As opportunities to support business growth with generative AI expand, leaders must look ahead to consider how gen AI will impact their operations now and in the future. In isolation, LLMs can rapidly produce boilerplate-level text and accurate basic code, serve a conversational function by prompting users to think differently or more deeply in response to prompts, and translate complex texts into conversational language. But in the long run, implementing gen AI tools in combination with other technologies, including other types of AI and machine learning, may be the most meaningful way to harness its power.

Bringing together multiple AI techniques, including generative AI and non-gen-AI tools alongside other technologies to serve a unified purpose constitutes a composite AI approach. Engaging this composite approach to combines separate AI functionalities to supplement the shortcomings of each can solve business problems that no one technology can address in isolation, extending the applicability of each tool.

Historically, many enterprise operations engaged AI solely within the framework of machine learning. On its own, however, machine learning has drawbacks. It demands a larger store of data than many organizations have available. It has limited reasoning capacity. And it isn’t easily made legible to human observation. LLMs, on the other hand, counterbalance each of these disadvantages. As such, introducing LLMs into machine learning systems dramatically increases the number of business problems ML can address. For example, effective predictive modeling needs massive data troves. LLMs can support more robust simulation scenarios with synthetic data generated from their own stores, specialized to the modeling task at hand, given appropriately defined parameters. Generative AI can also be used for tasks that support machine learning like text analytics, natural language prompting, and graph labelling.

Composite AI applications are already at work in many CRM and ERP systems, and in a variety of service contexts like help chats, virtual assistants, and digital concierges. Forthcoming composite AI applications will likely run deeper in enterprise operations, performing tasks like workflow automation, simulation, and low-code/no-code generation. In Cascadeo’s cloud management platform, Cascadeo AI, for example, gen AI increases the speed and accuracy of human-engineered ticket response by validating, analyzing, and suggesting remediation steps automatically.

Many analysts, including those at Gartner, recommend approaching implementation with generative AI’s significant risks in mind. Inaccuracy, bias, inadvertent plagiarism, and artificial hallucinations are concerns with any use of generative AI tools, and can be compounded when these technologies interact with your most sensitive data and become part of your workflow processes. Enterprise implementation of generative AI presents additional hazards, including intellectual property, data privacy, and cybersecurity risks, potentially hampering the enormous opportunities these new tools offer. Businesses must also manage customers’ AI-related fears alongside their own.

Composite AI integration requires expertise and care, with steps taken to defend against these known risks and others that may arise as the technologies continue their rapid evolution. Ethics and use policies are a essential starting points, with internal guidelines like assuring that no company or customer data is used in LLM prompts and that your data is not included in LLM training serving as simple examples of gen AI engagement rules. Safeguards should be articulated to customers, as well, to quell their AI anxieties. Working with AI experts can help assure that your composite AI integrations remain secure and operate ethically as they support increasing sophistication and growth throughout your enterprise.

About the Author: 

Karol See is Head of Product for Cascadeo AI, an advanced cloud-management SaaS tool with the power of human expertise and generative AI.