Unlocking AI Product Design & Management

Learn how to leverage the top roles and responsibilities for AI projects

Artificial Intelligence (AI) adoption is skyrocketing across all industries, with more and more businesses revolutionizing their operations through AI. According to a 2022 research study by IBM, 35% of companies are already using AI in their business, and an additional 44% are working to embed AI into their applications and processes. Business and society are at a turning point, and it’s never been a more exciting or nerve-wracking time to be a product designer. But where do you start when you want to implement AI into the product experience?

When I began my AI journey as a designer seven years ago, resources and guidance were scarce. There wasn’t much reference material produced from the perspective of designers, so I was faced with the challenge of finding out who to contact, where to look, or how to even get started. I forged my own path, uncovering the intricacies of AI and Machine Learning (ML) in user and business contexts.

I started by gaining knowledge about the definition of AI and its applications in user and business contexts. As I began designing an AI product experience, it became clear to me that successful AI initiatives require close coordination between numerous teams with a variety of specialized knowledge and abilities. I soon learned that the success of these projects depends on clearly defining AI goals from a business and user perspective, developing a clear product AI landscape, and identifying various roles and responsibilities. Putting these in the right context is crucial. So, let’s dive right into how you can achieve this.

Below I want to take you through 7 key steps to begin integrating AI holistically into the product experience by activating some of the most common roles and responsibilities in AI cross-teams.

#1: Identify the business goals with stakeholders and product managers

One of the most important tasks of designing an AI product experience is to identify the business case for embedding AI in the first place. You must be able to define how AI will solve the user pain points intelligently, and this is where Line of Business stakeholders and Product Managers play a crucial role. Their job is essentially to identify user pain points and uncover business opportunities to integrate AI experiences throughout the end-to-end journey. Their responsibilities include defining valuable AI use cases, validating their impact on users, customers, and the organization as a whole. Additionally, these stakeholders prioritize AI initiatives on the roadmap, and craft persuasive marketing and sales messages for new AI features. Getting in touch with them first will set the base for creating the right solution for your customer.

#2: Translate business requirements with business analysts

Business Analysts are responsible for translating business requirements into technical requirements. They work with the business team to understand their needs and ensure that the machine learning models meet those needs. The business analyst also communicates the results of the models to the business team in a way that is understandable and actionable.

#3: Bring a human-centered perspective with user researchers, UX designers, and user assistants

Bringing together User Researchers, User Experience Designers and User Assistants will help you define valuable AI use cases and facilitate Design Thinking workshops to bring business, technology, and user insights together. They differentiate AI by bringing a human-centered perspective to the end-to-end product experience. The team creates the user experience in the product tests, validates, and improves AI through user research and testing, and delivers visual demos for sales and marketing of AI features. The user assistance team comes into play when defining the right tone and language for the AI experience that users can trust. This is especially important when you are building conversational UI.

Some of their outputs include:

• Defining AI guidelines and design principles that guide the development process from idea to implementation.
• Analyzing user insights and customer needs to understand the demand for automation in the product experience.
• Creating AI user journeys to identify opportunities for AI integration throughout the user journey.
• Understanding how AI can enhance human productivity by addressing specific user pain points and leveraging technological capabilities.

By engaging in these collaborative activities, the team can deliver an AI experience that is aligned with user needs, enhances the overall product experience, and maximizes the value and impact of AI in the end-to-end journey.

#4: Build and maintain machine learning models with data scientists

Data scientists work closely with the business and UX team to understand their needs and develop models that solve business and user problems. They ensure data is accurate, consistent, and properly labeled. They gather and prep data, develop methods to build clean and meaningful data to build the AI model, build AI models from proof of concept to delivery, and regularly maintain models and data after initial delivery. The team also delivers documentation for AI features.

Some of their activities include:

• Articulating the problem in categories such as classification, clustering, regression, and ranking
• Establishing data collection mechanisms
• Checking the quality of the data
• Formatting data to ensure consistency
• Reducing data
• Completing data cleaning
• Creating new features from existing ones
• Joining transactional and attribute data
• Rescaling data
• Discretizing data

#5: Deploy and maintain machine learning models with the engineering and development team

This team works with Data Scientists to understand the models and to deploy them in a scalable and efficient way. They also monitor the models to ensure that they are performing correctly and make updates as needed. They are also responsible for collecting, processing, and storing data. They design and maintain the data pipeline that feeds data into the machine learning models. The data engineer ensures that the data is accurate, consistent, and secure. They build front and back-end environments to deliver AI models and user experiences, incorporate instrumentation into the AI user experience to monitor accuracy and capture user feedback, and perform quality assurance (QA).

#6: Get the support you need from Marketing, Sales, and customer representatives

These roles provide resources to validate the value and impact of the AI use case and how it can best be messaged back to customers and users. They build product demos that are focused on AI features in the product and speak directly to the needs of the customers and the end users.

#7: Check transparency and compliance with the AI ethics and legal experts

These experts play a crucial role in AI projects by ensuring that the development, deployment, and use of artificial intelligence systems are ethical, fair, transparent, and compliant with relevant laws and regulations. Usually, product managers are the ones initiating collaboration with the AI ethics and legal team. Their activities revolve around addressing the ethical and legal implications of AI technologies which may include:

• Establishing ethical frameworks
• Conducting risk assessments to identify potential ethical and legal risks associated with AI projects
• Ensuring that AI projects comply with relevant legal requirements, standards, and regulations
• Addressing privacy concerns related to the collection
• Storing and processing personal data by AI systems
• Mitigating biases in AI algorithms and models that can lead to unfair outcomes or discrimination
• Advocating for transparency in AI systems by promoting the use of interpretable and explainable algorithms
• Facilitating discussions and collaborations with various stakeholders including developers, data scientists, policymakers, and end-users
• Conducting social and ethical impact assessments to evaluate the broader implications of AI projects
• Contributing to the development of AI policies, guidelines, and regulations at organizational, national, and international levels

The key to AI product design is collaboration

Designing an AI product experience requires collaboration, brainstorming, and a focus on the unique problems AI can solve beyond human capacity. By augmenting machines to understand users and their needs, we aim to enhance human capabilities. This collaboration comes with greater responsibilities to build ethical and meaningful intelligent product experiences. Each team member plays a vital role, and having a shared goal is key.

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Experience matters. Follow our journey as we transform the way we build products for enterprise on www.sap.com/design.