How to Build Trust and Transparency in Your Generative AI Implementation
Adoption of generative AI (gen AI) is on the rise, with more than 50 percent of U.S. workers surveyed saying they use the technology in their work, according to the Wharton School of Business. What’s more, businesses across all industries are eager to take advantage of this technology within their business processes: 80 percent of leaders plan to adopt gen AI within three years. According to research by McKinsey, however, organizations are encountering greater challenges than anticipated in harnessing the full potential value of gen AI.
For businesses to get the necessary impact out of their investments and ensure a positive experience for their organizations, their partners and their customers, they need to adopt gen AI the right way. Behind successful usage of gen AI, there should always be robust data governance, security and accountability. Any business adopting gen AI, for whatever process, needs to ensure that trust and transparency come first and by design, not just as an afterthought. This is where the fusion of intelligent automation (IA) and gen AI make for a winning combination.
How Does Intelligent Automation Enhance Generative AI?
Intelligent automation acts as the intermediary between an organization’s people, technology and generative AI because it automates and orchestrates processes end-to-end while also providing a detailed audit trail. This trail helps with regulatory compliance, accuracy and trust — critical factors in any industry, but especially those working with highly sensitive data.
Gen AI and Intelligent Automation in Action
There is no shortage of examples and applications where gen AI can make a difference. Organizations can automate faster and speed up process discovery and development by enabling users to write prompts to create processes, automations and other components. The decision-making process can improve with gen AI by making accessing and analyzing data easier. The complexity of automations can be lessened by smoothly integrating more intricate and nuanced scenarios into current processes, with minimal disturbance or compromise on quality.
You need to be able to hold a gen AI accountable and audit it, however, and you need to be able to tell it what to do so it can learn what information it can retrieve. Combining gen AI and intelligent automation serves as the linchpin of effective data governance, enhancing the accuracy, security and accountability of data throughout its lifecycle. Put simply, by wrapping gen AI with IA, businesses have greater control of data and automated workflows, managing how it is processed, secured from unauthorized changes and stored. This process wrapper concept will allow you to deploy gen AI effectively and responsibly.
Intelligent automation acts as the intermediary between an organization’s people, technology and gen AI because it automates and orchestrates processes end-to-end while also providing a detailed audit trail. This trail helps with regulatory compliance, accuracy and trust — critical factors in any industry, but especially those working with highly sensitive data.
In the insurance industry, for instance, automating tasks involved in prior authorization and referrals can improve efficiencies and productivity. For example, intelligent automation can consume handwritten prior authorization and referral forms to find missing data points and submit them for approval at a much greater speed and accuracy than a human. This results in cost and time savings, lower denials and increased patient satisfaction.
Digital workers can also enhance customer service centers because they can retrieve previous customer interactions from internal systems so the gen AI can summarize the record. Gen AI gathers all the key relevant data and gets that information to your customer service agents so they don’t have to sift through tons of data themselves. Intelligent automation orchestrates your workflows consisting of humans, AI and digital workers, end-to-end.
Future-Proofing Generative AI investment
The transparent adoption of gen AI is imperative right now, as innovation continues to grow at a rapid pace. In the past year, we’ve seen significant innovations in using language learning models (LLMs) and gen AI to simplify automations that tackle complex and hard-to-automate processes. According to IDC, this includes large enterprises relying on AI-infused processes to enhance asset efficiency, streamline supply chains and improve customer satisfaction.
Five years ago, AI tools and models were fairly limited and had narrow applications, but now with off-the-shelf learning models and applications requiring few complex skills, the only barrier to entry limiting gen AI adoption is data quality. Whether you’re a manufacturing powerhouse or global financial institution, summarizing vast quantities of unstructured data is a challenge for the C-suite and revenue teams alike.
As an example, global consulting firms such as Accenture, EY and Deloitte release separate reports on the uptake of gen AI and how to implement it. Gen AI can summarize those reports or articles to provide an overview for your C-Suite, saving time and enabling you to drill down for more details, such as the best use cases to start with and how to measure success. Executives gain a wider point of view with cross learning from multiple opinions, allowing for a deeper and more relevant understanding and experience. With this knowledge, executives can ask questions regarding their industry, company and department. This can help them develop a strategic document on a particular topic.
Forrester’s AI Pulse Survey highlights that it is time to move beyond the hype surrounding gen AI to strategic implementation because, as gen AI adds pressure on systems, measurement becomes unpredictable, complicating insight delivery. Because of gen AI’s added demands on your current systems, managing security, privacy and consent adds another layer of complexity. Machine learning’s random nature demands live data sets for measurement and monitoring, lacking a standard linking gen AI models to source data, increasing uncertainty and risk. This is the single biggest barrier to adoption of gen AI by B2B enterprises.
For example, ChatGPT can help you write a letter, but it cannot help you run a risk or fraud report for an enterprise based on customer data because that information is part of the general data set. As a predictive tool, gen AI can make stuff up or hallucinate ideas based on vast learning data sets. You need to use your own data and build standards through machine learning on top of LLM models to give you relevant outputs.
Responsible Automation
Orchestration through an intelligent automation platform can trigger requests between digital workers, AI and other systems. Digital workers source data from a variety of inputs for the AI to use when generating content, and all digital worker actions are traceable throughout the entire process lifecycle. Guidelines are designed to improve prompt quality for better output. AI draws on data in the first stage to create an informed output and digital workers use the output to update internal/external systems.
Guardrails such as human-in-the-loop oversight and audit logs provide transparency and explainability. With the right guardrails in place via a process wrapper like intelligent automation to control data input, output and training models, gen AI can transform how a business automates its processes. By combining gen AI with intelligent automation as the process wrapper, organizations can ensure the security of their data management and transparency.
IDC predicts that global spending on artificial intelligence (AI) will exceed $500 billion by 2027, with a substantial share of this investment expected to target the U.S. market. With a surge of offerings from vendors, organizations need to sift through the hype and realize actual business value.
Cloud, data, AI, and automation software will continue to push boundaries and overlap with others to create unique applications. As organizations continue to invest in these technologies and a digital workforce, they can future-proof their data management and ensure that they can make well-informed decisions, while maintaining trust and transparency in their operations through intelligent automation.
