This article first appeared on AI Authority, authored by Mavenoid's VP of Product, Galina Ryzhenko.
Driven by the fear of missing out, many business executives race to innovate and implement new AI solutions as soon as possible. In their haste, these executives risk deploying an AI solution built with bad data or without fully testing the solution’s impact on their business. While being first to market can provide a competitive advantage and differentiation, an unsuccessful launch can do more long-term damage to your brand reputation.
To balance speed and safety when implementing a new AI service, consider the following steps.
7 Steps for AI Safety When Deploying a New Solution
1. Educate Yourself on the Solutions You’re Considering:
- Learn about AI’s capabilities and how different providers make use of those capabilities. Are these innovative approaches delivering better results than other technological options?
- Research the various AI options available to you and spend time with case studies that reveal whether that solution would address your specific business concerns. Look to industry-specific examples to uncover best practices for deployment, and understand any limitations. For example, medical device manufacturers may have additional HIPAA requirements and should pay extra attention to safety and guardrails of AI models they use.
2. Understand the Business Problem or Opportunity:
- AI alone won’t solve all your business problems, but understand that this new technology can be a powerful tool when deployed with purpose. Take the time to outline your pain points and then map AI’s capabilities to those areas to understand how you can drive specific impact with a new AI solution.
- For example, if you struggle to keep support documentation up-to-date with new product releases, generative AI can help suggest new content based on gaps in your support flows.
3. Consider Your Audience:
- AI includes many technologies such as computer vision, reinforcement learning and large language models (LLMs), to name a few. Most popular LLMs are trained with publicly available data and knowledge for training, including unverified sources such as Reddit or other online forums that may have biases. For this reason, it’s critical to understand the source data and models that trained the AI solution you are considering. If you decide to train your own model, or take an open-source one and finetune it, then you need to consider which data is used to ensure it doesn’t add biases or propagate inaccurate information.
- For example, an AI model trained on historical job description data may have gender biasesand recommend certain professions to the user, based on outdated gender roles in certain professions, such as engineering or teaching.
- Brands must also consider new regulations, such as the EU AI Act, that are becoming increasingly strict to address growing concerns about bias, privacy violations, and accountability in high-risk applications and ensure that customers’ and citizens’ rights are protected as AI continues to evolve.
4. Improve Your Data and Content:
- Some AI solutions will require your business data and content in order to deliver value to your customers. As with all AI models, source material determines the quality of the solution’s output. Therefore, when you implement an AI solution you will need to ensure that you feed it high-quality information.
- To improve the quality of your data and content, you should update incomplete or outdated data sets and update documentation with new product information. At the same time, you should ensure that all other services are connected via APIs or other integrations, in order to feed updated information into your AI solution. Build an analytics dashboard where you can benchmark and measure progress against business goals, allowing you to continually improve your support offerings.
5. Choose the Right Tools:
- Purchasing an off-the-shelf specialized AI tool is often safer, more effective, and less resource-intensive than building one from scratch. There are many third party analyst services such as G2 that can provide you with an analysis of the top AI vendors.
- Additionally, you may have an opportunity to test the solution, or talk to existing customers to understand the solution’s potential impact. Companies that fail to test solutions can experience significant issues, as evidenced by notable botches like those from Air Canada, Chevy, or New York City.
6. Create an Evaluation Framework:
- Develop an objective evaluation framework to assess the AI’s performance toward your business goals.
- For example, if you are applying AI to provide self-service customer support, a recommended framework could look like this:
- Run a sample of 1,000 real customer requests through your AI solution.
- Rate the accuracy of the responses on a scale from 1 to 5.
- Look at potential customer satisfaction (CSAT) based on these responses.
- Compare the AI’s answers to those previously given by humans, and decide if the AI’s performance is good enough.
- Additionally, you should consider:
- Check for speed and responsiveness—ideally, only 2-3 out of 100 requests should time out or be too slow.
- Test the guardrails. Try to “break” the system by asking tricky or inappropriate questions, like asking about competitors or trying to get it to sell something for $1.
7. Iterate with Your Vendor:
- The beauty of AI is that it learns, and improves, over time. Collaborate with your AI vendor to build a plan for how you can continuously improve the solution based on real-world data and feedback from your business. Ideally, your AI should learn and improve based on new data.
- You should also work with your AI vendor to gain insight into their roadmap for how they plan to integrate new AI capabilities over time.
You Don’t Have to Choose Between Speed and Safety
While the pressure to adopt AI quickly can be intense, especially with competitors racing to capitalize on its potential, it’s essential to approach implementation with care and strategy. Rushing into AI adoption without aligning it with business goals or evaluating the quality and safety of the solution can result in costly setbacks, including damage to your brand’s reputation. By following a clear framework, thoroughly researching AI options, and collaborating closely with vendors, businesses can quickly deploy and harness AI’s power while minimizing risks.
To access the full article from AI Authority, click here.
