How to craft a Successful AI strategy?

Today companies invest hugely to incorporate AI into their organization. Failing to translate the efforts into successful decisions that align with the organization’s objectives can lead to a considerable loss. It is significant for AI teams to prioritize delivering business values that correlate with the organization’s objectives. The more the investment, the more the risk involved. Thus, crafting a powerful AI strategy is inevitable for your AI success.

What does it take for your AI projects to succeed? The first thing would be avoiding data and analytics gaps.

“Data and analytics leaders are investing heavily in analytics, machine learning, data science, AI, and other related technologies. The belief is that such technologies can drive better decision making and, thus, business outcomes. However, there is a real gap between this belief and the idea of a decision.

Analysis by Andrew White, Gartner”

This gap has forced organizations to look for new approaches beyond data and technology investments. A few approaches, like Gartner’s decision intelligence framework and AI canvas framework, aim to cover this gap.

AI Canvas framework tries to integrate the business outcomes and data requirements at the definition level of the project. It provides a clear picture of the project requirements and implementation methods at the individual project level. The single-page project scope aims to communicate and define a shared understanding of the project opportunities and requirements. However, it needs to address the gap as it takes a narrow and data-centric approach and fails to provide a clear picture of the project’s impact on the organization’s decisions.

On the contrary, the Decision Intelligence framework from Gartner has a broader view, including various AI techniques and technologies. But it needs to provide a practical step-by-step approach to building successful AI projects. Thus, both frameworks need to bridge the gap.

How can you address this gap?

Decision-Centric Approach® overcomes the limitations of the frameworks mentioned above. It focuses on the bigger picture and provides a step-by-step approach by combining people, processes, data, and rules. It helps organizations to make optimized, situation-aware, and customer-centric business decisions.

Decision-Centric Approach® creates a map for critical business decisions, including an organization’s sub-decisions and related business objectives.

How does Decision-Centric Approach® work?

Business decisions are a vital part of an organization. Organizations must make and implement business decisions more effectively and efficiently to stay ahead of the crowd. In the Decision-Centric Approach®, the business decisions are at the focus, helping you craft an AI strategy where values, objectives, and decisions align.

Decision-Centric Approach® has three important stages. They are
(a) Discover
(b) Automate
(c) Scale

Discover:

At this stage, we identify and comprehend business decisions and their interdependence. We also interpret how the organization makes decisions to create an organizational decision audit.

The outcome of this stage is a blueprint- graphical visual models that represent the impact of the business decisions on the business objectives. The blueprints are significant as they are the foundation of all organizational improvements. They depict how you make decisions, establish trust, and enhance stakeholder collaboration.

It is significant to update the blueprints constantly for better performance. These blueprints give you an idea of which part of the decision-making process you can improve. Thus, it makes the entire process transparent and manageable.

Automate:

We can interpret the most valuable decisions from a business objective’s blueprint. Every critical business decision may correlate with one or more business objectives. To reduce the complexity of critical business decisions, you can break them down into sub-decisions.

Now that you have crafted sub-decisions, you can choose which sub-decision you need to automate based on its impact. This clear understanding of what to automate will help you avoid any errors.

Depending on the type and requirement, you can use the AI Canvas framework or the decision intelligence framework to automate any sub-decision. One crucial factor you need to consider is whether it is a repeated decision. So, automating it impacts the organization’s performance by

• Increasing the organization’s operational capacity
• Reducing error rates, bias, and personal interpretation

Note: It requires further experimentation, as automation alone will not drastically improve your business decisions.

Scale:

Now you can

• Incorporate automated decisions into your systems and processes.
• Define, measure, and collect the KPIs for automated decisions.
• Measure the collective impacts of the decisions on their key business decisions over time.

Business decisions are multifaceted. You must continuously monitor, measure and experiment with different variations of sub-decisions to achieve the business goals with automated decisions.

Taking advantage of the blueprint, you can experiment further to understand what works best for you. Start with A/B testing, introduce an alternative to an automated decision, and analyze the impact on the business objective.

Takeaway

As your AI success begins with your AI strategy, Decision-Centric Approach® is your go-to framework. It provides a practical guide to identifying, designing, implementing, and scaling AI projects. It bridges the data and analytics gap by explicitly defining business decisions and their influence on the organization. Furthermore, it also depicts the impact of business decisions on business objectives and ecosystems.

Learn more on Decision-Centric Approach® and how it can help your AI team and your organization succeed.