How do you develop and implement a successful AI strategy for your business?

An example of this: focus on program challenges (e.g., time, cost, quality, etc.) to drive positive outcomes such as customer confidence and predictable program controls.

It is important to assess the maturity of your organization. This can be done by a current state analysis. Here you assess the impact drivers (current vision and use cases) and the enablers. The enablers can be differentiated into two categories: Human enablers and technological enablers. They can be further broken down but one of the most crucial things are leadership understanding and commitment and the culture. Both heavily need to be augmented with appropriate training programs that teach the ability to identify problems that can be solved with data and AI. We call this data thinking

Large organizations have silos of maturity level. One department's data and system may be ready to experiment with AI whereas others may not. It's important to assess readiness at a department level or any other logical level for the organization's IT landscape.

The first step is to clearly identify the business objective and the specific task that the AI system will perform. You need to establish the goal to measure your readiness against.

An example of this; What is your Unique Methodology to Respond to Emerging Changes? For us; It’s The combination of optimizing work components while testing their enhancements. This allows our teams to rapidly fit reinforcing technology components to the improving operations. The components can include our inwardly facing algorithms or outwardly facing signals.

A good method for use case identification is data thinking. Data Thinking combined the creative methodology of design thinking with data science and AI expertise. The data thinking approach is a user-centric approach that ask relevant data questions at each step of the use case development process. The method teaches people to identify, describe, and evaluate use cases based on the organization‘s data & AI strategy.

It's important to bring Organizational internal dynamics as one of the factors while identifying the use case. An employee would be in a better stage to qualify for the use case instead of any consultant coming up with a recommendation based on feasibility or ROI.

This is the first step. AI should not be implemented just because others are using it, but rather to solve a business problem or to exploit a business opportunity.

Diversification is the key to faster experimentation. It's always better to not keep all the eggs in the same basket i.e. to choose different partners for different use cases. This would also introduce a competitive spirit among various teams to perform best in a given time & budget.

First decide whether to develop solutions in-house or buy or outsource AI solutions. Once strategy is determined, vendors and platforms can be selected.

AI-based systems are tricky in nature to test. Especially model-driven cases which are trained on a set of data. The best bet is to use training data and test data as a subset of production data.

There's a fun saying: Think Big. Start Small. Scale Fast. Before implementation, consider the scalability and long-term maintenance of your AI systems. As AI technology evolves, it is essential to plan for future enhancements, updates, and ongoing monitoring of the system's performance.

Establish the metrics to be used to evaluate the system's performance before embarking on the implementation process. This ties into your initial goals. You need something to measure against.

Data & AI transformations are seismic shifts that reshape businesses overnight. To thrive, employees need deliberate training in AI methodologies and tools. This learning isn't optional, but crucial for effectively leveraging AI in workflows. With Generation AI (GenAI) revolutionizing work dynamics, this training becomes more vital. It prepares teams for the AI era, boosting productivity and spurring innovation. Hence, training is not an indulgence, but a strategic necessity in the AI-driven landscape.

With time, each organization gathered multiple models and their versions. It's important to plan out managing and maintaining models in a continuous manner. The capability to re-train models is essential for any long-lived production-ized AI system