What makes generative AI so exciting and different from traditional AI? While traditional AI is focused on detecting patterns, generating insights, automation, and prediction, generative AI starts with a prompt that lets a user submit a question along with any relevant data to guide content generation. Traditional AI algorithms process data and return expected results, such as analyses or predictions; generative AI algorithms produce newly synthesized content, like text or images, based on training from existing data. A reason that generative AI became so popular so quickly, is because it empowers the end user: right now, anyone can log on to ChatGPT and start using it, which is a first for an AI application. Zero barrier to entry. Traditional AI necessitates rigorous data preparation and processes to develop and test a model designed to produce a good outcome. With generative AI, you can just start talking to it, and it will understand what you want to do and offer a response.
So, why not discard traditional AI methods then? Because while generative AI is an awesome new capability, it is still an emerging technology that is best suited for use cases around content generation and summarization or extending the capabilities of traditional chat bots. Today traditional AI has a wide range of very effective applications, including automated insights (“how did my sales last quarter compare to the prior year?”), predictive modeling (“what will my sales look like next month and what’s driving it?”), intelligent alerting (“alert me if you detect a customer’s spend exceeding a certain threshold”), and natural language processing for tasks like text classification (“extract and categorize names of people, organizations and contract value”). So it’s no surprise that firms like McKinsey see traditional AI continuing to account for the majority of the overall potential value of AI.
At Qlik, we see great traditional AI use cases at play everyday, even more so as a result of extending beyond just data scientists and opening up the power of low-code machine learning to business analysts. Here are a few examples of where AI is delivering measurable value for organizations of various sizes, and across many industries.
Example #1: Using traditional AI to achieve significant savings and improve patient outcomes
A great example of an organization driving measurable value with traditional AI is Appalachian Regional Healthcare (ARH). ARH uses automated machine learning to determine which patients are most at-risk for missing or cancelling their appointments. Data is used to analyze a variety of barriers such as transportation, distance or local weather. With this information, nurses or support staff are able to reach out to the highest-risk patients in the right ways with reminders and reassurances. This has enabled ARH to reduce cancellation and no-show rates leading to millions of dollars in savings, all while improving the health of the patients under its care. Sounds simple right? Yet it’s hugely valuable.
Example #2: Using traditional AI to uncover new revenue opportunities for clients
Gray Associates, Inc. is a software and services firm focused on higher education, helping their clients develop data-informed institutional strategies that maximize outcomes for students, the school, and its constituencies. Gray uses AI-powered analytics and automated machine learning to power their location assessment and predictive modeling service that provides advice to their education clients on large investments, such as opening a new campus. Data is collected from these clients, and predictive insights are derived from big data sources including US Census data, as well as competitive indicators such as regional job postings and matching programs from existing universities or trade schools. Their service has been used by dozens of their education clients with impressive results. For example, StrataTech Education Group reported revenue of $1.5 million from a new school it opened in Houston following advice from Gray Associates.
Example #3: Using traditional AI to meet climate goals
C40 is a global network of nearly 100 mayors of the world’s leading cities that are united in action to confront the climate crisis. C40 is applying machine learning to climate datasets, which are often massive, and combining it with Qlik’s AI tools to analyze climate trends and emissions data to find patterns that can help them and the cities they serve do take action. This has enabled cities like Accra, Ghana, to undertake tangible measures to cut emissions while improving the health of its inhabitants. Today 840,000 users across 17,000 municipalities are using these insights through C40’s knowledge hub.
Perhaps it’s these stories of data for good that excite me the most about the value that all of AI can deliver. This spring, Qlik hosted a roundtable of experts and innovators during the United Nations Water Conference in New York, to explore the promise of AI to manage water and climate crises, and to learn how businesses, governments, and agencies can use new data insights and context to inform the most important decisions of our era.We strongly believe that AI can play a vital role in managing water supplies, predicting shortfalls, and adapting to water crises and look forward to continuing our efforts in this area.
For both traditional and generative AI, there is so much untapped potential - and the journey should start with your data. You can leverage the power of traditional AI and generative AI today with Qlik not only with our augmented analytics capabilities, but also with Qlik AutoML allowing analytics users to easily generate predictive analytics, and with our newly released suite of OpenAI connectors to request and embed generative AI content directly in your analytics experience.