Business and IT Alignment is always a hot topic between stakeholders of any digital program. As projects get complex, or organizations start programs of work i.e. multiple projects running to achieve a set of outcomes, it becomes more important to ensure they are aligned with business objectives.

Wikipedia defines the Business-IT Alignment as

The business-it alignment is a process which organizations uses information technology (IT) to archive business objectives.

With the everyone’s expectations from what AI can bring to global economy, it becomes more critical to ensure the AI projects are aligned with business expectations. Wall Street Journal mentions

Artificial intelligence has the potential to incrementally add 16 percent or around $13 trillion by 2030 to current global economic output.

This shows why AI, Analytics and Machine Learning are in the hype of technology. Therefore, technology leaders of organization showing strong interests in testing and trying our these technologies. And on the other hand, based on reports of companies such as Gartner; 86% of companies have initiated AI projects and 4% have the AI project deployed. Because, it becomes very hard to justify the values of these types of project to company’s board and their executives.

The Challenge

To make all types of AI (including Generative AI), Analytics and Machine Learning (ML) projects a success, it is critically important to ensure everyone is clear about Business Decisions that are impacted. As business decisions' impacts are far-reaching, the team must use a blueprint of business decisions to make sure they improve the right metrics of the right decisions with the right technology at the right stage of the project. Otherwise, they will face Type 3 error i.e. solving the wrong problem.

The challenge for the most of AI, Analytics and Machine Learning projects is the fact they are mostly wasting time and money without delivering much of business values (type 3 problem). Based on the Forrester Report named “The Dawn of Digital Decisioning”:

Enterprises waste time and money on unactionable analytics and rigid applications

Forrester point of view on decision is that

…nexus of business rules, data, analytics, and machine learning models…

The other challenge for AI, Analytics and Machine Learning projects is they are not actionable, or the one that are delivered they are not actioned yet, or not fully integrated into systems and processes of organization.
A team delivers an algorithm, shows numbers on a dashboard, at its best it is a very small (or isolated) part of organization, which it does not change the companies decision-making processes and behaviours because they are not integrated into company’s processes, systems and not managed by operations team.

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So what’s the Answer?

The answer is to make strong Business and IT Alignment in AI, Analytics and Machine Learning projects. You might ask how? Below are couple of suggestions:

Last updated October 26th, 2023 at 02:16 pm, Published December 10th, 2019 at 02:16 pm