The end of the year is usually a time when most organizations are either actively engaged in business planning for the next year or have just completed the process. Regardless of where they might be in the business planning process, just about everyone involved would agree that it was a painful. Most organizations still rely on antiquated spreadsheets to share financial data. Too often, there’s not even much consensus concerning whether the data in those spreadsheet applications are accurate.
Regardless of external factors, most businesses every year will assume they will continue to grow a few percentage points. Many times, finance teams ignore the lines of business altogether. The business plan is based on historical data collected by finance teams that frequently don’t trust projections made by overly optimistic line of business executives. The simple fact of the matter is that what passes for business planning in most organizations is deeply flawed. Most business plans are not actually worth all the time and effort that goes into creating them.
A survey of 1,000 business planning professional conducted by Dimensional Research on behalf of Anaplan, a provider of planning software delivered as a software-as-a-service (SaaS) application, highlights the extent of the problem. Only 15 percent of companies surveyed report executing on all their plans. A total of 39 percent of the executives surveyed could claim their organization put three-quarters or more of their plans into action.
Why AI Addresses Business Planning Failures
The fundamental challenge organizations face is two-fold, says Simon Tucker, chief planning officer for Anaplan. The first is that they often don’t have trust in the data they do have to make a reliable decision. The second is that the data they do have tends to reside in disparate systems, says Tucker. In fact, Tucker says, all too often, each business unit makes plans without consulting other business units. There’s been some progress over the last two years as organizations embrace the concept of connected planning, but much work remains to be done, adds Tucker.
“It’s still a relatively new concept,” says Tucker.
The good news is that advances in machine learning algorithms and forms of artificial intelligence (AI) will make it simpler to gather and analyze relevant business data regardless of what silo it resides in, says Tucker.
In fact, the Anaplan survey finds 94 percent of survey respondents reporting that they believe machine learning has a role in the future of planning technology, while 55 percent said they expect capabilities such as creating “what-if” scenarios to enable organizations to run advanced analytics applications with confidence.
A survey of 1,062 business and IT professionals published this week by Infosys, a provider of IT services, similarly finds AI as being perceived to deliver increased outcomes when combined with analytics by 37 percent of respondents. The biggest challenges in realizing those benefits stem from a lack of expertise in integrating multiple datasets (44 percent) and a failure to understand how to deploy the right analysis techniques (43 percent), find the survey.
Those issues have now become boardroom-level conversations as organizations look to create data analytics strategies that span the entire enterprise, says Sunil Senan, a vice president at Infosys.
“Data analytics is no longer a tactical tool,” says Senan.
Specifically, the Infosys survey identifies customer experience enhancements (31 percent), risk mitigation (28 percent), developing new business models (23 percent) and profit maximization (18 percent) as the primary drivers of investments in advanced analytics.
The Human Element
Developing analytics expertise is even driving some companies to unexpected lengths. For example, Northwestern Mutual is developing a data sciences institute on the campus of its Milwaukee, Wisconsin Headquarter campus. During a recent National Competitiveness Forum in Washington, D.C., Northwestern Mutual chairman and CEO John Schlifske described how the insurance provider is working with a variety of academic institutions such as Marquette University to turn Milwaukee into a technology center.
“We want to grow talent that wants to stay in Milwaukee,” says Schlifske.
Schlifske even went so far as to describe not being able to attract people with data science skills as an existential threat to the business. Earlier this decade, Northwestern Mutual acquired LearnVest, a financial technology startup based in New York for $250 million. Northwestern Mutual recently decided to shutter LearnVest. But many of the data scientists that Northwestern Mutual gained via that acquisition still work for the insurer. Part of the attraction of working for Northwestern Mutual versus moving to Northern California to work for a technology company is that the cost of living is much lower. But unless there are critical masses of data scientists working for the insurer, data scientists won’t be interested, adds Schlifske. Most data scientists want to work and live in an area where a technology ecosystem is being fostered, notes Schlifske. The first students from the data science institute funded by Northwestern Mutual will be graduating in the Fall of 2019.
Because of that issue, Marquette views Northwestern Mutual as a natural partner. During the same conference, Dr. Michael R. Lovell, president of Marquette University, noted that growing homegrown data scientists is ultimately going to be less expensive for corporations than continuing to acquire startup companies. If nothing else, students who remain in the Milwaukee area also enjoy other intangible benefits when they decide to go to work for Northwestern Mutual, says Lovell.
“The commute is going to be a lot shorter in Milwaukee,” says Lovell.
In the meantime, vendors ranging from Amazon Web Services (AWS) to SAP are pouring resources into various types of business planning software. At the recent re:Invent 2018 conference, AWS launched a preview of Amazon Forecast, a managed cloud service based on the AI models that Amazon uses to manage its own operations. AWS CEO Andy Jassy told conference attendees that Amazon Forecast provides 50 percent more accurate forecasts at one-tenth the cost of traditional supply chain software without requiring customers to hire dedicated data scientists.
“There’s no machine learning experience required,” says Jassy.
SAP, meanwhile, sees AI accelerating a transformation of business planning that is starting to gain momentum, says Dave Williams, vice president of global product marketing for enterprise software at SAP. As business planning becomes more of a continuous process rather than an annual event, businesses are becoming more agile, says Williams. Organizations can continuously adjust their strategies as they leverage AI to more precisely track any number of key performance indicators (KPIs).
In time, not only will financial teams be able to work more closely with business executives to identify and track KPIs, there will also be digital assistants making recommendations based on both the latest events as well as potential outcomes that might be achieved by dynamically adjusting organizational strategy.
“Business executives will be able to make a more intelligent decision rather than just relying on gut feel,” says Williams.
Of course, it may take a little while longer before AI transforms business planning for the better. But at this point, it’s clear that the way businesses are managed will soon be transformed utterly.
