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Three key considerations for AI and RPA risk management

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

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 comes with risks, careful planning and foresight can help mitigate them. AI and RPA risk management in practice should involve:

  1. Weighing risks and benefits and ceding some control to employees

  2. Upskilling your employees so they can use RPA and AI more effectively and responsibly

  3. Creating a risk management framework to place guardrails and governance around RPA and AI activities

AI and RPA risks and challenges vs. benefits

At PwC, we’ve witnessed the benefits of RPA and AI 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 actually 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.

AI is also an efficiency powerhouse. With the right data and AI 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. 

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 best. 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. 

AI 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. 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 AI can drive better outcomes 

Implementing automations and AI 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. 

It’s important to teach people how to build digital assets by giving your people access to a sharing platform that allows them to find and distribute employee-built automations and AI models. Through upskilling, your company can promote citizen-led innovation, which, in turn, will become the engine for digital transformation.

Create an AI 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 avoid a wild west of error-filled digital assets getting built and shared across the organization.

Having a single, centralized repository to place employee-built assets and designated, qualified reviewers is key. There are ready-made platforms to help provide governance guardrails that enable citizen-led innovation, while also making citizen-built assets more secure. 

For AI specifically, choose an operating model with a consistent approach to data, governance and AI use across your organization. Create responsible AI use toolkits 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 update controls around AI use, making sure they cover every stage of the AI life cycle.

Aligning AI operations with RPA and other automation tools––as well as operations for data and analytics—sets digital leaders apart. Consider appointing AI and automation leaders, or even creating automation and AI centers of excellence, to maximize the benefits and minimize your risks. 

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