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Three key considerations for GenAI and RPA risk management

Putting the power of big data to work for your company means identifying opportunities to implement automation and generative artificial intelligence (GenAI).  

Tools like robotic process automation (RPA) are designed to drive efficiencies by making it easier to complete time-consuming tasks. They can also free up time to dedicate to value-add activities that benefit both individuals and the enterprise. 

These digital tools can streamline processes, save hours of manual work, reduce errors and costs and increase speed to insights. Although implementing new technologies likely comes with risks, careful planning and foresight can help mitigate them. GenAI and RPA risk management in practice should involve: 

  • Weighing risks and benefits and ceding some control to employees 

  • Upskilling your employees so they can use RPA and GenAI more effectively and responsibly 

  • Creating a risk management framework to place guardrails and governance around RPA and GenAI activities 

GenAI and RPA risks and challenges vs. benefits 

At PwC, we’ve witnessed the benefits of RPA and GenAI firsthand.  

We’ve helped clients transform using RPA to shave hours off processes and transform manual controls into automated ones that operate consistently. RPA is also scalable and mitigates risk. The vast majority of companies who implement RPA see reduced errors, improved process efficiency and enhanced customer response times. And 77% see improvement in the quality and consistency of decision-making. 

GenAI is also an efficiency powerhouse. With the right data and GenAI models, companies can help predict future market conditions and their impact to help you prepare for new threats and seize opportunities. These kinds of insights can improve decision-making around workplace investments, staffing and go-to-market strategies. GenAI can also help save countless hours of manual labor and tedious, routine tasks freeing employees to focus on more creative, strategic activities. 

But there are challenges to consider when implementing these technologies. 

With RPA, process ownership and leadership buy-in can become roadblocks, especially if business strategy and automation implementation plans don’t align.  It can also be challenging to identify where automation will work appropriately. Some processes are still better handled with a traditional, process-first approach and require heavier controls and governance. These tasks may not lend themselves to automation without significant risk.  

GenAI also has its share of risks. Insufficient, or even flawed, data can cause models to become unstable. Biased code writers can end up running a model that delivers skewed results or results that contain hallucinations. Security considerations, like data breaches that allow hackers to access data through backdoors, could end up having negative consequences for consumers.  

Upskilling in RPA and GenAI can help drive appropriate outcomes  

Implementing automations and GenAI models can help employees solve the real problems of their everyday work. A combination of both can streamline tasks, get people the insights they need faster and free up time for creative thinking.  

Through upskilling, your company can promote citizen-led innovation, which, in turn, will become the engine for digital transformation. 69% of CEOs believe that GenAI will require most of their workforce to develop new skills in the next three years. 

Create a GenAI and RPA risk management framework 

After you’ve upskilled your people to use these new technologies, you need a system of curation and vetting to help avoid a wild west of error-filled digital assets getting built across the organization. 

For GenAI specifically, choose an operating model with a consistent approach to data, governance, and AI use across your organization. Create responsible GenAI practices around how you’ll utilize the tech and distribute and socialize them widely to help reduce bias.  

Limit access to more sophisticated AI models and use cases to data scientists and data engineers. Additionally, be sure to continually take precautions to scale responsibly

Aligning AI operations with RPA and other automation tools — as well as operations for data and analytics — sets digital leaders apart.Consider appointing GenAI and automation leaders, or even creating automation and GenAI centers of excellence, to help take advantage of the benefits and decrease your risks. At PwC, each of our GenAI factory pods contains business analysts, data scientists, data engineers, and two GenAI-specific roles — a prompt engineer to refine the GenAI model’s output and a model mechanic to oversee and customize the model’s inner workings.  

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