Generative AI technology has made inroads across multiple domains in recent years. Much of this progress is due to advances in new large language models made possible by transformers. Meanwhile, improvements in slightly older techniques have made it easier for AI to generate text, images, voices, synthetic data, and other types of content.
The recent launch of ChatGPT has put generative AI in the spotlight, raising public awareness of its potential for business, productivity and art.
“Releasing ChatGPT made AI accessible for free to anyone with a browser. So, our families, kids, and people without a background in AI or data science can put it to work,” said Bret Greenstein, data and analytics partner at PwC. “This comes after a year of image-generating AI and filters in mobile apps creating magical output, so the public has already warmed up and become aware of AI in everyday life.”
Jonathan Watson, CTO at legal practice platform Clio, also attributes the explosion of generative AI to recent advances in generative models, such as generative adversarial networks and variational autoencoders, which are capable of generating high-quality outputs. Moreover, generative AI has many applications, such as music, art, gaming and healthcare, making it more attractive to the wider population.
Some observers call generative AI a new general-purpose technology that could deliver the same kind of broad impact as the steam engine and electricity. “I think interest in generative AI has exploded so quickly because it can act as our second brain and ultimately has the potential to drastically improve productivity and efficiency across a wide range of industries,” said Rex Chekal, chief product designer at TXI. A Chicago-based product innovation firm. “It basically frees up my cognitive bandwidth to focus on higher impact, higher value tasks.”
Generative AI industry use cases
Industry experts see many new generative AI use cases beyond just writing emails or asking questions faster:
Generate code. Donncha Carroll, partner and chief data scientist who leads the data science and engineering team at Lotis Blue Consulting, said his group uses GitHub Copilot to write entire blocks of Python code to support their services. The tool delivers anywhere from 30% to 50% improvement in productivity, depending on the project. Writing different types of text. John Blackmon, CTO at ELB Learning, said his firm uses generative AI to generate various types of content, including resource guides, how-to articles, news articles, essays, product descriptions and social media posts. “As long as you thoroughly review AI content and use it as a helping hand, not your final content, it can and will make your life and work easier,” he said. Automation. Kavitha Chennupati, senior director of global product management at SS&C Technologies, an IT solutions developer, said the firm uses generative AI to suggest where new automation should be deployed. This enables an even larger cross-section of workers to initiate the development of automation, such as robotic process automation bots and low code-driven processes. Documentation. Pierre Custeau, CTO at ToolsGroup, a supply chain planning and optimization firm, uses generative AI tools to help with the process of creating better documentation.
The future of generative AI
Virtually every enterprise program and service today is adopting generative AI in some capacity. And while the technology offers tremendous promise, businesses must consider some of its challenges and limitations as they expand their use of the technology. Many of the first limitations slow down applications, while others can create real problems, such as AI hallucinations, where generative AI applications make up content unrelated to facts. Recent examples of AI hallucinations include Google’s Bard wrongly saying the James Webb Space Telescope took the first pictures of an exoplanet and the case of an Australian mayor suing OpenAI for defamation after ChatGPT said he was jailed for bribery.
Businesses using these types of chatbots should be aware of how this type of misinformation can lead customers to perform potentially dangerous repairs, resulting in brand damage. Successful enterprises will develop countermeasures to reduce the likelihood of misinformation and identify ways in which generative AI can deliver real value to customers and the bottom line.
Here are 10 future trends to pay attention to.
1. Prompt-based creation
Some of the most remarkable applications of generative AI are in art, music and natural language processing. Clio’s Watson expects this to drive a need to learn rapid engineering skills to produce better content. He expects many firms to improve UX through tools for rapid based creation; However, IT decision makers must protect corporate data and information while using these tools. If implemented correctly, it may not even seem like it works with AI.
2. APIs unlock enterprise use cases
While chat may be getting all the attention today, new APIs will make it easier to weave various generative AI capabilities into enterprise applications. “While people use ChatGPT for many things, from coding software to bedtime stories for our kids, it’s the APIs that enable ChatGPT that are so interesting,” said PwC’s Greenstein. With these APIs, any application — from mobile apps to enterprise software — can use generative AI to improve an application. Microsoft and Salesforce are already experimenting with new ways to add AI into productivity and CRM applications.
3. Restart business processes
As generative AI improves, it is likely to automate or augment more mundane tasks. Greenstein predicted that this will enable companies to rethink their business processes to use the technology and scale what the workforce can do. “With that, entirely new business models will emerge, just as they do after any disruptive technology comes to market,” Greenstein said. “AI native business models and experiences will allow small businesses to appear large and large businesses to move faster.”
4. Improved healthcare applications
TXI’s Chekal sees the potential for generative AI to improve patient outcomes and make life easier for healthcare professionals. Generative AI can extract and digitize medical documents to help healthcare providers obtain patient data more efficiently. It will also improve personalized medicine and therapeutics by organizing more medical, lifestyle and genetic information for the appropriate algorithms. Intelligent transcription will save time and help summarize complex information as part of doctor-patient conversations rather than as a separate process. It will also improve patient engagement through personalized recommendations, medication reminders and better symptom tracking.
5. Better synthetic data
Synthetic data has been around for years. Improvements in generative AI technology can help firms find ways to leverage imperfect data while mitigating privacy concerns and regulations. “The use of generative AI in creating synthetic data has the potential to improve our ability to rapidly create new AI models, improve our decision-making capabilities, and give our organizations ways to respond to change in a much more agile way to respond,” said ToolsGroup’s Custeau. .
The history of generative AI
6. More effective scenario planning
Custeau also believes generative AI could improve the ability to simulate large-scale macroeconomic or geopolitical events. The industry is grappling with a stream of events that have caused massive supply chain disruptions that have had long-lasting effects on organizations, the economy and the environment. Custeau’s team investigated better ways to simulate rare events that could help to cost-effectively reduce their adverse effects.
7. Hybrid models increase reliability
Large language models (LLMs), such as ChatGPT, show the potential for new technologies, such as transformers. However, future progress may often require combining multiple models. “The current issue with these tools is that they are unsuitable for regulated industries due to their probabilistic nature and inaccurate reporting based on the trillions of data sets they draw from,” said Emmanuel Walckenaer, CEO of Yseop, a content generation platform. Hybrid models combine the advantages of LLMs with symbolic AI’s accurate and controllable narratives. He predicted that hybrid models would spur innovation, productivity and efficiency within regulated industries by ensuring more accurate outputs.
8. Personal generative applications
ELB Learning’s Blackmon predicted an increase in personalized generative apps tailored to individual users’ preferences and behavior patterns. For example, a personalized generative music application could create music based on a user’s listening history and mood. Similarly, AI can analyze an individual learner’s strengths, weaknesses and learning styles during online training and then recommend the most effective teaching methods and most relevant resources. Ultimately, AI-powered virtual assistants may become standard features in learning platforms by providing real-time support and feedback to learners as they progress through their courses. Personalized assistants in enterprise apps can help streamline work processes based on an individual’s style.
9. Domain-Specific Applications
Lotis Blue Consulting’s Carroll believes that generative AI will provide numerous opportunities to refine domain-specific applications. For example, generative AI can extract insights from medical publications about a disease condition or automate mind-numbing query-answer typing in customer service centers. LLMs can ingest industry-specific information to provide insight for domain-specific workflows. For IT decision makers, the emphasis is moving from exploring the cool, new technology to identifying good data to train customers on LLMs for their applications without introducing operational or reputational risks to processes. “This may well be the catalyst IT leaders needed to shift the paradigm around data quality, making the business case for investing in building high-quality data assets,” Carroll said.
10. Natural language interfaces
Todd Johnson, managing director at Nexer Group, predicted that generative AI will help drive the creation of natural language interfaces (NLIs) that are more intuitive and easier to use. “NLIs allow users to interact with computer systems using natural language instead of programming languages or syntax,” he explained. For example, in a supply chain context, generative AI could provide an audio interface for workers in a warehouse distribution center. Workers can communicate with the NLI through a headset connected to a manufacturer’s ERP system to navigate a packed warehouse, find specific items, and reorder materials and supplies. This can reduce administrative errors and improve efficiency.
“You’ll hear the term co-pilot a lot, and I think that’s the right way to think about it,” Johnson said. “This technology will allow everyone to focus on how they can better serve their customers and grow their business.”
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