What’s your AI maturity?

Following are a few strategies that, if executed rightly, would bring a company closer to being a sustainable Expert in AI.

AI Success Framework 🧾

Our AI Success Framework consists of 4 dimensions that, once ingrained in the DNA of the company, can ensure long-term success, directly impacting the company’s bottom line.

Data Strategy 🔖

Data-driven teams are built on data strategies with a long-term vision. A data strategy should include the processes, tools, and plans used to generate, pre-process, and analyze data. Developing a Data Strategy is one of the most critical steps for AI success.

Here, we talk about the main components of a robust data strategy:

• Data Collection
• Data Pre-processing
• Data Governance

Data Collection

Data collection for training AI models is the most basic and critical step in the entire model development journey. Here you would capture records of past events and interactions. Predictive models learn from these events and interactions to capture patterns; hence, the input data is critical for accurate results. No modeling approach can fix the gap if the input data has substantial errors (Garbage In, Garbage out).

Below are some essential points which we need to consider during data collection:

• Data Streaming: The ability to process continuous data streams in real-time as they are generated.
• Data Pipeline: The data pipeline enables data to be moved from multiple data sources to a destination (for example, a data warehouse). During the process, data is transformed and optimized, arriving in a state that can be analyzed to develop features and business insights.

Data Pre-processing

Real-world data is generally noisy, has missing values, and may not be usable for AI models. Data pre-processing is crucial for transforming raw data and making it suitable for machine learning models, improving their accuracy and efficiency. In data processing, various steps are involved, such as transformations, imputation, dimensionality reduction, etc.

This step ensures an efficient AI development process. To underscore this point, according to a report in The State of Data Science 2020, data scientists, on average, spend 45% of their time getting the data ready for the development of models. Such a massive amount of time spent loading and cleansing the data takes valuable time away from data scientists, impacting their overall productivity.

Data Governance

Data governance refers to managing data throughout its lifespan, from acquisition to disposal. All the steps you take to ensure your data is accurate, accessible, secure, and usable are considered data governance.

Successful organizations consider the who, what, how, when, where, and why of all their data to ensure security and compliance while maximizing business performance.

PS – AI powered data governance is a thing now!

Team Setup 🤝

The most critical aspect of delivering AI success is building a symbiotic team structure equipped with the right capabilities to solve customer problems.

A matrixed organization structure is a great option to balance functional focus and technical expertise. Such a team structure will consist of verticals focused on specific business areas and solving problems by developing functional expertise. They would work closely with the horizontal technical experts who cut across the verticals to provide support.

Cross-functional “Data Product Teams” within each vertical are empowered to solve business problems end-to-end. In most of these advanced data product teams, you will see the following roles:

• Data Product Owner/Manager (PO): Partners with business stakeholders to understand their problems, translates these into product requirements/features, and prioritizes the team’s backlog to build the data product. The latter is done by assessing the relative value of features/requests and aligning the product roadmap with the stakeholders. Another key responsibility is to drive the data product’s adoption once it is in production.
• Data Analyst: Analyzing data using statistical techniques and developing reports, partnering with business stakeholders to share insights from the data product. The data analyst also provides the Data Scientist with valuable insights into developing the AI/ML model.
• Data Engineer: Responsible for developing, testing, and maintaining data pipelines and data architectures. Enabling the development of AI solutions and then deploying and maintaining them.
• Data Scientist: Apply data science techniques, such as machine learning, statistical modeling, and artificial intelligence, to solve stakeholders’ problems.A multi-disciplinary product team structure has several advantages in AI development. The primary benefit is that the technical team members are involved in developing the product features from scratch and hence have a better understanding of the stakeholder requirements. The PO also helps them shape the product with regular inputs to build a product aligned with the business needs.

A significant problem in a traditional setup of Analytics organizations is that the technical teams like Data Science and Data Engineering work in silos, and the deployment handover is a tedious and time-consuming process. In a Product team setup, all the team members are involved in the product journey from the beginning and have complete details of the features developed, the final requirements, and the timelines expected. Hence, any handover between disciplines is smoother.

The above team structure also ensures that synergies between disciplines are realized. For example, the analysis by the Data Analyst can help in feature engineering for the Data Scientist, and exploratory data analysis from Data Scientists can help drive insights.

Finally, the PO is the team member that sets the tone for the team tasks by ensuring the team is working on features that solve their stakeholders’ business problems. This ensures both timely delivery (efficiency) and business impact (effectiveness) of the product AI.

Tech Stack 💻

The technological landscape in this field is fast-paced. Multiple technological innovations are happening rapidly, and keeping pace with them is arduous. The tech stack for data science can vary depending on the specific project and goals, but some standard tools and technologies used in data science today include:

• Programming languages such as Python and R for data manipulation, analysis, and visualization
• Data storage and management tools such as SQL and NoSQL databases and Hadoop
• Machine learning and statistical modeling libraries and frameworks such asTensorFlow, Keras, PyTorch, sci-kit-learn, and caret
• Visualization libraries and tools such as Matplotlib, seaborn, and Tableau for creatingcharts, graphs, and other visualizations of data
• Cloud-based data platforms and services such as AWS, Azure, and GCP for scalabledata storage, processing, and analysis.The power, flexibility, and self-learning capabilities of latest wave of AI algorithms differentiate them from the past – together with the increased amount of data available. Today, raw power is sourced from deploying GPUs – processors initially designed for the heavy-duty task of generating sophisticated computer visuals, hence, computing power and storage (discussed above) are vital components of the tech stack for a successful AI strategy.

Outcome Driven approach 🎯

The single most important thing that differentiates successful AI strategies from the rest is the impact that the solutions create. With the number of AI technologies in vogue and the fear of missing out, teams usually fall into the trap of trying to fit a square peg into a round hole.

One should focus on developing a complete understanding of the customer’s problem to avoid getting into such a scenario. Problem definitions generally tend to turn into solution definitions which is a no-no. For example: “I want a recommendation engine on my website” is not the problem. The problem would be: “As a customer, I want to easily find products relevant to my needs/interests’. The recommendation could be one way of achieving this, but that is a decision that the product team and stakeholders should define.

Having an eye on the impact is another crucial step. “How can we define impact this early? The impact is not quantifiable!” are the types of comments you could hear, but one needs to have a rough idea of what business impact your solution will drive. Not only does this step give you a baseline estimate, however approximate it may be, but it also forces you to map the benefits flow of your solution. For example, improved search on the website leads to improved findability, which leads to better conversions.

We believe the above can help an organization point a team in the right direction toward AI success. This article can be summarized as three key takeaways:

1. Focus on a timely and clean data strategy
2. Build a synergistic cross-functional team structure
3. Execute the AI strategy with an outcome-driven approach.

These three key steps will set you on the path to AI success.