Oracle Details Importance of AI in the ‘New Normal’

AI-based scenario modelling is taking on a whole new level of importance amid the COVID-19 pandemic, as business leaders across finance, HR, supply chain, and sales make difficult decisions for the sake of continuity, impacting employees, customers, suppliers, and partners.

 

The pandemic has upended “business as usual” so quickly that we lack historical data to guide our decisions. Scenario modelling can fill in those gaps, helping business leaders anticipate the future.

Anna Tan, Head of Apps, Oracle Philippines

As a CXO, you’ll want to look at your key stakeholder groups, identify their risks, and create models for the worst-case and most likely outcomes of the business decisions you could make. You can then weigh up the costs and make a final decision with confidence.

 

Start by deciding the scope and issues you need to address immediately, while defining your key drivers – it may have been growth a week ago, but now it’s continuity, for example. Next, collect and analyze the quantitative and qualitative data you’ll need to make your key assumptions.

 

Once you have the foundation in place, you can start developing the different scenarios. Consider what scenarios are most important or likely for your LoB, and start there. Define what the impacts of each will be on sales, cash flow and CAPEX, then decide what metrics you’ll use to measure each. Finally, monitor the plan constantly and consider if you’ll need more frequent reporting to respond to changing metrics.

 

However, current disruption can make accurate scenario modelling a tall order. CXOs have a huge number of stakeholders to consider, and the data they need is often scattered across different data environments.

 

To make the task easier, business leaders need to involve fewer people in the process and limit the number of scenarios they consider. In fact, model no more than four, but be sure to spend equal time on each, even if you think certain models are less likely.

 

Oracle is also offering free access to strategic modelling capabilities through its Oracle Planning Cloud for the next year. Customers will benefit from improved agility, more accurate forecasting and decision-making, with the power to run detailed what-if scenarios for many potential scenarios. Evaluating all options ensures you’re ready for the unexpected – a constant in this ‘new normal’.

 

 

Making the most of AI

 

Of course, scenario modelling is only one part of the solution. Unprecedented amounts of data can be a blessing and a curse without the right support. CXOs can be overwhelmed by masses of new data alongside the many data management responsibilities that come with them. Data collection, cleansing and security can drag business leaders away from prediction and strategizing. Without assistance, they can’t work at the speed required.

 

To help carry the load, CXOs should consider what they can streamline and automate with AI. AI solutions can analyze and interpret vast quantities of data in little time, making it invaluable for scenario planning. It can also automate the many repetitive but necessary tasks associated with data management.

 

However, it’s worth tempering expectations and being realistic with where the technology is deployed. Companies often struggle to deploy the technology at scale and have unrealistic expectations for it. The last thing you want now is to embark on a costly and ambitious moon shot that fails to meet your objectives.

 

To make the most of your AI investment, you should both buy and build applications. You don’t need to build everything from scratch, and doing so could create compatibility issues later on. What you need is a strategic approach that delivers interrelated solutions that maximize AI’s benefits rather than rolling out a series of disparate solutions.

 

Special attention should also be paid to data quality. It needs to be complete, cleansed and up-to-date for an AI solution to deliver accurate insights. Fortunately, AI-driven data engines can cleanse and enrich data records before they are served up for analysis.

 

Another important consideration is tuning. AI ‘maintenance’ is usually performed expensively and manually by data scientists, but it’s hardly feasible when your organization has hundreds of AI models to maintain. Applying machine learning to this process automates this expensive task, keeping costs under control. 

 

There’s no silver bullet for business disruption. However, the scenario modelling and Artificial intelligence can help organizations weather the storm. When detailed, comprehensive models are combined with AI efficiency and human judgement, businesses will make better, more impactful decisions that help shine a way through the crisis.