Building a Winning AI Strategy: A Comprehensive Approach

Facing critical challenges and frequent exposure to changing business environments is not new for enterprises. Over the years, enterprises are becoming immune to the changing environments. But one critical challenge continues to impact the decision-making of enterprises at scale.?

So, what is the challenge?

Over the last decade, organizations have adopted data and analytic practices to make data-driven decisions where a human interprets the dashboards and insights gathered to make business decisions.?

The data and analytics efforts aim to empower enterprises to make data-driven impactful decisions. But, at scale, more than this practice is required. The real challenge arises when enterprises try to make business decisions with data and analytics efforts alone.?

Data and analytics combined with decision intelligence will change the game for enterprises. Decision intelligence enables organizations to make faster, more impactful decisions as it efficiently translates the data to business decisions at scale.

Now that we are talking about decision intelligence, let's get into the Strategy part where enterprises decide on the driving factors for a project's success. For enterprises to make their AI vision a reality, they need a powerful strategy that drives them in the right direction, avoiding any disaster, as most AI investments are huge; nothing other than success can cost enterprises a fortune.?

Here is the approach that enables organizations to make better and faster decisions. It is the Decision-Centric Approach?.

Decision-Centric Approach? includes people, processes, data, and rules at the design level so organizations can make optimized, situation-aware, and customer-centric business decisions.

It can accommodate multiple technologies in the AI field, enabling enterprises to automate even the critical decisions that require incorporating multiple technologies. It maps the key business decisions and their related business objectives along with the sub-decisions and other decision requirements that influence the key business decisions. It comprises three stages:

Discover:

During the Discover phase, the primary focus is understanding the organization's business decisions comprehensively. The ultimate deliverables of this phase are the blueprints that provide a graphical representation of the organization's business decisions. These blueprints serve as visual models, demonstrating how these decisions strongly correlate with the organization's objectives and significantly influence outcomes. These blueprints can help you to improve the decision further by allowing you to manage decisions effectively.?

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Automate:

The crucial consideration for automation is whether the decisions are repeated and significant enough to benefit from automation. By automating such decisions, your organization should achieve:

1. Enhanced operational capacity.
2. Minimized errors, bias, and personal interpretation.

More often, business decisions can be complex to automate as a whole. You can start by automating a sub-decision of the business decision. Based on your organization's technology and team competency, you can automate any sub-decisions that influence key business decisions. You can also choose to incorporate AI decisioning platforms.

However, it is essential to note that automation does not guarantee improved outcomes for key business decisions. The effectiveness of automation in improving these decisions may vary, and you should validate it through experimentation.

Scale:

The critical parts to improve key business decisions associated with business objectives are monitoring, measuring, and experimenting with multiple variations of sub-decisions. At the Scale stage, you will be able to:

1. Integrate automated decisions into systems and processes.
2. Define, measure, and collect the KPI of the automated decisions.
3. Measure the impacts of the automated decisions on their key business decisions over time.

To improve the outcome of the decisions further, you can perform A/B testing and introduce an alternate automated decision to check which decision improves the business objectives better.

Takeaway:

AI decisioning Platforms clearly show how decisions impact the business objectives, you can improve those decisions to experience better business performance.

If you are looking for a technology partner who can help you quicken your decision-making while improving the quality of your decisions, you can check out FlexRule X, the next gen decision management and automation platform.

With the Decision-Centric Approach?, FlexRule? provides solutions for the pain points enterprises witness in organizational decision-making processes. It has enabled enterprises to make high-quality business decisions while minimizing the reliance of operation and SEM teams on IT and software development efforts.

For more information check out https://www.flexrule.com/links/yfh4