Beyond Data Optimization: How New Generative AI Models are Creating New Opportunities for Business
Key Takeaways:
- Generative AI is transforming operations in retail, healthcare, finance and more
- Multimodal AI, agents for automation and predictive analytics are all gaining traction
- Standing out from the crowd with AI is vital in saturated marketplaces
- Increasing complexity and a rapidly evolving landscape makes AI expertise essential
The growth of artificial intelligence in recent years has helped organizations around the world transform their data optimization. But the advent of generative AI, where machine learning can use existing data patterns to create text, audio, video and images, has added a new dimension to what businesses can do with AI.
The knock-on result of this change is that new business opportunities are emerging all the time, and those willing to embrace them early are gaining first-mover advantage. In this blog, we’ll highlight some of the new generative AI models that are transforming businesses, and how you can take full advantage.
Where is Generative AI Thriving?
The use of generative AI is spreading far beyond the tech-led industries that you might expect, and is starting to influence practical use cases in a range of sectors. These include, and are by no means limited to:
Healthcare
Generative AI can be used to support molecular simulations that aid faster and more insightful drug discovery and development, so that better treatments can reach more people that need them.
Retail and Consumer
Virtual try-on technology helps online shoppers try on clothing, accessories and other items. This allows them to buy goods in confidence, receive better customer experiences and minimise the risk of inconvenient returns.
Financial Services
Generative AI can assess patterns of data and provide helpful insights to inform a variety of financial functions, from tax calculations and bookkeeping, through fraud detection and compliance, to credit applications and investment strategies.
Technology Development
Several areas of software and tech product development can be streamlined and expedited with generative AI, such as design, data analysis and generating creative solutions. More generally, it can also support informed decision-making, and better customer service through task automation that frees up staff time.
Travel and Hospitality
Generative AI can provide personalized experiences, tailored recommendations, automated booking processes, real-time customer service assistance, and targeted marketing content.
Top Emerging Generative AI Models for Business
According to Gartner, less than 5% of enterprises were using GenAI-powered APIs, models or applications in production environments - but that figure is expected to top 80% by 2026. But what are the technologies powering this growth?
Multimodal AI Platforms
These artificial intelligence systems can bring together different types of data, such as text, audio, images and video, and assess all of them to generate more comprehensive, detailed and accurate insights.
Generative AI Marketplaces
New marketplaces have emerged that ease access to generative AI models, datasets and services for all types of businesses, helping them leverage the possibilities of image generation, text creation and music composition.
AI Agents for Business Process Automation
An increasing range of business tasks and business processes can be automated through generative AI agents. This includes using machine learning and Natural Language Processing to inform decision-making in evolving situations, and support in streamlining workflows and increasing employee efficiency.
AI-Powered Financial Services
Generative AI can power vital insights for better understanding of markets, customers and opportunities alike, including real-time calculations, forecasting, analytics and performance measurement.
Conversational Commerce Platforms
Mobile app messages as part of conversational marketing grew 137% in 2023 alone, representing a major move towards omnichannel customer experiences. AI-powered commerce platforms enable coordination of this and all other types of messaging, including calls, app messaging and voice assistant chats.
Employee Productivity Assistants
The proliferation of workplace assistants, supported by generative AI, are helping employees get far more done day-to-day. Routine tasks can be automated, while AI can support the drafting of emails and reports, so that workers can focus more time and effort on the tasks that drive real business value.
Predictive Analytics for Strategic Decision-Making
The ability to use generative AI to forecast trends and behaviors, based on historical data, can help organizations make better decisions in all areas. This can make a fundamental difference to the long-term success of a business, and remove the guesswork from their future strategy.
Hyper-Personalized Customer Engagement
Generative AI comes together with data, automation and analytics to enable hyper-personalized user experiences, where individuals get messages, recommendations and offers that are most likely to resonate with them personally. This can help businesses stand out from the crowd in the saturated world of eCommerce and digital marketing.
What Are The Key Barriers to Progress?
Putting all this into practice and taking full advantage of generative AI can’t be achieved overnight. It can be a complex process, and many organizations feel they need support in navigating all the challenges along the way, such as:
- Computing Infrastructure Requirements: Putting the substantial computing resources in place for training and running GenAI models, so as to avoid performance bottlenecks and unnecessary costs.
- Data Quality and Preparation: Ensuring data is clean, comprehensive, representative, and therefore able to support quality, reliable generative AI output.
- Technical Debt Management: Considering the architecture, maintenance and scalability changes that are needed to support generative AI, and how to implement them in the most cost-effective ways.
- Hallucination Management: Deploying robust verification systems and human oversight controls to ensure that all content generated is reliable, accurate and free of bias.
- Intellectual Property Usage: Avoiding potential risks of copyright infringement, attribution and data ownership, which are often caused by generative AI models using training data they shouldn’t be to generate outputs.
- Regulatory Compliance: Adopting flexible compliance strategies to maintain legal, ethical and responsible use of AI in a rapidly evolving regulatory landscape.
- Bridging the Skills Gap: Seeking alternative ways to access the necessary skills to develop, deploy and maintain GenAI systems, at a time when those skills are difficult and expensive to procure.
In Summary: Building a Future-Ready Generative AI Strategy
As generative AI continues to evolve, integrating different types of data and deploying increasingly specialized AI models will be important to stand out from the competition. That will help generative AI tools become more deeply embedded in business operations, and increase the value of the insights that they provide to drive organizations forward.
At Ciklum, we have extensive experience with supporting organizations like yours with generative AI deployments. With the help of our expert insights, we can help you:
To find out more on making this happen in your organization, contact the Ciklum team today.
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