Now, more than ever, agility is the currency of success. And while agility may be about responding intelligently to the changing nature of the marketplace, those responses must be rooted in a plan. Today, many organizations leverage newer technologies in the cloud for planning, having moved away from manual spreadsheets. And while the cloud offers greater collaboration and the ability to easily combine both historical and real-time data, it’s just the beginning. Digital transformation is changing and will continue to change the definition of best practice planning in organizations. As such, the next step for business planning revolves around two key areas—advancements in AI and machine learning, and increased automation.
The power of ‘what if’
What-if scenarios are already incredibly powerful for strategic decision-makers. Organizations can model different versions of the future based on historical information and predictive analytics before choosing the best path forward. Consolidating executional data within organizations is the first step in capitalizing on future AI opportunities. However, there is a lot more to come. In fact, compared with what AI is going to make possible, scenario planning is still in its infancy.
Today’s scenario planning is a good proof of concept, but as long as humans are driving the creative process—it relies on people to ask the right questions of the right data—what-if planning is going to be constrained by available resources. The most advanced decision-making today is typically supported by a few best-estimate scenarios—maybe four or five at most. However, in truth, there are many more possible futures to potentially prepare for, and what looks like best practice now is going to seem vastly limited in scope before too long.
As the volume and variety of available data grows, and access to that data gets easier, AI and machine learning algorithms will make it possible to drill down, consolidate, and leverage incredibly granular information at the highest levels.
AI and machine learning use cases
To consider how these AI and machine learning algorithms will work, let’s look at a use case of a CEO aiming to achieve a 40 percent growth target over a two-year period and wants to model what that looks like to present at the annual executive offsite. AI and machine learning-enabled planning could help to quickly and automatically find the optimal growth path, while accommodating any conditions and assumptions on the fly.
Essentially, the planning system could measure historical performance and recommend a market segment mix strategy, along with the associated budget increases in the specific marketing and sales activities needed to support it. If they then decide they need to cap growth in sales to smaller businesses in order to also expand into enterprises and international markets—while also maintaining expenses at a certain increase—an alternative, optimized model could be quickly created without any manual lifting.
A future with machine learning
The future of business planning is not just about thinking bigger—it is about making better decisions and operationalizing them faster. That’s where machine learning comes in. Increased automation, driven by algorithms, is going to blur the boundaries between planning, execution, and analysis until planning cycle times have all but evaporated.
Planners will be able to ask deep, complex strategy questions and see the results modeled in real time. As the data becomes more trusted, they will be able to make significant, informed, “just-in-time” decisions, confident in the patterns surfaced in the data. And as the line between planning and transactions systems begins to blur and disappear, plans will automatically cascade down to operational departments—even down to individual workflows—in real time.
‘Strategy’ will become the province of human-driven innovation while planning becomes an organic, ongoing exercise of continuous improvement inextricably linked to the transactional systems that execute plans.
Leading the change
Today finance acts as the central junction within business planning and is, therefore, a natural steward for change, helping normalize new habits and behaviors for the rest of the organization. As such, there is a strong case to be made for finance teams to double down on their new position as stewards of change by acting as transformation leaders—both for existing processes, and for future, unknown developments.
Finance’s role will change significantly in order to leverage technology developments in the data-driven, AI future. Driving collaboration with business partners, breaking down data silos, and embracing new technologies and processes to keep pace with today’s rapidly changing business environment will be key. The result will be an augmented, intelligent planning process that delivers true business agility.