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.
To make any IT project successful, including the AI, Analytics and Machine Learning projects, it becomes critical for the team to deliver business values, and improve the metrics that executives are interested in and business is striving to achieve which always is around revenue, risks and costs. This is called the IT and Business alignment.
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. 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…
And they talk about the solution and say
Digital decisioning can stop this insanity
The other challenge for AI, Analytics and Machine Learning project is they are not actionable, or the one that are delivered they are not actioned yet. Team delivers an algorithm, shows numbers on the dashboard at the best, which it does not change the companies decision-making cycle and does not change the behaviour in company because they are not integrated into company’s processes.
So what’s the Answer?
The answer is to make sure Business and IT Alignment is strong in AI, Analytics and Machine Learning projects is to ensure they are:
- Using Decision-Centric Approach to know exactly what decisions are going to change and understand the maturity level and impacts of the decisions
- Utilise the full decision automation to make the actions as part decisioning project, and fully integrated and incorporated into processes.
- Using Decision-Centric Approach and Advanced Decision Management Suite to measure the impact and close the feedback loop from the decisions results
- Using the Advanced Decision Management Suite to incorporate the knowledge of domain experts as part of the decisioning cycle along with the AI, Analytics and Machine Learning techniques
Last updated July 31st, 2020 at 03:38 pm, Published December 10th, 2019 at 03:38 pm
CEO and the founder of FlexRule – He is an expert in architecture, design, and implementation of operational decisions, business rules, and process automation. Created Decision-Centric Approach, a methodology that brings People, Data, Rules, and Processes together to automate operational business decisions.
This approach is recognized by Gartner as the missing link to provide business value to organizations.