Yes, AI Is Flashy, but its True Value is Practicality

They tend to emphasize the thought-provoking and controversial use cases of AI, like using it as a replacement for on-air talent, creating programming or becoming an engine for developing ad creative.

It’s true that generative AI can produce imagery, copy and video with prompts. Its relevancy and quality remain in question – at least in these early stages. It’s easy to get caught up in the forward-facing aspects of what this technology can do. However, it’s a relatively immature area, with many things like regulations and disclosures still undetermined.

So, it’s wise to look past the flash and toward the true backbone of AI — its practical applications. The benefits of AI for those on the finance and business sides of media companies are more about how it can augment human intelligence.

After being in and around AI for 10 years, I believe the value you’ll realize from adopting AI will primarily be from its practical use cases related to automation and data analytics. Let’s look at what AI can do for you right now.

AI Helps Automation Be “Smarter”

You experience some level of automation in almost any technology you touch. In fact, we expect this from our platforms. This foundational automation falls into the category of robotic process automation (RPA). RPA uses “digital robots” to create, deploy and manage automation. It can emulate the actions of human resources in rules-oriented tasks that don’t deviate. It can do things like keystrokes, navigation, data identification, data extraction and more.

Anything manual on your side is ripe for errors and a slow pace, so RPA enables faster processes and greater accuracy. Infusing AI with RPA creates intelligent process automation (IPA). AI subsets that enhance automation include machine learning, computer vision and cognitive automation. It makes automation more valuable and better able to assist humans.

Some examples of this include:

• Accounts payable activities related to matching invoices to purchase orders
• Managing vendor contracts by aggregating data from multiple systems so you are always aware of payments and renewals
• Reconciliation of general ledgers by allowing automation to do cursory reviews before humans do it to mitigate the risk of errors
• Legacy and new platform integration to ensure data sharing and workflows rather than paying high costs for bridge compatibility
• Account setup with RPA bots pulling information from different documents into one
• Data collection and integration to support audit trails and compliance
• Using electronic invoicing and payment platforms that automate processes in the cycle and reconciliation

Imagine how much manual work you can eliminate that is repetitive. Nearly any time AI is mentioned these days, however, people express concerned that automation will replace humans. That’s not the case at all.

Fear that Automation Replaces Humans is Another Flashy Headline

In considering which activities businesses can automate, there is much trepidation around the answer. The World Economic Forum Future of Jobs Report concluded that machines perform 34% of all business-related tasks. The results also revealed that 50% of organizations expect AI to create jobs, while only 25% believe it will eliminate them.

Technology has always worked this way -- displacing some jobs but creating new and better positions. AI is falling into the same trajectory. With AI more accessible and usable, the future business or finance jobs it may supplant would relate more to manual, repetitive work. In its place will be work that relies on those who embrace AI and become experts at leveraging it to deliver better processes and greater insights.

Your people can engage in more meaningful work using problem-solving and critical thinking skills, which are essential for data analysis.

AI and Data Analysis: Insights are Waiting in Your Big Data

Data is critical in business intelligence, but only if it’s accessible and you have the frameworks to analyze it. Human review of data is too expensive and time-consuming. Findings from this are usually late and no longer actionable because of the human labor required. AI speeds all this up. Again, this is where AI and human intelligence work together.

Data analysis platforms powered by algorithms can aggregate and interpret data from many systems. It can find trends, patterns and anomalies. It can create some what-if scenarios so you can make reasonable hypotheses regarding change.

The practical application of AI and machine learning algorithms analyzes your historical data. The results are predictions about future trends. For example, you can use it to find patterns related to advertising revenue to determine seasonality trends, which would be useful to sales.

The other major area where AI can help is with forecasting. With all types of data to consider, it will deliver fine-tuned predictions. You’ll then add your knowledge to its forecast to develop a more reliable and accurate one.

Practical AI is the Future

It’s easy to get distracted by AI’s flashiness, but staying rooted in the practical is how to make it work for your media company. As you upgrade and update your tech stack, look for pieces that work within an AI ecosystem. You’ll want them to be built on modern architecture, enable easy integration and allow for data sharing. With these things top of mind, you’ll be ready to execute automation and data analytics that yield high value.

MFM will be offering three regional meetings (in D.C. Atlanta and L.A.) in October and November, each of which will showcase the best ways to incorporate AI into finance processes. There’s still time to register, but space is limited. You’ll also be able to watch video interviews with several of the panelists here on MFMInSites.com this month and next. Take every opportunity you can to learn how AI can work for you.

This article was written by Beth Osborne who leads content strategy and execution at Marketron. She’s been a professional writer for over 20 years, helping tech and software companies tell inspiring stories.

Posted at MediaVillage through the Thought Leadership self-publishing platform.

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The opinions expressed here are the author's views and do not necessarily represent the views of MediaVillage.org/MyersBizNet.