Navigating the Generative AI Revolution: Smart Integration Strategies for Business Growth
The groundbreaking impact of ChatGPT and its potential applications have generated significant buzz throughout the automation industry. Generative AI now exhibits human-like capabilities in creative and cognitive tasks. One notable example is that reports indicate GPT-4 has passed exams such as the USMLE and BAR, which typically require years of human study. As a result, businesses are increasingly exploring the adoption of generative AI to drive innovation and growth acceleration.
While early adopters have primarily focused on content creation, generative AI is now making notable strides in the realm of automation. A striking 90% of leading companies have established centers of excellence for automation and 75% of Fortune 1000 companies have already invested in automation. The efficiency gains offered by generative AI hold the promise of a hyper-automated future for enterprises, enabling them to automate even those processes previously deemed low return on investment (ROI) by traditional robotic process automation (RPA) solutions.
However, integrating generative AI into existing processes and systems presents challenges due to the novelty of the technology, along with privacy and safety concerns. Finance, sales and legal departments, in particular, demand reliability, auditability, predictability, privacy and safety from automation systems. Despite their remarkable capabilities, large language models (LLMs) have exhibited biased or flawed reasoning based on their training data, generating “hallucinations” or inconsistent responses. While enterprises recognize the transformative potential of GPT technology, concerns about inaccuracies and false assertions are creating anxiety in business leaders tasked with delivering on a generative AI plan for their business.
The following strategies can help business leaders proactively ensure the safe enablement of their businesses in the new world of generative AI:
Incorporate human review into generative AI tasks
Begin by leveraging generative AI for tasks that allow easy post-generation human review to ensure accuracy and safety. This includes the creation of text content for marketing or sales, for summarization of reports in business workflows, and software development as well where Incorporating a human in the loop allows for the identification and correction of errors or biases before they impact business operations.
Adopt a “bag of models” approach
When incorporating generative AI, organizations should use a “bag of models” approach by utilizing specialized models for specific tasks, rather than relying exclusively on ChatGPT or a single AI model. This approach could include using OCR models for document understanding, GPT, Cohere and Anthropic for text understanding and procedures designed by HuggingFace or AWS Bedrock for domain-specific tasks. Employing best-of-breed models for given tasks helps reduce errors. Deploy a generative AI platform that allows control of models, as these are rapidly evolving today.
Prioritize data residency and privacy
When using generative AI to query business systems, ensure your company’s private data does not inadvertently become “common sense knowledge” within the AI model. This prevents essential information from leaking to future model users. Business leaders should look for generative AI platforms that can leverage databases (sql and vector), leverage AWS/Azure/Google accounts of the customers for LLM AI services.
Exercise caution with certain generative AI systems
It is important to be cautious when providing API access to Generative AI systems that lack robust, scalable pre-execution review mechanisms with human involvement. Reviews should be efficient and accessible to business users without requiring developer support or training in complex tools. The best language for review is natural language and that is the language that generative AI models speak. Having support for multiple natural languages is a plus.
Understand not all AI systems are a silver bullet
Acknowledge that no AI system is infallible and invest in technology that enables straightforward exception handling when using generative AI technologies. Business users should be able to resolve exceptions independently, without relying on developers or specialized prompt engineering expertise. This is especially true for automation systems where most of the inefficiencies in the previous generation of RPA solutions were around the inability to dynamically handle exceptions and learn. The newer generative AI technologies can solve this problem by the ability to capture exceptions then explain and resolve in natural language.
By implementing these strategies, business leaders can harness the immense potential of generative AI while mitigating the associated risks. This approach will ultimately drive innovation and growth within their organizations, ushering in a future of increased efficiency and improved responsiveness to market demands.