Managing and Organising AI Talent

In spite of its potential, executing and achieving success in machine learning and AI projects can prove to be quite challenging. Frequently, even the most diligent efforts can be hindered by uncertainties surrounding a rapidly evolving technical landscape, perplexity about how to construct and organize teams effectively, and the struggle to discern hype from reality. Therefore, it is important to understand the execution challenges we will face at a holistic level before we pinpoint project opportunities, prioritise ideas, and evaluate the potential impact of these endeavors.

AI projects are distinct from traditional software engineering projects, necessitating dedicated efforts. Often, we start by establishing a robust data infrastructure capable of supporting machine learning or data science projects. It is advisable to perceive the development of AI and data-driven solutions as an iterative process, encompassing the creation, training, and deployment of a solution in a production environment. In the long term, our objective should be to operationalize AI by integrating it into all feasible workflows and processes, as well as developing data products that embrace emerging AI/machine intelligence technologies.

Defining success is also a vital aspect of managing AI projects and data science experiments. Naturally, success is often context-specific. To embark on this journey, we need to address the following fundamental questions: What problem are we attempting to solve? How can we determine when we've achieved our goal? What are the steps required for productionizing the solution? And, what are the product, user, and infrastructure concerns associated with deploying the solution?

Building data products and analytical solutions often entails leveraging classic and emerging data and machine learning-enabled capabilities that will be relevant within a six-month to two-year timeframe. Some aspects of success are general enough to merit discussion. The recommended hallmarks of AI/ML success include:

• Generation of new knowledge through insights obtained (documented via reports, presentations, etc.)
• Decision-making or policy formulation based on the outcomes or insights derived from an experiment or data analysis.
• Integration of a particular predictive model as a feature in an existing product, such as Gmail's Smart Reply.
• Development of a stand-alone data product or app with tangible business impact.
• Creation of new capabilities that either automate or enhance the efficiency of existing processes.

By acknowledging these hallmarks of success, organizations can better understand the value and impact of their AI and ML projects within the broader context of their business objectives.

Achieving success in data science and AI initiatives hinges on forming a high-performing machine learning/AI team. A vital element of such a team is the presence of a diverse array of multidisciplinary skills. It is also essential to invest in training existing in-house talent to build the right AI capabilities that will support the organization in the long term. In the long run, the Data Science and AI team should comprise members with specialized roles, such as Machine Learning Engineer, Data Engineer, Data Scientist, and AI Product Manager.

As organizations increasingly depend on artificial intelligence (AI) and data science to drive their operations, the need for a well-defined approach to managing and organizing AI talent becomes crucial. The responsibilities of such a team are multifaceted, covering the spectrum from strategic planning to technical execution. Initially, the team is tasked with designing an organizational AI/Data roadmap. This involves crafting a competitive strategy that anticipates the future, focusing on emerging data and machine learning-enabled capabilities within a timeframe of six months to two years. This strategic foresight is essential in staying ahead in the fast-evolving tech landscape.

On the operational side, the team’s responsibilities extend to analyzing and understanding business requirements, technical constraints for each project, and developing solutions that meet these needs. This includes collecting, analyzing, and interpreting large datasets to implement various predictive and prescriptive analytic solutions. Moreover, the team must establish robust data infrastructure, internal processes, and data pipelines that are crucial for the effective implementation of data-driven solutions. Part of their role also involves building prototypes as proof of concept based on the latest research, transforming them into production-quality products, and developing detailed project plans to track progress and ensure timely delivery within budget constraints.

Furthermore, the team plays a critical role in extracting insights from various types of organizational data—such as labeled data, streaming data, graph data, and process log data—to enhance decision-making at both strategic and operational levels. Maintaining databases, data structures, and the data warehouse are also significant components of data engineering and infrastructure oversight. Importantly, the team advocates for the democratization of data, promoting policies that ensure data access is not restricted to a specific analytics group or senior management but is broadly available to all within the organization, as far as legally permissible. This inclusive approach not only fosters a culture of informed decision-making but also enhances the overall data literacy of the organization.

It is worth mentioning that in a startup environment, individuals often assume multiple responsibilities, meaning one or two people may need to handle a variety of tasks. However, as an organization expands, team members naturally gravitate towards specialized roles. The initial team is tasked with executing a series of cross-functional projects to assist various divisions or business units with AI initiatives. Upon completing these initial projects, the team will be well-positioned to establish a recurring process for consistently delivering valuable AI projects throughout the organization's growth.