Today’s businesses must process an astonishing amount of data to make decisions, even in operational areas such as supply chain, procurement and contract pricing. Assessing these extraordinary volumes of data is nearly impossible for even experienced managers.
Through distributed computing and powerful artificial intelligence (AI) and machine learning (ML), decision process automation (DPA) can address this complexity by automating how organizations analyze massive volumes of information. Simultaneously, human experience and insight complement algorithmic decision-making by assessing contexts too nuanced for AI and ML to address as of yet.
DPA offers a new way to optimize decision-making by automating how organizations analyze what has happened and what is happening to more accurately predict what could happen.
Technology has created enormous challenges by growing the amount of data businesses need in order to compete — but technology also offers the solution. Organizations can continuously model, simulate and select the best choices for decision-makers — automatically — by using distributed computing, algorithms that can process massive amounts of data at lightning speed, and so-called evolutionary AI.
This is the opportunity that DPA presents, the logical next step from robotic process automation (RPA) and intelligent process automation (IPA) in the evolving continuum of business process automation (BPA).
RPA improves operational efficiency while reducing human error. IPA helps organizations automate complex tasks, leveraging cloud technologies to bind together operational systems. And other functional software addresses challenges in supply chains and procurement. All are effective, because they allow knowledge workers to focus on more value-added tasks.
But none of these operational technologies can aggregate multivariate data; simulate and model the effects of different decisions past or present; or rank-order optimal decisions based on desired outcomes, allowing managers to then choose one.
DPA can. It leverages massive computing power and increasingly sophisticated algorithms to assist in business process decision-making. Just as one might create a “digital twin” to model performance parameters on an aircraft engine, DPA creates a digital model of an organization. It examines historical data, trends and decisions; evaluates current conditions by analyzing incoming data streams; and models potential scenarios and outcomes. Using DPA, organizations can better examine evolving opportunities and formulate previously unseen solutions. Call it a look into potential futures.
Moreover, DPA can capture and retain the elements of inherently loose processes, such as individual decisions or tacit processes, by extracting and tracking key process details. And by analyzing a simulated instance of an organization’s past choices and current data inputs, DPA optimizes decision-making in situations where even the best minds may balk or fall short. It winnows out optimal choices from overwhelming amounts of data, incorporating algorithms that progressively refine outcomes while eliminating human biases.
DPA delivers choices based on how the organization has worked in the past even as it incorporates data streams from the present. This brings humans back into the loop precisely at the point where the human capacity for insight and foresight, and the ability to understand institutional context, offer the most value.
In short, we believe DPA is the future, because it shows organizations what the future can look like — machines present optimal choices, while humans remain in the decision loop.
How DPA works
DPA does not eliminate RPA or IPA, but uses data generated from both as input. Fundamentally, it is a higher-order decision engine running in a virtual model of a business’s operations. It relies on evolutionary AI, running iterative sequences of scenarios, with thousands or even millions of variables, on a cloud-based network of millions of distributed processors.
This sequential algorithmic analysis allows the decision engine to rapidly refine to optimal outcomes, enabling automated decision-making at lightning speed. DPA makes choices as humans might, but with the ability to manage an extraordinary number of complex variables, including data inputs from evolving conditions in real time, without overlooking data or making decisions based on hunches.
Consider how proactive insight and rapid scenario modeling could save millions of dollars in contract pricing annually by repricing contracts for key commodities, raw materials or parts as conditions change. DPA could automatically optimize the structure and terms of deals and streamline customer interactions. By contrast, consider how an individual (or even a team of analysts) often struggle to optimize decision-making in rapidly evolving circumstances. The figure below shows how DPA can use information streams from tens of thousands of customers to predict order patterns across products.