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Living and Breathing AI Transformation Strategy

By: Balaji Gopalakrishnan, Ph.D., FRM,SVP, Head of ML & Data Science, Wells Fargo

Struggle forAI-Based Transformation

Whilebusinesses focus oninnovation to drive growth and sustainability, many of themstruggleto implementeffective AI transformation programs. Identifying AI based use cases that add value to businesses is important but having a long-term AI strategy for developing foundational capabilities to support current and future use cases at scale is critical for accelerating AI transformation. If this planning is not well thought through, business leaders mayend up losing their confidence in the transformative power of AI.

"The value proposition of AI Transformation is more than the sum of use cases"

Against thisbackdrop, many businesses, at first, let their executivesdecide uponthe AI use cases that can help them improve some of their organizational KPIs. Subsequently,they draw a roadmap for their data science teams to develop the AI models for the use cases before the technology team deploys the modelfor its business conception. However, theprocessof building AI models for use caseshas remained in siloes. Often, once a business sees its efforts on a use case failing with the model outcome, often times due to data limitations, theystop working on it and move on to the next one. This results in abandonment of the data transformation work done on such use casesalong with the insights developed by the data scientists working on the model. Clearly, the value proposition of AI Transformation is more than the sum of a set of use cases alone.

Develop a robust AI transformation strategy

The foremost requirement for any businessin the course of their AI transformation journey is todevelop a clear vision for the transformed state of their business/ organizationin the next fewyears. This transformation may not be entirely driven by AI and it could be a combination of deterministic operational improvements, new training programs for staff, along with AI driven transformations for optimizing uncertain decision making processes. There is a need to carefully isolate and identify, from the overall business transformation end state, the AI driven transformation potential.This would be best achieved through a cross functional internal AI transformation team consisting of business partners, data SME’s, domain experts, data scientists, IT, and engagement managers.

By zeroing onbusiness KPI-related functions that can be driven by AI, we can begin to develop an AI transformation journey map to achieve the AI transformation goals through a combination of near to medium-term AI driven business uses cases along with AI capabilities development to scale AI transformation to support rapid exploration of future use cases from internal and external data sources.

Accelerate the AI transformation Journey

There are four key areas in addition to others that can accelerate the AI transformation journey for a business. These are people & process, scalable data ecosystem, rapid experimentation, and robust infrastructure.

Having a dedicated team focused on the AI transformation goals for a business domain area can help the team build a strong knowledge base over time and the dividend from this can result in deeper insights, better use cases, and more powerful models. Building a strong knowledge base in the domain area would require good collaboration within the team and having an agile process for staying nimble in executing projects would greatly help in sharing expertise and insights along the way.

An important capability for accelerating AI transformation is a data foundry, which is a robust data ecosystem for specific business domain areas, such as card collections or fraud,to support multiple concurrent AI model use cases. It needs to be set up with the right level of data aggregation to seamlessly generate model features for multiple AI models. A data foundry is developed over time in an incremental fashion by leveraging data transformation work from prior use cases and merging new internal and external data sets to expand the foundry.

Identifying AI model use cases that has the potential to save millions of dollars for the business is very important and should be a key part of the AI transformation strategy. However, the path to refining the data into value for these use cases lies in unfettered data and model explorations on the data foundry by a dedicated data science team. As the data foundry matures with cross domain and external data sources, it lends itself naturally to rapid model explorations to drive deeper insights and accelerate the AI transformation objectives of the business.The model explorations could result in reusable features that can be integrated into a feature store as part of the data foundry to be used by multiple AI models.

Last but not the least, AI transformation is highly dependent on a robust infrastructure. As new data sources are added to the data foundry, the need to quickly collect and integrate data is critical to performing rapid model explorations. Having a broad based AI infrastructure ecosystem with the right balance of high-performance compute and storage, as opposed to a hodge-podge of multiple solutions, can greatly help with the seamless execution of downstream data pipeline development, model training, and model deployment.

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