CNN vs. GAN: How are they different? neural radiance field (NeRF)

Generative AI landscape: Potential future trends

Learn more about the growth of generative AI, its impact on other technologies, use cases and 10 trends that will contribute to the technology's development.

Generative AI technology has percolated across multiple domains over the last few 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 higher-quality text, images, voices, synthetic data and other kinds of content.

The recent introduction of ChatGPT thrust generative AI into the limelight, raising public awareness of its potential for business, productivity and art.

"The release of ChatGPT made AI accessible to anyone with a browser for free. So, our families, children and people without a background in AI or data science could 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 that created magical output, so the public has already been warming up to and 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, capable of generating high-quality outputs. In addition, generative AI has many applications, such as music, art, gaming and healthcare, that make it more attractive to the broader 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 fast 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, principal product designer at TXI, a Chicago-based product innovation firm. "Basically, it frees up my cognitive bandwidth to focus on higher-impact and higher-value tasks."

GAN training method
Generative adversarial network (GAN) training can create accurate human faces, synthetic data or facsimiles of humans.

Generative AI industry use cases

Industry experts see plenty of new generative AI use cases beyond just writing faster emails or asking questions:

  • Generating 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 whole blocks of Python code to support their services. The tool delivers somewhere from 30% to 50% improvement in productivity, depending on the project.
  • Writing various kinds 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 review AI content thoroughly and use it as a helping hand, not your final content, it can and will make your life and jobs 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 greater 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, is using generative AI tools to assist in the process of creating better documentation.

The future of generative AI

Practically every enterprise app and service is adopting generative AI in some capacity today. And, while the technology offers tremendous promise, enterprises need to consider some of its challenges and limitations as they expand their use of the technology. Many of the first limitations slow down apps, while others might create real problems, like AI hallucinations, where generative AI apps make up content that's not tied to facts. Recent examples of AI hallucinations are Google's Bard incorrectly stating 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 had been jailed for bribery.

Enterprises using these kinds of chatbots need to be aware of how this kind of misinformation could direct customers to carry out possibly dangerous repairs, resulting in their brand being damaged. Successful enterprises will develop countermeasures to mitigate 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 will drive a need to learn prompt engineering skills to produce better content. He expects many firms will improve UX through tools for prompt-based creation; however, IT decision-makers must safeguard corporate data and information while using these tools. When implemented correctly, it might not even seem like working with AI.

2. APIs unlock enterprise use cases

Although chat might be getting all the attention today, new APIs will make it easier to weave various generative AI capabilities into enterprise apps. "While people are using ChatGPT for many things, from coding software to bedtime stories for our children, it is the APIs that make ChatGPT possible that are so interesting," PwC's Greenstein said. With these APIs, any application -- from mobile apps to enterprise software -- can use generative AI to enhance an application. Microsoft and Salesforce are already experimenting with new ways to infuse AI into productivity and CRM apps.

3. Reimagine business processes

As generative AI improves, it will likely automate or augment more everyday tasks. Greenstein predicted this will let firms reimagine 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 the market," Greenstein said. "AI-native business models and experiences will allow small businesses to appear big and large businesses to move faster."

4. Improved healthcare apps

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 access 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 could help firms find ways to harness imperfect data, while mitigating privacy concerns and regulations. "The use of generative AI in creating synthetic data has the potential to supercharge our ability to rapidly create new AI models, enhance our decision-making abilities and give our organizations ways to respond to change in a much more agile way," ToolsGroup's Custeau said.

Generative AI timeline
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 created massive supply chain disruptions that have resulted in long-lasting effects on organizations, the economy and the environment. Custeau's team has been exploring better ways to simulate rare events that could help lower their adverse effects cost-effectively.

7. Hybrid models boost reliability

Large language models (LLMs), like ChatGPT, showcase the potential for new technologies, like transformers. However, future progress might often require combining multiple models. "The current issue with these tools is that they are unfit for regulated industries due to their probabilistic nature and inaccurate reporting based on the trillions of data sets they are pulling from," said Emmanuel Walckenaer, CEO at Yseop, a content generation platform. Hybrid models combine the benefits of LLMs with symbolic AI's accurate and controllable narratives. He predicted hybrid models will spur innovation, productivity and efficiency within regulated industries by ensuring more accurate outputs.

8. Personalized generative applications

ELB Learning's Blackmon predicted a rise in personalized generative applications tailored to individual users' preferences and behavior patterns. For example, a personalized generative music application might create music based on a user's listening history and mood. Similarly, AI could 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. Eventually, AI-powered virtual assistants could 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 might help streamline work processes based on an individual's style.

9. Domain-specific apps

Lotis Blue Consulting's Carroll believes generative AI will open numerous opportunities for fine-tuning domain-specific applications. For example, generative AI could extract insights from medical publications on a disease condition or automate mind-numbing query response typing work in customer service centers. LLMs could 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 for training customers on LLMs for their apps without introducing operational or reputational risks to processes. "This may well be the catalyst that IT leaders needed to change the paradigm on 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 digital transformation consultancy Nexer Group, predicted generative AI will help drive the creation of natural language interfaces (NLIs) that are more intuitive and easier to use. "NLIs enable users to communicate 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 could interact 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 could reduce clerical errors and improve efficiency.

"You'll be hearing the term copilot a lot, and I think that's the right way to think of it," Johnson said. "This technology will allow everyone to focus on how they can better serve their customers and grow their business."

Next Steps

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