I have been fortunate enough in my career to implement artificial intelligence and machine learning technology into several products, most recently creating a “ASK AI” chatbot with default prompts to help marketing professionals. I even (many moons ago) designed a “What is your AI IQ” microsite for Microsoft. So I’ve learned a thing or two about how to integrate AI effectively.
To ensure AI is properly applied for any Software as a Service (SaaS) product, a product design and management team should consider the following process steps:
Define the Customer Problem Clearly
Ensure any AI-powered feature addresses a real customer pain point or delivering clear value. Are you making the customer more efficient? Are you providing intelligent insights that help them make better decisions? Are you personalizing aspects of the product based on what you can identify/learn from your customer’s use of the platform?
Additionally, keep it simple and don’t over-engineer your solution. Resist the temptation to use AI for novelty; focus on practical and impactful solutions. Just because you can use AI to do something cool doesn’t mean that it will have any benefit for the customer.
Understand your data
Ensure the data used for training is clean, relevant, as accurate as possible, and ethically sourced. Just because you have access to data doesn’t mean you should use it.
Get customer consent if their data is leveraged. Build transparency around data collection and usage, incorporating opt-out mechanisms where needed.
Make customers part of the solution
Ensure AI decisions and recommendations are explainable to users in non-technical terms. Most customers are not experts in machine learning and by providing context and background explaining what information informs the AI feature’s output increases confidence and usage.
Provide user controls to allow users to control and fine-tune AI behavior as necessary (e.g., adjustable recommendation filters). Provide feedback loops – mechanisms for users to provide feedback to improve the AI over time. Finally, ensure your system gracefully handle AI-related errors without disrupting the customer experience.
Prototype and test proposed AI features
Just because there is an “AI Gold Rush” doesn’t mean you need to rush your solution to market without the proper due diligence.
Use prototypes or mock-ups to simulate AI functionality and gather user feedback before full implementation. And conduct thorough usability testing to validate the AI’s effectiveness and user experience.
Use AI ethically
This is a no-brainer but must be stated. Regularly test for and mitigate biases in AI models, ensuring the solution does not have biases based on age, race, gender or social status. . And align with legal and regulatory requirements for AI usage (e.g., GDPR, CCPA). Do not cede control to the ML/AI technology – have human oversight and code in the ability for human intervention. And implement “guide rails” that prevents the feature from “making up stuff.”
Do iterative improvement
Establish key performance indicators (KPIs) to measure the effectiveness of your AI powered feature(s) (e.g., accuracy, adoption rate, ROI). Incorporate real-world usage data to refine models and improve outcomes.
Communicate the value proposition
Clearly articulate how the AI feature improves the user experience or solves a problem. Slapping a “AI” icon onto the UI is not enough. Highlight the benefits (and use case). And communicate with transparency. Educate users on how the AI works, why it was implemented, and how it benefits them.
Closing
By taking these steps, you can ensure that AI is thoughtfully and effectively implemented for your product, enhancing the customer’s experience while maintaining trust and usability. Now, get started!