minutes or even seconds.
Decision intelligence can provide recommendations in an instant, even from the most comprehensive big-data sets. This combination of speed, accuracy, and scalability allows companies to be agile enough to react to market trends in real time.
Democratization of analytics
The complexity of data analytics can make it somewhat inaccessible to less tech-savvy people, but many key decisions are made by hands-on managers who are rarely exposed to such intricate technical information.
For example, the Global State of Enterprise Analytics report found that 79% of employees that aren’t adept at analytics have to ask their IT department or a business analyst for help when making a data-led decision. Furthermore, 61% of less-data-skilled employees wait a few hours, a few days, or longer to get the data they need.
This data skills gap is a common issue among businesses and has been recognized by the UK Government in a 2021 policy paper, which estimates that the problem led to the UK data economy realizing only around half of its full potential in 2016.
Decision intelligence changes this. By using AI to crunch the numbers and formulate recommendations, unskilled workers no longer need to be exposed to such confusing data. With all predictions and guidance presented to them in an easily understood format, they can bypass time-consuming consultations with analysts and act immediately.
What can decision intelligence be used for?
As long there is enough data to work from, decision intelligence can make recommendations on any decision, from the color of a worker’s uniform to a multi-billion-dollar buyout. Here are a few examples of how it can help businesses.
Decision intelligence in user experience (UX)
With an increasing number of businesses operating solely online, the importance of user experience (UX) has never been greater. Once a user arrives on a website, the owner of that website wants to keep them there. However, with many big e-commerce websites or content providers serving millions of users every day, it’s impossible to manage them all personally.
That’s where decision intelligence can help. By gathering first-party data from these users as they navigate a website, AI-powered recommendation engines can make UX decisions based on activity. This could be which article to read next, which songs appear on a playlist, product suggestions or something else.
Decision intelligence in sales
By processing historical sales data, decision intelligence can be useful in sales optimization. Deal timelines, conversion rates, and even likely sales revenue can be predicted based on past results.
Data on consumer behavior can also be assessed to help decide the most effective sales techniques for individual prospects, as well as identifying the most promising sales leads and performing risk assessments among other things.
Decision intelligence in HR
In the most basic instance, decision intelligence in HR can be used to streamline the recruiting process, using AI to assess resumes submitted for a specific role.
It can, however, go further than simply matching job descriptions to skill sets. Other examples include assessing a candidate’s suitability to company culture; measuring workforce performance to decide on training requirements and project allocation; and even evaluating different recruitment methods for efficiency and value for money.
Decision intelligence in supply chain and logistics
It’s never been more important for businesses to ensure supply chains are fully optimized. Decision intelligence can use market data to forecast demand and make complex decisions in capacity planning, helping to ensure maximum productivity.
It can also be used to improve all aspects of delivery. Examples could be streamlining warehouse operations for smooth dispatch processes, or planning and optimizing delivery routes, redirecting drivers in real time to avoid delays.
Decision intelligence companies
Decision intelligence tools are available from a growing number of providers, including IBM, Quantellia, Busigence, and Oracle Business Intelligence, all of which offer AI decision-making software.
The power of the technology is also by no means lost on Google. In 2019, the tech giant appointed Cassie Kozyrkov as its Chief Decision Scientist, who in her own words is on a mission to “democratize decision intelligence and safe, reliable AI.”
Additionally, the previously mentioned startup Peak boasts a diverse client list that includes the likes of AO World and GlaxoSmithKline.
The problem with decision intelligence
Decision intelligence is only as good as the data it can utilize. This means it is likely to widen the gap between the already dominant big-tech players with eye-watering amounts of information at their disposal, and the smaller organizations that do not have access to such diverse data sets.
Additionally, the potential for artificial intelligence to completely replace executive decision making in the future is strong. This is likely to be cost-effective for businesses, although it would have an unavoidable negative impact on global employment.
Finally, in a world where AI is actively making key operational decisions about how a business is run, there could be something else to consider. Humans base decisions on facts and experience as far as possible, but cognitive bias and emotion are typically factors too. While many decision intelligence models do incorporate social science, this finely nuanced emotional decision making is something machines are lacking.
Indeed, the best decisions are often made with a cool, rational head – but many successful business leaders might argue that their ability to act on gut instinct is what has given them the edge.
The future of decision intelligence
The NVP survey mentioned above found that 99% of respondents invested in big data and AI in 2021, with 62% spending $50m and above. Furthermore, in its Top Strategic Technology Trends for 2022 report, Gartner predicts that by 2023 more than a third of large organizations will have analysts practicing decision intelligence.
With decision intelligence set to become so much more commonplace, there’s no doubt data is more valuable than ever. With such importance placed on comprehensive data sets, it’s time for businesses to look for innovative ways to gather information on consumer behaviors.
New technologies such as image streaming can provide valuable analytics for businesses who want to learn more about the way people interact with their content. By acting now to establish new data streams, businesses can ensure they are well-placed to harness the power of decision intelligence as the technology becomes more widely accessible.
Learn how SmartFrame’s Insights can provide valuable information about how your users interact with your online content.
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