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Guiding Generative AI for the Enterprise: Why AI Governance Matters

Jim Kaskade

CEO

The critical elements of AI governance for the enterprise
The critical elements of AI governance for the enterprise
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Artificial IntelligenceBest Practices
Published 07/21/23
2 minutes read

As organizations embark on the journey to incorporate Generative AI into their operations, their concerns are not limited to user experience alone. Security, privacy, data regulations and robust governance rank high among their priorities. They aim to unlock the transformative power of Generative AI while adeptly managing potential risks.

The pivotal question facing businesses is whether to construct their own large language models, maintain granular control or partner with a trusted, enterprise-ready solution like Conversica.

AI Governance Necessities for Enterprises

Any enterprise-grade Generative AI solution needs to satisfy several crucial requirements, especially if the AI is going to interact directly with customers versus simply enabling human-to-human interactions:

  • Model Training: Building and training the model on client-specific data sets is crucial for ensuring the AI system understands and can cater to unique business needs, but most importantly represent the brand with approved content only.
  • Model Refinement: Defining and refining the model to steer towards desired business outcomes is an ongoing process, ensuring the AI system remains relevant and effective. Even if you use client-specific data for training data, the AI models need to be developed to create action versus just providing passive answers to questions. This will require a sophisticated level of model feedback on actions taken by the AI that informs whether the next-best action is accurate or not.
  • Automated Governance: Building automated governance reduces the risk of rogue or irrelevant responses from the AI, improving the quality and reliability of its output. But most importantly, if the AI is going to take action, governance becomes even more crucial.
  • Human-In-The-Loop (HiTL) Safeguards: Incorporating human oversight mechanisms ensures AI experiences remain consistently relevant and brand safe. This blend of human and AI efforts often leads to the most effective outcomes. If the model is confused, the human can intervene. If the model is incorrect, the human can optimize the model. Without human oversight, the AI will never achieve high levels of accuracy. Conversica’s ability to interpret AND take the correct next-best action is over 98% accurate because of HiTL.
  • Continuous Evolution: Constant evaluation and evolution of the technology as new enhancements emerge is key to maintaining a state-of-the-art AI system. Conversica has over 15 years of experience in applied AI.

What Happens Without These Guard Rails

However, the reluctance or inability to adopt these best practices could lead to several potential pitfalls, ultimately creating more liability than benefit:

  • Security Risks: Without proper governance, there’s an increased risk of data breaches and other security issues that could damage the organization’s reputation and bottom line.
  • Regulatory Violations: Inadequate attention to data regulations could result in non-compliance, leading to hefty fines and damage to the company’s reputation.
  • Poor User Experience: If the AI system isn’t trained and refined based on specific business needs and outcomes, it may fail to provide an optimal user experience.
  • Brand Damage: Without human oversight and brand safety measures, AI could potentially generate content that’s inconsistent with the brand’s values and voice.
  • Outdated Technology: Failure to continually evaluate and update the AI technology could result in the system becoming outdated, leading to decreased performance over time.

Therefore, robust AI governance is not merely an option but an absolute necessity for any organization seeking to harness the full power of Generative AI responsibly and effectively.

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