Generative AI is also potentially transformative in business when used in combination with other tools, especially traditional AI and machine learning (ML) models.
Generative AI is also potentially transformative in business when used in combination with other tools, especially traditional AI and machine learning (ML) models. This implementation method, called composite AI, strategically fuses multiple types of generative and traditional AI alongside other technologies to achieve a singular, cohesive objective.
By combining diverse AI functionalities, composite AI can effectively address the individual limitations inherent in standalone systems. As a result, composite AI has the potential to tackle intricate business challenges that would be insurmountable for a single model.
Historical Context and Comparison
Before the emergence of generative AI, many enterprises used machine learning to drive innovation, which presented limitations with data storage and reasoning capabilities.
These shortcomings can be supplemented by generative AI via large language models (LLMs) that possess the ability to create synthetic data and articulate complex algorithms in plain language.
Current and Future Applications of Composite AI
Composite AI has already made inroads into various business applications such as customer relationship management and enterprise resource planning systems. Additionally, tools such as help chats, virtual assistants, and digital concierges have enhanced human-AI interactions.
Future use cases are also expanding for composite AI, including workflow automation, simulation tools, and low-code and no-code software development that stands to democratize specialized areas of software development. One company that specializes in cloud infrastructure, Cascadeo, has also launched a platform to help companies manage cloud deployments using composite AI– one among many advanced AI offerings available in a rapidly growing market.
“Composite AI holds significant potential to improve how many businesses operate and interact with their clients or customers,” said Jared Reimer, Co-Founder, President and CTO of Cascadeo in a recent interview. “Equally difficult will be the processes of stringing together otherwise disparate AI systems into one composite whole, a new service we’re digging into and believe will grow significantly in the future.”
Risks and Recommendations
Despite its benefits, composite AI systems do pose some risks. Analysts note that generative AI can produce erroneous or even totally fabricated outputs. This can include the amplification of inherent biases, plagiarism, and AI “hallucinations” where systems fabricate data that is accurate enough to seem correct, but contains significant problems that render it unusable.
As businesses dive deeper into AI integration, there are also heightened concerns around intellectual property, data privacy, and cybersecurity, which are all pivotal for maintaining the trust and integrity of both the organization and its stakeholders.
Beyond these general risks, enterprises also face the unique challenge of addressing anxieties around AI that could emerge among investors and customers, especially as these systems become more outward facing.
Given these challenges, experts recommend that businesses approach composite AI with a healthy dose of caution and expertise. As with any process that poses risks, companies are best served by engaging experts, setting clear guidelines, and continuously educating both internal teams and their customer bases.
Ethical and Security Considerations
Moving forward, businesses should also carefully consider the ethical factors of their composite AI integration strategies, experts say.
This can be done by establishing clear ethics and use policies that are enforced internally and made visible externally (particularly to key stakeholders) to ensure a sense of transparency and awareness.
Additionally, experts also suggest that it’s paramount to ensure that no proprietary company or sensitive customer data finds its way into LLM prompts, which can be done by making sure data remains excluded from LLM training sets. This measure not only upholds ethical standards but also serves as a crucial data protection strategy.
Generative and composite AI have indisputably marked a significant shift in the business landscape, revolutionizing operations, customer interactions, and approaches to enterprise challenges. Prioritizing transparency, continuous education, and robust ethical and security protocols will play key roles for businesses aiming to responsibly harness the vast potential of composite AI now and in the years to come.
Victoria Barrett serves as Marketing Manager for Cascadeo, and is a veteran writer, editor, English professor, and publishing professional. Her writing has appeared in Washington Post, Salon, and a variety of literary journals.
This piece was written by an outside contributor. Guest contributors are not employees or staff of TechEchelon, and their views do not necessarily reflect those of the publication. The contributor's full bio appears in italics at the foot of the article.
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