Embarking on an AI transformation Journey

But the goal of data science is not to execute. Rather, the goal is to learn and develop profound new business capabilities. Algorithmic products and services like recommendations systems, client engagement bandits, style preference classification, size matching, fashion design systems, logistics optimizers, seasonal trend detection, and more can't be designed up-front. They need to be learned. There are no blueprints to follow; these are novel capabilities with inherent uncertainty.

Cultivating AI proficiency presents a distinct array of hurdles when integrating Analytics and AI throughout an enterprise. To maintain a competitive edge in the rapidly advancing AI arena, would requires businesses to take nimble, informed decisions about effective data use, both at the strategic and operational level. Consequently, dedicating time to devise an AI and data science blueprint is a worthwhile endeavor that can subsequently steer the organization towards generating value enabling them to build defensible moats.

Organizations need to create internal processes and capabilities that enable them to obtain, process, and utilize data promptly, thereby generating efficiencies and developing new products essential for navigating a competitive landscape. For firms interested in leveraging AI and data science to create an advantage, specific to their domain, means developing a mindset where:

• Dedicated efforts are made for instrumenting and collecting as much data as possible.
• Proactively and promptly gauging pertinent metrics; that is, data is gathered to measure all aspects, ultimately promoting transparency in the existing deployed processes.
• Leveraging data in a methodical manner to identify and analyze the root causes of inefficiencies.
• Systematic use of historical data to create machine learning-based models, which can forecast outcomes of crucial processes and potential faults in advance, mitigating risks.
• Advancing beyond conventional analytics by developing innovative capabilities, such as creating new data products to enhance process efficiency, facilitate automation where viable, and provide intelligent decision support for process users.
• Encouraging a culture of heightened curiosity about collected data sets, by democratizing access to data to team members across the organization.

An effective data and AI strategy typically translates to finding the best use cases by considering how ML can improve operations, increase efficiency/productivity, and drive innovation. The roadmap can help organizations develop possible pilot projects, evaluate new ideas, and establish a focus by prioritizing them, in order to develop the right capabilities. Executing pilot projects will also help gain momentum towards developing innovative solutions crucial for stay maintaining competitiveness in their specific domain.

The pre-requisite for organisations to succeed at AI and data analytics initiatives is to have:

Sufficient understanding of AI and data science: There should be a general understanding of AI that cuts through the hype and buzzwords within the organization and the enabling capabilities associated with it, that is relevant to business needs. e.g In the real world, 75% of the challenge in Machine Learning is building the right dataset. The key is to establish a mindset and an internal process that allow them to systematically identify, select and execute valuable AI projects.

Availability of key Resources: AI projects often require large-scale compute resources, lots of data, and efficient algorithms. Without availability of these resources, AI projects often fail to meet expectations. Its important for us to develop an in-house team that is capable of executing on multiple AI projects, allowing us to develop relevant valuable capabilities. Its also important to nurture talent in the long run, so that we can systematically execute on multiple AI projects that deliver direct value to the business.

Strategic Alignment: Alignment with strategic goals (of the organization) is the key in order to succeed in an AI-powered future. The AI efforts must be broadly aligned with near-term business goals, therefore it's important to define a prioritization mechanism for the efforts.

Understanding the Impact:

Incorporating AI across organizational processes translates to impact on three different levels:

1) AI-enabled infrastructure: where need new approaches where AI can be embedded into objects and services to provide ambient intelligence and new kinds of experiences.

2) AI-Enabled processes: We deelop new tools and workflows for designing our processes where AI can provide automation and decision-support. This is to better understand the process behaviour retrospectively. Outputs will be actionable knowledge, diagnostic insights about performance bottlenecks, understanding their root causes and highlighting other sources of inefficiencies. Develop predictive monitoring capabilities where for example we predict the future behavior of those instances and provide better support for operational decision-making across the organization. Lastly, Case variant/outcome prediction aims at predicting process instances that will end up in an undesirable state (measured as likelihood and severity of fault occurrence or violation of compliance rules).

3) AI-as-collaborators: We view AI as fundamentally improving the decision-making capabilites of decision makers and managers across the organization. It aims at providing intelligent assistance to process users by offering concrete recommendations in various process-related decisions like resource allocation decisions or action recommendations. Such type of assistance can improve the process performance for running process instances and help avoid the risk of failure (or sub-optimal performance where process goals are not met).

Building AI solutions that start showing traction can take between 6-12 months. Ideally, we should identify business metrics to express value of these projects. The next step will be understanding Project Requirements in detail with various phases. e.g, Analysing, identifying and understanding business requirements, needs, and technical constraints for each project. After completing the initial analysis, we will have to determine what tools, skills, and budget are needed. In the next phases, the outcome will be a prototype that if successful, can be turned into a production-ready solution. Over time this turns into a repeated process to continuously deliver a sequence of valuable AI projects. Overall, the road map will also help organizations in developing project plans to track progress and ensure that the efforts are aligned with a coherent AI strategy.