Generative AI in Enterprises: Overcoming Challenges for Successful Implementation
Generative AI technology has captured the imagination of people worldwide, offering exciting possibilities for enterprises to unlock value and achieve a return on investment (ROI). However, implementing generative AI use cases in the enterprise comes with its own set of challenges, risks, and barriers. Let's delve into some of the major obstacles that need to be addressed.
1. Bring Your Enterprise Data to AI Vendors: To leverage generative AI, enterprises often need to provide their proprietary data to AI vendors and hyperscalers. However, this approach poses risks. Firstly, there is a concern that the vendor may use the data for training their own AI models without your knowledge. Secondly, AI models trained on your data could potentially be shared with other customers who may not possess similar rich datasets. Finding trusted vendors who prioritize data privacy and security is crucial.
2. Trusted, Secure, and Compliant AI: Enterprises must exercise caution when evaluating generative AI solutions and ensure that the generated answers or responses are accurate, reliable, and compliant. Language models have the tendency to generate fabricated information, sometimes with a high level of confidence. Evaluating hallucination rates, accuracy benchmarks, and citation of responses becomes essential to establish trust in the AI-generated outputs.
3. Embedded AI in Existing Workflows: For generative AI to be effective in the enterprise, it needs to be seamlessly integrated into existing applications and workflows. Standalone AI solutions that operate in silos can limit the value and adoption of the technology. By embedding generative AI into tools such as customer support platforms, like Salesforce Service Cloud, employees can benefit from AI-generated answers right within their familiar working environments.
4. Continuous and Adaptable AI Learning: Initial AI models deployed in production are often based on limited assumptions about the real world. To ensure continuous performance improvement, a framework and pipeline that encompasses user feedback, expert feedback, and new data sources must be established. This enables both automated and human-in-the-loop reinforcement learning, enabling AI models to learn from various dimensions.
5. Data Privacy and Protection: Data privacy remains a significant barrier to widespread enterprise adoption of generative AI. Instances of sensitive data leakage due to the inappropriate use of language models have raised concerns. Enterprises need to ensure they have appropriate safeguards in place to protect sensitive information when leveraging generative AI technology.
6. Deep Integration into Workflows: To unlock the true value of generative AI, it must be deeply integrated into existing enterprise and user workflows. Whether it's improving search capabilities to save employees time or providing customer support agents with proactive recommendations for handling frustrated customers, generative AI must align with specific action workflows. Language action models (LAM) are emerging as purpose-built models focused on generating workflow actions rather than just generating text or images.
7. Prohibitive Cost of Implementation: Building and maintaining a generative AI stack can be costly. Enterprises have options to either build the entire stack in-house or choose specific components to buy off the shelf. Costs associated with training, inference, personnel, testing, maintenance, and infrastructure should be carefully considered to assess the long-term financial implications.
While there are challenges to overcome, enterprises should consider embracing generative AI technology. Working with vendors that prioritize safety, trust, and domain-specific language models can be advantageous. Turn-key generative AI applications, such as GPT, copilot, and search, that offer a promising ROI and time-to-value can also facilitate successful implementation.
In conclusion, generative AI technology holds immense potential for enterprises but requires careful consideration of the challenges and risks involved. By addressing data privacy concerns, ensuring secure and trusted AI outputs, deep integration into workflows, and continuous learning, enterprises can reap the benefits of generative AI and drive innovation in their organizations.
