Cracking the Code: A Playbook for Creating Cutting-Edge AI Products

Artificial intelligence (AI) and machine learning have become ubiquitous buzzwords, with companies in every industry looking to leverage these technologies to create innovative products and services. However, while the potential of AI is tremendous, simply throwing AI at a business challenge does not ensure success. Like all product management, building AI products requires thoughtful planning, assembling the right team, narrowing the focus to address specific business needs, and continuously iterating and improving. This article provides an overview of best practices for product managers seeking to lead teams in developing AI products that create real business value.

An Introduction to AI and Machine Learning

First, let's level-set on what we mean by AI and machine learning. AI refers to computer systems that can perform tasks that have historically required human cognition and decision making. Machine learning is a subset of AI focused on algorithms that can learn from data to make predictions or decisions without being explicitly programmed to do so.

There are a few key machine learning techniques product managers should be familiar with:

- Supervised learning algorithms train on labeled example input-output pairs to learn a function that maps new inputs to outputs. Classification algorithms predict categorical outputs while regression algorithms predict continuous number outputs.

- Unsupervised learning algorithms find patterns and structure in input data without labels to guide the process. Clustering algorithms group data points, while dimensionality reduction techniques simplify representations.

- Neural networks are computing systems inspired by the brain composed of layers of simple computing nodes connected together. Deep learning uses multi-layer neural nets trained on very large datasets capable of exceptional performance on complex problems like computer vision and natural language processing.

Leading an Effective AI Product Team

AI has become pervasive across industries, but not all applications actually move the needle for businesses or provide real improvements for end users. Product managers must be savvy about what use cases truly warrant an AI solution. Problems that would benefit from additional data analysis or enhanced decision making are prime candidates. Product managers should also deeply understand customer needs to identify the highest value opportunities to remove friction in workflows.

Once high potential AI product ideas are identified, many product managers make the mistake of letting the data drive the process rather than the business needs. Yet, an effective framework puts business priorities first in the following sequence:

1. Clearly identify business problems and metrics

2. Assess required data quality and availability

3. Build models matched to data constraints

4. Deploy products and measure against key metrics

5. Actively solicit user feedback to improve products

Jumping straight to data before aligning on what specific business goal a product will achieve often leads teams down dead-end paths. Product managers must connect data scientists with business leaders early on to ensure they deeply understand the key jobs end users need to accomplish and can identify the parts of those workflows where AI could add the most value. These may only address a small portion of broader goals initially, but maintaining tight scope prevents teams from losing focus.

For example, for a graphic designer needing to find stock photos for marketing campaigns, an AI visual search recommendation engine could drastically reduce searching and filtering time. The product manager would need to work closely with designers to map out all steps in their content creation workflows. This would identify the goal of reducing time spent between selecting images to license and actually downloading and inserting them into content as the highest value AI opportunity. Data scientists would then need a dataset of labeled stock images mapped to detailed annotations of image characteristics to train deep learning models. But by starting from user needs, data requirements become clear.

Cross-Functional Teams Are Key

AI products require tight collaboration across disciplines with very different backgrounds to be successful. At minimum, an effective AI product team requires the following roles:

- Product Manager: Responsible for connecting business goals to technical work, ensuring team priorities stay aligned to solving key user needs

- UX Designer: Designs intuitive, human-centered products improving usability

- Data Engineer: Builds and manages data pipelines from source systems to production models

- Data Scientist: Researches state-of-the-art techniques and builds highly accurate machine learning models matched to data constraints

- Software Engineer: Develops production infrastructure for analytics, data management, and machine learning model deployment

- DevOps Engineer: Manages cloud infrastructure, monitoring, alerts, and auto-scaling for reliability

- QA Engineer: Ensures product quality, handles testing, and monitors model performance outliers

This cross-functional team must work tightly together in an agile fashion, rapidly iterating on solutions while soliciting continuous user feedback. Models will improve over time as more data labelled by human experts expands knowledge. The team must also monitor for model anomalies indicating potential issues like data drift or biases.

The product manager plays a crucial role in maintaining harmony across domains and clearly communicating priorities as user needs evolve. They must deeply understand capabilities from low-level model optimization techniques up through end user workflows to make appropriate tradeoffs balancing speed and accuracy.

Start Small, Think Big

The most effective AI products focus on improving very specific components of workflows rather than attempting to replace entire human roles outright. While AI promises to augment human intelligence and productivity enormously in the coming years, current technology still has limitations. Setting realistic expectations around viable short-term goals versus moonshots prevents wasted effort while building organizational confidence in AI solutions. Teams should celebrate small wins, gradually expanding products’ capabilities and users over time.

With the right business focus, an understanding of tools’ maturity, and a collaborative, multidisciplinary team balancing user needs with technical constraints, product managers can lead the charge on developing truly transformative AI. The future looks bright for organizations putting in the upfront planning and hard work required to build AI the right way.