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Generative AI models like ChatGPT are already proving to be the most disruptive technology since the Model T. Whether you’re a family run business or a large multinational, AI will become pervasive across your industry, and as this technology advances, organizations that integrate generative AI will better position themselves to adapt. Generative AI surged in 2023 with strong adaptation and interest from users – platforms such as ChatGPT, Midjourney, DALL-E reached one million users much faster than other major tech products.  

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Every company wants to capitalize on this interest – in 2023, announcements and deals related to generative AI were announced every month.  Microsoft kicked off the year by announcing a multiyear . In March, OpenAI announced the release of ChatGPT-4 and added plug-ins enabling .The same month, it announced a partnership with Salesforce to make ChatGPT available directly with Salesforce’s Slack team communications platform. In April, Microsoft refreshed its SharePoint and OneDrive software with a ChatGPT-enabled security solution called Copilot.  

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The value of generative AI has the potential to skyrocket, with that the value of the value of generative AI will go from $11.3 billion in 2023 to $51.8 billion by 2028.

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In this article, we’ll explore why companies should not only embrace generative AI but also actively prepare for it. The applications will soon cover every core business function, and the risks can’t be ignored, and we’ll identify strategic steps your organization can take to harness the full spectrum of generative AI’s capabilities.

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Top Generative AI Applications in 2024

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1. Scale your sales and marketing. Your organization can harness generative AI and tailor your products, services, and marketing to individual customer preferences. You can provide personalized product recommendations, craft customized email marketing campaigns, or generate personalized responses in customer support. As a result, you can quickly scale your e-commerce business, accelerate revenue growth, and improve your sales conversion. 
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2. Improve your customer service. Your customers will expect you to communicate with them as individuals in an engaging way. Generative AI excels at personalization, which is a key driver of customer engagement and satisfaction. Enhance the customer experience and improve customer response time. 
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3. Increase manufacturing efficiencies. The potential for automation is significant. Generative AI can:
   
   - Automate repetitive and time-consuming tasks.
   - Increase operational efficiency.
   - Increase safety by reducing the risk of human errors.
   
   This reduces costs and frees up human resources for more creative and strategic work, further improving your organizations efficiency.
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4. Refine your supply chain management. Generative AI can draw in information from user inputs and provide supply chain management efficiencies in: 
   
   - Order processing
   - Inventory management
   - Logistics planning
   - Supplier management
   - Customer service for supply chain partners
   
   This will streamline ordering and inventory, improve supplier engagements and quality. 

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In a recent of more than 2,500 executives, 38% indicated that customer experience and retention is the primary purpose of their generative AI investments. This was followed by revenue growth (26%), cost optimization (17%) and business continuity (7%).

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We expect huge shifts in these directions in 2024, but these applications don’t come without risks.

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Top Generative AI Risks in 2024

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1. Limited control and predictability. Generative AI systems are hard to control and predict because they are based on machine-learning models, which are difficult to change. Outputs of such models are hard to interpret and may not meet users’ expectations. 
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2. High setup costs. Training the models requires large amounts of data sets and high computational power. Your organization must invest in storage space and processing capabilities. Depending on your business size and budget, the high cost of initial setup could slow your adoption.  
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3. Deep fakes. Generative AI can produce fake photos and images that are indistinguishable from the real thing. Synthetic media like photos and videos can be used to spread misinformation and improperly manipulate public opinion. This ability to create deep fakes may lead to increased cases of identity theft, fraud, and counterfeiting.
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4. Copyright ambiguities. Ambiguities exist over the authorship and copyright of AI- generated content—who owns the rights to creative works and how the works can be used. Generative AI tools are trained on large amounts of publicly available data. They are not designed to be compliant with copyright laws. Pay close attention to how your organization uses the tools.  
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5. Lack of originality and creativity. AI-generated content is new but created primarily based on existing data by a system with limited ability to come up with new ideas on its own. This lack of originality and creativity can be a problem, especially in the creative industry.  
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6. Safety and data privacy. The underlying data on which the models are trained may include personal information obtained without consent. This is a violation of privacy, especially if data can be used to identify an individual, the person’s family, or location details. Prepare your organization for malicious actors. Make sure mitigating controls are put in place. For example, check to verify if you have a cyber-insurance policy and see if it covers AI-related breaches.

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Risks involving bias, transparency, and the ethical implications of deploying such sophisticated models in real-world applications will remain top priorities for organizations.

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Additionally, your organization should assume that any queries they enter into ChatGPT or other generative AI models will become public information. You’ll want to have processes in place to avoid accidentally exposing your IP.

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Set yourself apart from your competition

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Melyssa Plunkett-Gomez recently spoke at . She discussed the importance of encouraging businesses to view AI as an asset rather than just a tool. She calls this your organizations “composition of experts.” Each core function of your business will have its own AI trained specifically to do a certain job. 

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Seven different “AI experts” will be trained in:

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• Customer Support
• Sales & Marketing
• Product R&D
• Finance
• Risk & Legal
• HR & IT
• Supply Chain

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For example, customer support demands need a different expertise on how to talk to customers than you would need to manage your supply chain and work with your vendors. These would require different sets of experts. Your enterprise strategy will vary depending on your size, products, or services. But you can still train your own models and they become core IP for your business. 

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The goal is to turn generative AI into an asset that not only addresses your current business challenges, but also positions your organization for future growth. Thinking about a long-term strategy for your business is critical. 

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But how do you do this?

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Five steps to think strategically about your AI transformation:

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1. Start with the best open-source model.
2. Fine tune these specific to your enterprise.
3. Optimize on the highest performance hardware.
4. Deploy in a private and secure environment.
5. Training on data to build long term differentiation. 

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For more on these strategies, read Executive Chairman of Ayna, Nick Santhanam’s article that details in your industrial business. 

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Conclusion

The degree of generative AI adoption currently varies according to function and industry sector—including a plethora of opportunities for industrials.

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Across functions, information technology is making the most use of generative AI and is expected to be the leader in fully integrating generative AI into critical functions. In addition, notable shares of employees in supply chain, manufacturing, marketing, advertising, sales, and product development expect to embrace generative AI by 2025.

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By industry, the technology, media, and telecommunications sector has the greatest usage of generative AI and the highest expectations for full integration in the future. Though other sectors -  including financial services; business, legal, and professional services, and healthcare - are currently engaging with AI more significantly, the industrial sector is not far behind. Leading players that include General Motors, Georgia-Pacific, BMW, Rockwell Automation, and Siemens showcase diverse applications of generative AI. These early applications are primarily in machine operations, plant scheduling and optimization, and manufacturing productivity.  

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Companies that didn’t embrace the internet 20 years ago are struggling, or more likely gone. AI has the same parallel today, no matter your industry. Ignore it at your own peril.

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Stay up to date on upcoming events and listen to our podcast where we talk with leaders from across the industrial sector.

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