There’s no doubt that one of the hottest tech topics lately is leveraging generative artificial intelligence to produce content. From marketing emails to proposals to bespoke graphics to software code, GenAI is streamlining processes in multiple industries and in multiple ways. Consumers, too, are exploring their own creativity through GenAI tools.
But content creation isn’t AI’s only specialty. There are many other applications for AI that can make a big productivity, strategy and cost-saving impact for businesses—and even support essential social endeavors. Below, 16 members of Forbes Technology Council discuss AI applications that businesses and the public should know about and explore.
1. Predictive Analytics
Companies should employ generative AI and data tools for forecasting, using past data to foresee trends and refine their strategies. Predictive analytics is vital for industries including finance, logistics, healthcare and retail, as it enhances data handling and precision. Centralizing data boosts accuracy and efficiency, forecasts consumer needs and reveals opportunities. Proper data oversight is essential. – Murtaza Amirali, DataPillar
2. Creating Educational Materials
There is a huge opportunity for education services to use natural language processing tools to create educational materials—the cost of creating and translating educational materials is dropping through the floor. Additionally, machine learning platforms, while not as new as other tech, can be used for predictive analytics in support services, and there are AI engines that can make recommendations on how to solve technical problems. – Thomas Lah, TSIA
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3. Automated Decision-Making
Automated decision-making systems offer transformative potential. By harnessing data, these systems enable real-time, data-driven decisions; optimize operations; and enhance customer experiences. Their ability to analyze vast datasets surpasses human capability, ensuring more accurate and efficient outcomes across various domains, such as finance, healthcare and supply chain management. – Shelli Brunswick, SB Global LLC
4. Robotics
AI-powered robotics is proving to be a really exciting area that has powerful business implications. Both AI and robotics are already game-changers across industries—combined, they can help businesses increase efficiencies, spot discrepancies and create a safer work environment for everyone. – Edwin Huertas, Shockoe | Mobile by Design
5. Contextual Ad Targeting
The world is heading toward a cookieless future, but brands still need their messages to reach audiences with relevance and precision. Contextual ad platforms can be equipped with AI to increase the efficiency of targeting via precise content recognition that takes into account the emotional and other characteristics of the content where an ad is placed. – Ivan Guzenko, SmartyAds Inc.
6. Financial Forecasting Models
Predictive AI is forward-looking, analyzing past data to unearth predictive patterns and then using current data to provide accurate forecasts of what will happen in the future. Today, it’s used in industries such as financial services to mine stock data to generate reports or power mortgage risk models. Predictive AI is a workhorse with a crucial role to play in any organization’s AI strategy. – Thomas Robinson, Domino Data Lab
7. Business Process Automation
Business process automation leveraging AI is an area of rapid development and promise for all businesses. BPA provides the ability to automate mundane tasks by leveraging intelligent agents that can operate browsers to carry out repetitive tasks at scale. While the true potential of this technology is still in its infancy, it’s a capability businesses should be tracking. – Pete Hanlon, Moneypenny
8. Industry-Specific Language Models
Large language models are getting a lot of hype, but task-specific and industry-specific language models can have a greater impact, especially in high-compliance industries such as healthcare or finance that have a greater need for accuracy and privacy. LLMs aren’t necessarily the best option for tasks that need to be performed near-perfectly to meet stringent compliance and data safety requirements. – David Talby, John Snow Labs
9. Adaptive Cybersecurity Tools
As cyber threats become more sophisticated, AI-powered security tools offer distinct advantages, including faster data analysis, real-time threat adaptation and a lower dependence on human intervention. To safeguard business assets, organizations must prioritize the safe and responsible implementation of these technologies to effectively mitigate the intensifying risks and stay ahead of attackers. – Chris Wysopal, Veracode
10. Small-Scale Data Analysis
There are AI tools that make it easy for laypeople to perform analysis of small-scale datasets. These tools allow leaders to capitalize on their “hunches.” By running these AI tools on their laptops, leaders can quickly confirm or disprove their hunches without the need to send in special requests to their IT team. – Joseph Ours, Centric Consulting
11. Neuro-Symbolic AI
Neuro-symbolic AI elevates conventional AI tools, such as machine learning and advanced numerical analyses, with a level of cognitive reasoning that adds context, awareness and knowledge. There is a certain level of rigidity that comes with numerical AI that just can’t work in industrial settings, where decisions need to account for a multitude of variables. Neuro-symbolic AI is the answer. – AJ Abdallat, Beyond Limits
12. Predicting User Behavior
Predicting user behavior and suggesting the right product at the right time has become a cornerstone application of AI in businesses today. Leveraging all your collected customer data, enriched with public domain data and social media activity, can create an incredibly effective sales tool for your business, dramatically increasing your conversion rates. – Dimitar Dimitrov, Accedia
13. Multimodal AI
As more businesses recognize the value of integrating different data streams to drive innovation and value creation, multimodal AI will become widespread. At its heart, multimodality uses a combination of AI techniques to analyze diverse forms of data, such as text, images, speech and videos, to gain critical insights and enhance the quality of recommendations. – Umashankar Shivanand, SleepScore Labs
14. Voice Interfaces
Voice is undoubtedly the interface of the future. We’ll be able to speak to virtual customer agents, and more importantly, we’ll be able to engage with machines via novel voice interfaces. The latency, prosody and logic these models now exhibit are uncanny and will open up a world of opportunity. – Francisco (Tony) Navarro, Red Ventures
15. Anomaly Detection
Generative AI has been a popular topic lately, but it’s important for businesses to also focus on AI-powered anomaly detection systems. These systems use advanced algorithms to identify unusual patterns in large datasets. They help businesses detect potential security breaches, fraudulent activities or operational inefficiencies and take proactive measures to address issues before they escalate. – Meiran Galis, Scytale
16. Edge AI
Edge AI is powering critical monitoring scenarios in sectors including agriculture, healthcare, manufacturing, public safety and more. These AI workloads have a different model architecture than generative AI and typically run locally, where the data is generated. Edge AI is even being leveraged on low-cost (less than $10) equipment to detect such issues as anomalies, broken glass and impending motor failures. – Pete Bernard, EDGECELSIOR
