How AI Is Refactoring Finance, Manufacturing & Healthcare
The AI revolution across multiple sectors and the way forward.
Whether we call this the fourth or even fifth industrial revolution, where levels of human-machine interaction and collaboration reach even loftier heights than many of us ever imagined possible in our lifetimes, the shift AI is bringing about across industries is seismic, explains Lori Witzel of Spotfire, Cloud Software Group.
Today, artificial intelligence (AI) is massive in terms of ingesting information through large language models (LLMs) in the realm of generative AI. It is hugely diverse in terms of its scope across predictive, causal, generative and other AI use case types – and it is widespread and all-encompassing due to its applied relevance across every industry vertical.
Boosting Business Value
Now that both business and technology leaders realize they need to evidence some level of AI optimization and acceleration to drive new business value, these same departmental leaders also understand that it is vital to grasp the vast potential of AI to accelerate automated insights from predictive intelligence and analytics.
We now sit at a strategic inflection point where the opportunity exists to use pragmatic, insightful and (above all) functionally relevant AI-powered analytics to accelerate operational efficiencies across industries. This is a chance to actually change the way companies work; it’s a chance to create new business models and overhaul operational constructs that have been around for decades.
Simply put, we can now think about accelerating positive forces of digital disruption with new tools inside new methodologies, but inside industries that we already recognize. Let’s consider three very important examples in the shape of finance, manufacturing and healthcare.
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Finance, Money & Banking
As we know, the financial sector runs on numbers. This core fact underlies this industry’s applicability for AI-enriched acceleration and automation. Because our AI engines (of whatever modal type) are designed to drink from large volumes of data to train and learn, the shape of the banking industry is inherently well-aligned with the use of AI technologies.
When we consider the user-level changes in finance and banking that we have seen played out in the last decade in relation to the development of mobile banking and money management, we can immediately see where AI will apply. We need automated intelligence if we are going to build applications that can make decisions faster than any human operative could.
Users already expect instant service and assistance from the applications they use on their desktops and mobile devices. We need AI to keep pace with this new cadence. These same users now expect to be able to access instantaneous decision-making when engaging with historically manual processes related to banking transfers, deposits, trades and actions related to the new world of cryptocurrencies.
The CA Business Journal states, “The advent of digital currencies, artificial intelligence and mobile applications has encouraged the proliferation of startups, which challenge traditional financial institutions by offering tailored services for today’s tech-savvy consumers.”
Manufacturing
It might sound like a simplification, but the manufacturing industry has many parts. Indeed, it has many parts, figuratively, operationally and literally. With global supply chains having been jolted so markedly in the wake of the pandemic and other disruptive world events, every businessperson and consumer now has a more acute sense of where production lines are operating and where they are stalled or experiencing outages.
As we now build a more connected and collaborative world, applying AI to everything from error detection in production lines to customer delivery will be crucial to the future success of the manufacturing base in every country.
Manufacturing plants and other heavy equipment users are shifting to AI-based predictive maintenance (PdM) to help anticipate and adapt to industry needs. Without timely management of equipment, companies risk losing valuable resources. While performing machine maintenance too early can be a waste, delayed maintenance can also lead to losses caused by the machine’s extensive wear and tear.
On the march towards new levels of efficiency, AI will now empower manufacturing organizations to augment their processes and reach a new level of capability in terms of managing supply chains more efficiently. This will encompass everything from monitoring the incoming stream of raw materials and the management of core utilities, all the way through to tracking parts and products across the manufacturing plant floor and ultimately down to final deliveries to customers.
Healthcare
While many industries are described as having mission-critical functions, it is surely healthcare that is most directly understood to operate systems, equipment and processes that are life-critical. As we now use AI to crunch through massive volumes of data to detect life-threatening illnesses, healthcare professionals also have the opportunity to use automation to analyze and identify impurities on a medical production line more quickly. These are, of course, actions that could literally mean the difference between life and death.
Not only will we now use AI for detection and healthcare systems management, but we will also make use of it to accelerate processes that drive drug discovery and clinical trials. Although we stand at yet another inflection point as we humans start to build trust in AI-enriched medical practices, the industry will ultimately show the true worth of AI in this space as we get used to relying on data-driven solutions to streamline and automate administrative tasks, support physicians and look after more accurate and readily available patient records.
Building a Data Value Chain
All these functions start with data, and crucially, it needs to be data that runs through systems that offer appropriate levels of governance. This means enterprises need to use data virtualization and data management tools that enable automated alerts and process changes to prepare information for data systems consumption properly.
It’s at this point that organizations in the three verticals noted here and elsewhere can start to say that they have established a data value chain, i.e., an approach to information management that sees a business adopt industry-specific, rapid and pragmatic AI across the enterprise.
This is when a company really starts to be able to meet and exceed customers’ expectations and demands. By using AI-enriched predictive and precise analytics, enterprises can now get ahead of their competitors, make real-world reductions in live operational costs and reduce their time-to-decision window in all aspects of business.
What AI revolutions have you witnessed in your sector? Share with us on Facebook, X, and LinkedIn. We’d love to hear from you!
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