Business intelligence (BI) platforms are evolving. By adding artificial intelligence and machine learning, companies are transforming data dashboards and business analytics into more comprehensive decision support platforms. This movement toward \u201cdecision intelligence\u201d sees its sophisticated mix of tools increasingly embedded into enterprise workflows, when and where decision-makers need them most.\n\u201cDecision intelligence is the ability of the enterprise to process large amounts of data to make decisions,\u201d says Nicole France, analyst at Constellation Research. \u201cIt\u2019s the same thing that business intelligence was going to do, but accessible throughout the enterprise.\u201d\n[ Learn the secrets of highly successful data analytics teams. | Beware the 12 myths of data analytics and the sure-fire ways organizations fail at data analytics. | Get the latest on data analytics by signing up for CIO newsletters. ]\nSome of the most visible examples of decision intelligence in action are recommendation engines, which use analytics to predict which products consumers would find most appropriate, or which movies they should watch next. Tools such as these provide context and pertinent options to help people make better decisions, France says, adding that the dashboards and analytics of traditional BI tools are still valuable, but decision intelligence is more accessible and relevant.\n\u201cFor people on the front lines, context matters,\u201d she says. \u201cAnd there\u2019s a degree of complexity that\u2019s difficult to get right. The goal is to present things in a clear, easy to understand way, so people can understand some complex analysis, and make a decision quickly.\u201d\nThe case for decision intelligence\nThe COVID-19 pandemic has accelerated digital transformations in nearly every sector of the global economy \u2014 and AI is increasingly at the heart of this. More than 95% of companies surveyed by 451 Research, consider AI to be important for digital transformation \u2014 and 65% say it is very important.\nAccording to the survey, which was released in late January, AI adoption rose 9 percentage points last year in the U.S. compared to the previous year, with only 28% of companies saying they slowed down AI initiatives as a result of pandemic.\nAnd a key field where AI is catching on is in data and analytics. According to a 2021 survey of software developers and IT leaders by RealBI, 41% of companies saw an increase in requests for access to data and analytics, with one of the top reasons being to enable users to make data-driven decisions. Moreover, the survey showed increased interest in embedding machine learning into analytics software or dashboards, with nearly 16% planning to add the technology in the near future, over the 6% of companies that currently do.\nSuch as addition of AI or machine learning to a business intelligence platform enables it to evolve into a decision intelligence platform by providing context, predictions, and recommendations when and where the decision maker needs them.\nAccording to Gartner, more than a third of large organizations will have analysts practicing decision intelligence by 2023.\nThe research firm defines \u201cdecision intelligence\u201d as a framework that enables data and analytics leaders to design decision models and processes in the context of business outcomes and behavior. In practice, this means decision intelligence uses analytics to help employees, customers, or business partners make decisions by offering them data, analysis, and predictions when they need it, and where they need it.\nAs decision intelligence becomes a core part of business processes, decisions get made faster, easier, and less expensively than before.\nCutting down lines at the California DMV\nNot only can decision intelligence help employees make better decisions, it can help them make decisions faster. The latter is particularly important when people are waiting in line at the Department of Motor Vehicles (DMV), risking catching a deadly disease each minute they\u2019re there.\n California DMV\n\nAjay Gupta, chief digital transformation officer, California DMV\n\n\n\u201cIn my world, decision intelligence is not just analytics and insights, but being able to make decisions,\u201d says Ajay Gupta, California DMV\u2019s chief digital transformation officer. \u201cWe use AI in our day-to-day work where it\u2019s not just telling you what you need to do and you go do it, but it\u2019s helping you make decisions like another human would.\u201d\nThe agency began implementing intelligent document processing right around the time the pandemic hit, he says. It allowed customers to upload documents and find out whether there was anything they were missing before they arrived at the DMV. Digital transformation platform vendor ABBYY helped the DMV with the project, with additional work done by consulting firm User Friendly Consulting.\n\u201cThere is some mining involved with computer vision,\u201d Gupta says. \u201cAnd the AI is making decisions based on historical data and the training we have provided.\u201d The platform reduces the need for people to leave and come back later with the right documents, he says. \u201cAnd it reduced the time of the transaction because there was less processing that had to be done at the window.\u201d\nFor example, there\u2019s a federal push to upgrade drivers\u2019 licenses to the new Real ID format, which will make it easier for people to fly domestically. As a result, many California residents have needed to come in to the DMV to get new licenses. By adding AI functionality and the ability to upload documents ahead of time, California DMV has reduced in-person transaction time from 27 minutes per person to around 10 minutes.\nThat helped a lot during the pandemic, Gupta adds. \u201cThe less time you spend in a crowded facility, the less changes of exposure.\u201d Plus, without documents being passed back and forth, there was less opportunity for the virus to be passed along on paper surfaces.\nA chatbot also helps answer basic questions from both DMV customers and employees alike, he says. \u201cOne thing we\u2019re exploring now is to use that to train the technicians just-in-time.\u201d\nInjecting decision intelligence\nCalifornia DMV is also planning on using AI for scheduling. With around 10,000 employees working at home, in field offices, and at headquarters, it can be tricky to ensure shifts are covered with enough personnel at each branch.\nToday, data scientists at the DMV perform the analytics for this, providing recommendations to regional managers and office managers. But the agency is now evaluating platforms to embed the decision intelligence into systems used by non data scientist employees and is expecting to make its final vendor choice this year.\n\u201cWith new tools, that will be federated out,\u201d Gupta says, and integrated with the workflow systems. \u201cIt\u2019s all going to be part of an easy-to-use interface, using out-of-the-box products specifically designed to have a nice user experience. It will create an augmented decision-making process for employees.\u201d\nThe final decision will be up to the humans, he says. \u201cIt presents options, creates calendars that can be changed, creates the optimum baseline schedule, and the actual trigger is pulled by managers.\u201d\nThe tools that the DMV is currently evaluating to do this include the ability to inject street traffic data. The agency\u2019s website already includes foot traffic information, to help customers decide what day and time to come in. The information is also used to schedule work shifts.\n\u201cBut in the Bay Area and Los Angeles, the traffic and the parking create a lot of disruptions around the field offices, so we\u2019re looking at ingesting that data that would help us do this optimization,\u201d Gupta says.\nThe DMV is also looking to machine learning to help internal investigators identify waste and abuse within and outside the organization. \u201cOur objective is to get to a human-aided decision intelligence model complete with a feed from our investigators, behavioral scientists, and data officers,\u201d he says.\nCOVID-19 accelerated the agency\u2019s transformation timelines, he says, but the DMV was already heading in that direction.\n\u201cWe have been able to make good use of this crisis to help our customers with AI and RPA and ML. I hope we can continue to momentum. I hope COVID goes away \u2014 and fast \u2014 but that what we\u2019ve done stays,\u201d Gupta says.\nOther use cases for decision intelligence\nCybersecurity is an area where people have to make decisions based on vast amounts of fast-moving data with much potential risk for their companies. Here, AI and ML can play a role in helping security analysts make better decisions, as networking company Cato Networks shows.\n Cato Networks\n\nAvidan Avraham, research team leader, Cato Networks\n\n\n\u201cWe use AI and ML intensively for a bunch of activities at Cato,\u201d says Avidan Avraham, the company\u2019s research team leader. \u201cFor example, we built a reputation model that uses all information we have about a domain or IP address. Based on internal network data and open source intelligence data, it predicts the likelihood it can be malicious.\u201d\nThat means that threat hunting analysts can prioritize their investigations, he says.\nCato built its own technology to do this, using Amazon Elastic MapReduce to train its models. The company has been using the system for over a year now, Avraham says, with good results and a low rate of false positives. \u201cIt is embedded in our analysts\u2019 workflow,\u201d he says. \u201cBefore we built this technology, we used to do the analysis manually so, obviously, it is a much faster process now.\u201d\nDecision intelligence can also help companies be more consistent. Take, for example, a bank officer making a loan approval decision.\n PricewaterhouseCoopers\n\nAnand Rao, partner and global AI leader, PricewaterhouseCoopers\n\n\n\u201cWhat happens in many cases when individuals are involved is that every individual has a different background,\u201d says Anand Rao, partner and global AI leader at PricewaterhouseCoopers.\nThere are ways companies try to achieve consistency, such as with training, but external factors still come into a play. If a loan officer is having a bad day, for example. Here, decision intelligence tools can provide context and recommendations to help create more consistency in business processes.\nApplications of decision intelligence in other enterprise domains, including customer relationship management and sales tools, are growing as well \u2014 and not surprisingly, given the promise of pairing human intelligence with AI to augment the decision-making process.