Is Your GenAI Chatbot a Reputation Time Bomb? How to Scale AI Safely with a Responsible Framework

Ready to Deploy Generative AI? Why You Need a “Responsible AI” Roadmap to Prevent Costly Errors

Stop gambling with your brand’s reputation. Discover why deploying Generative AI without a safety net is dangerous, and learn Boston Consulting Group’s 5-stage “Responsible AI” (RAI) framework to mitigate risks like data leaks and bias while building customer trust.

Don’t wait for a public PR disaster to fix your AI strategy—continue reading to access the 4 actionable steps that will secure your GenAI applications against drift and operational failure.

Recommendation

Many firms are rushing to reap the benefits of GenAI, without carefully considering the serious risks it can pose. These risks range from the leaking of sensitive customer data to the use of offensive or biased language, both of which can damage your firm’s reputation and erode trust. Boston Consulting Group created a new Responsible AI (RAI) framework to guide firms in identifying these risks, and suggests four steps firms can take today to start leveraging this powerful new technology more responsibly.

Take-Aways

• GenAI poses serious risks to companies using it to engage consumers — Boston Consulting Group’s RAI framework can help protect your firm.
• Leverage an RAI framework throughout all stages of your GenAI application’s life cycle, continuously improving your system.
• Start mitigating GenAI’s risks and build stakeholder trust with four action steps.

Summary

GenAI poses serious risks to companies using it to engage consumers — Boston Consulting Group’s RAI framework can help protect your firm.

While Generative AI (GenAI) has massive potential when it comes to engaging customers with greater personalization and efficiency, the risks of deploying GenAI are considerable. For example, one car dealership’s chatbot made a costly error when it offered customers a car for only $1. Other costly GenAI errors can include biased hiring and consumer and corporate data leaks. Given that GenAI systems are nondeterministic — in that they produce multiple and varying responses to the same questions and converse with several users simultaneously — firms may struggle to prevent such system lapses. However, while it may be tempting to simply accept GenAI’s risks, treating them as “one-off” errors when they arise, doing so can perpetuate misinformation and trigger intellectual property (IP) concerns. Conversational GenAI agents, such as chatbots, can also cause severe damage to your brand and valued customer relations if they go off-brand (for example, by using offensive language).

“GenAI promises immense value to companies that can utilize it responsibly and accurately. But companies must update their established development practices in order to maintain control of the output of this powerful technology.”

To unlock GenAI’s full potential, while mitigating risks, consider deploying Boston Consulting Group’s robust responsible AI (RAI) framework, as you build and deploy your firm’s GenAI agents at scale. The RAI framework offers companies a comprehensive approach to both minimizing risks and aligning all aspects of the AI life cycle — from coding to deployment — with your corporate values, while upholding principles such as accountability, privacy, security, and fairness. Leveraging this RAI framework across your full application life cycle will enable you to build GenAI-based applications that people can trust with their data, while ensuring you remain compliant with the laws and regulations governing data collection and responsible AI.

Leverage an RAI framework throughout all stages of your GenAI application’s life cycle, continuously improving your system.

Embracing an RAI framework can help you better serve customers, protect your brand, and mitigate risks across the following stages of your application’s life cycle:

1. Design — The RAI framework guides you through mapping use cases, assessing risks, and defining the questions you hope your system will answer and the values you hope it will reflect.
2. Code — The methodology steers you through challenges throughout all stages of your development process, ranging from prompt engineering to integrating your application with your existing frameworks and systems.
3. Test and evaluate — Implement testing and evaluation frameworks that ensure the proficiency, security, compliance, equity, and safety of your GenAI application, while also performing traditional software assessments. Consider using a toolkit to ensure testing includes human-centered red teaming.
4. Deploy and release — To keep your AI system secure, multilevel development, including staging, quality assurance, and production environments, must take place to ensure interoperability and security.
5. Operate and monitor — Large language models (LLMs) can become more prone to errors or “drift” over time, making sustained performance monitoring vital. Engage in consistent monitoring to ensure you have access to real-time data insights, which you can leverage to improve your agent’s performance and the safety of your AI systems.

Start mitigating GenAI’s risks and build stakeholder trust with four action steps.

Unlock GenAI’s advantages and protect your firm by initiating the following actions as you adopt the suggested RAI framework:

1. Create a “development road map” — Carefully define the main risks you anticipate at each release stage, and the ways in which these insights will guide you in implementing the necessary security protocols, prompts, and guardrails.
2. Establish best practices — Build stakeholder trust by establishing an “RAI-based GenAI testing and evaluation suite, framework, and process.”
3. Scale best practices across activities — Establish a process to launch new applications and features in alignment with responsible AI (RAI) standards. Provide your organization with guidance on maintaining constant oversight and risk mitigation for GenAI applications with RAI-based frameworks.
4. Craft your response plan — Clarify the steps your organization should take, if a system drifts, behaves erratically, or fails. These should include the key contact points and a response plan for each team, helping you swiftly mitigate the cost of system failure.

About the Authors

Eric Jesse, Vanessa Lyon, Maria Gomez, and Krupa Narayana Swamy are professionals at Boston Consulting Group.