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Mind and the Machine: The Age of Digital Decisions

The Change Agent

AI and machine-learning technologies are transforming business intelligence platforms into powerful tools that optimise the decision-making process, create agility and drive the business forward.

Understanding Decision Intelligence

It’s estimated that the average adult makes more than 35,000 decisions per day. That may sound absurd, but according to a Cornell University study, we make nearly 230 decisions each day on food alone. When you factor in all of the subconscious decisions about what to eat, what to wear or what to watch on TV, those decisions quickly add up. That doesn’t even consider the cause and effect of those decisions that splinter into hundreds of microdecisions. The bottom line is that we are faced with a massive number of decision points every day.

For executive leaders, the number of decisions is likely higher, as they make decisions that shape company strategy, culture and vision. When do they pivot the business in response to disruption? How do they activate their digital transformation strategy? Often, those decisions must be made quickly, all while considering numerous potential outcomes and balancing risk with opportunity. But what are the components of such a decision? Peter Drucker said, “effective executives know that decision making has its own systemic process and its own clearly defined elements.”

Mind and the machine the age of decision intelligence

In a Harvard Business Review article, Drucker defined six steps in the decision-making process including:

  1. Classifying the problem
  2. Defining the problem
  3. Specifying the answer to the problem
  4. Deciding what is “right,” rather than what is acceptable, in order to meet the boundary conditions
  5. Building into the decision the action to carry it out 
  6. Testing the validity and effectiveness of the decision against the actual course of events

Drucker’s process has stood the test of time and is as relevant today as it was in 1967 when he penned the article. The only thing missing from his framework is data. That’s understandable since Drucker penned those steps before technologies such as artificial intelligence and data analytics were as pervasive as they are today. What does that mean for executive decision-makers today?

For years, organisations have leveraged business intelligence dashboards to help users make data-driven decisions. Unfortunately, often the analytics platforms are chosen to fit the data rather than leading with what the company is trying to solve for. Even with those tools, the user is left to interpret the data and draw their own conclusions. The sheer volume of data and lack of context provided can lead to poor decisions and less than ideal outcomes. That’s where decision intelligence comes in.

Decision intelligence is a subset of artificial intelligence that focuses on tangible business outcomes, combining human and machine inputs to arrive at the most desirable conclusions. By incorporating AI and machine-learning technologies, organisations are transforming their business intelligence platforms into powerful tools that optimise the decision-making process, create agility and drive the business forward.

59% of organisations have fully adopted AI and machine learning tools.1

Mind and the machine the age of decision intelligence

Our Perspective

How Decision Intelligence Combines AI, ML and Human Elements for Holistic Decision-Making

Data powers and informs future-facing solutions and helps organisations save money, increase revenue, reduce risk and improve the customer experience. Industry leaders across financial services, telecommunications, healthcare, retail and many others apply data analytics solutions to get the right data to the right people at the right time to make informed decisions. The problem is that once you’ve collected, mined and analysed the data, you’re tethered to that rearview data process, which defines the decision-making. And the outcomes are predicated on decisions made by an end user, who likely lacks formal training in data analysis and has a limited line of sight into other business units. For example, the marketing team fires up their BI dashboard and the data reveals the best performing content. Marketing and sales use the data to launch a new customer engagement campaign. But if the procurement and fulfillment units are unable to fill customer orders, the campaign backfires, leading to frustrated teams and worse, unsatisfied customers.

By 2023, more than a third of large organisations will have analysts practicing decision intelligence.2

Decision intelligence generates a holistic view of the business, removing the need to validate information or track down different stakeholders before making a decision. Through the decision intelligence mechanism, underpinned by AI, you can now look forward, backward and sideways. Every potential action is considered within the context of the expected outcomes, augmenting the human elements to help make faster, more accurate decisions. The AI models augment the decision-making process, putting the power of AI into the hands of the business users.

That all sounds great, so why isn’t that every company’s reality? Ten or 15 years ago, organisations were bound by the limitations of the technology. Business intelligence tools were implemented with the promise of delivering fresh insights that could solve every business problem. Often the technology failed to live up to the potential and users struggled to take action based on the data. Today, technology is no longer a hindrance. You’re bound only by your agility and your commitment to transforming the enterprise. Data and information is combined with cloud platforms, AI and machine learning to enable citizen data scientists across the enterprise to make decisions.

IDC estimates that the revenue of big data and analytics will reach $274 billion worldwide this year.3

Take, for instance, the supply chain, which as everyone knows is a massive challenge. The power of decision intelligence provides real-time insights into your supply chain ecosystem. Front-line decision-makers can assess the situation and redirect supply chain requirements to different routes, vendors, or partners. The business users are equipped to confidently make the right decisions and minimise negative impacts.

Faster decision-making, increased agility and cost savings are some of the ways organisations can leverage decision intelligence, but they’re also looking for ways to monetise their data. To fully monetise the data, there must be some level of data sharing or data exchange taking place. This is where the cloud poses a risk in terms of compliance. The cloud is one of the most secure ways to protect your data, but it’s how you’re storing data in the cloud that creates an element of data security risk. With the pressure to comply with privacy regulations, organisations are turning to the concept of data clean rooms. Data clean rooms allow for the secure sharing and exchange of sensitive user data.

For example, when consumers accumulate credit card points, they can exchange those points for different products or services. A loyalty points provider platform enables the consumer to use their points for a statement credit, hotel stays or gift cards from their favourite brands and everything in between. Even if the credit card company masks or anonymises the customers’ personal data, there is still a risk that bad actors could seize the data and tie it back to the consumer. A data clean room provides a place to aggregate customer data from different sources and platforms. Then multiple partner-companies can analyse the data under tightly defined guidelines and restrictions that keep the data secure. The application of decision intelligence creates opportunities to transform the business with data. Future-forward organisations unify their data with AI and machine learning. Users are equipped to quickly make action-oriented decisions that enable the organisation to thrive. That’s the power of decision intelligence.

The previous decade saw growth of almost 5,000% in the amount of data created, captured, copied and consumed in the world.4