The rules of planning are evolving daily. While we still steer businesses based on numbers, plans, budgets, and assumptions, thanks to artificial intelligence (AI) developments, computing power, and data we are on the cusp of a major transformation in the way we operate our businesses. We are progressing from static, cycle driven, labor-intensive, cumbersome planning exercises to dynamic, collaborative—and especially—intelligent plans.
Many companies are too focused on seeing data as an end “goal” when they should be focusing on how to integrate data into all aspects of the business. However, I believe an end is coming for manual data mining processes thanks to the advances being made in artificial intelligence (AI). But how can AI actually help in your planning exercise?
With AI, a machine learns, improves, and continuously optimizes without bias, offering options and potential actions that can improve performance across an organization, allowing you to redirect employees onto tasks that truly align with and work toward the overall vision.
Data is Not the Holy Grail
However, the increase in data over the years has started to do more harm than good. For instance, all data does not equal correct information. The more information available, the more “noise” there is and the harder it becomes to sort through what is real, which makes it easier for people to cherry-pick patterns that fit their pre-existing positions.
Just ask Steven Sloman and Philip Fernbach who wrote the book The Knowledge Illusion. Within the book they go on to say, “individuals know very little, the key to our intelligence lies in the people and things around us.”
We’re consistently drawing on information and expertise stored outside of our heads, while AI is only influenced by factual data and can provide a pure, impartial look at patterns and forecasts. Our brains are what make us smarter than any computer, but at the same time, our minds can also be influenced by unreliable sources.
Human Shortcomings
Humans have a difficult time looking at data objectively; we want the data to be in line with the stories we are trying to tell, while machines are only looking for the patterns that make the most sense. For example, two people with the same backgrounds and experiences will not create the same forecasts due to the influence of external factors.
Machines are bringing objectivity to planning by augmenting the available internal data with external data. AI is able to find patterns and make predictions that help organizations become more accurate, have better insights, and move faster than their competition.
More About Connected Planning:
- Transforming IT Project Planning for CIOs
- Connected Planning in the Age of Artificial Intelligence
- The State of Connected Planning Trends Review
The Collaborative, Connected, and Intelligent Self-Driving Enterprise
If you want to be prepared for the new age of Connected and augmented Planning, it is necessary to embrace artificial intelligence. As we have learned:
- Planning cycles will disappear as both actuals and forecasts are generated in real-time.
- Machine-based algorithms are looking at the past, internal, and external data, and are producing a more accurate baseline forecast.
- Confidence intervals are changing the tone of discussions and are reducing uncertainty.
- New insights are created as the self-driving enterprise beats traditional data reading capabilities to discover patterns and drastically improve business and decision-making.
- The self-serving capabilities allow business partners to enter the solution and make simulations, run some scenarios and reduce the time between planning and collaborative decision making to near zero.
Machines will never replace Connected Planning, but your planning processes can grow in efficiency, effectiveness, and accuracy by augmenting them with AI.
The world is rapidly preparing for this; are you?
Nick Vandesype
Besides being a Master Anaplaner and a principal solution architect in the Deloitte EMEA CoE, Nick is recognized as a subject matter expert in algorithmic design and data-driven predictions as well as Python and Tensorflow,
