Why Decision Intelligence is the “Last Mile” in Enterprise Software

The excitement around Decision Intelligence is tangible. And rightfully so, as this technology is creating opportunities and delivering outcomes that other technologies have promised, but never quite delivered on.

Like the last mile in a supply chain, the last mile in enterprise software functionality is often the most nuanced, complex, and difficult piece of an end-to-end process to perfect, even if it is often the shortest part of the process.

Decision Intelligence is often compared to (or even confused with) other technologies. To fully appreciate the potential and power of Decision Intelligence, let’s examine the limitations of other solutions whose functionality has left companies and users feeling frustrated, disillusioned, and dissatisfied.

Robotic Process Automation (RPA)

Let’s start with Robotic Process Automation or RPA. It sounds like a weighty solution based in machine learning and automation that should be able to handle anything thrown at it.

While it can boost productivity and scalability, the varying needs of most businesses have gotten too complex for RPA solutions to put a dent in the complexity of horizontal operations that are often interrupted, derailed, or escalated due to unpredictable events. Instead, RPA is great at dealing with known, repetitive tasks that are managed by a very rigid set of rules administered by IT teams, not users themselves, to guide their operation.

For straightforward decisions where there are no alternative choices to pick from, this could fit the bill. But once the task gets even marginally more complicated, the ability to understand context and suggest a path forward eludes the capabilities of RPA solutions. Relying on RPA in those situations can increase organizational risk, as the resulting analysis can’t factor in evolving requirements like ESG regulations, privacy requirements, or emerging trade policies.

So ultimately, while there is value in the output from an RPA solution as a feeder into other solutions (which we’ll elaborate on later), businesses can’t stay competitive with static data points alone.

Business Intelligence (BI)

Moving up the “technology evolution stack” we turn to Business Intelligence or BI Tools. Similar to RPA tools, BI tools brought a level of scalability, automation, and competitive advantage when they first became an option for an enterprise tech stack. They helped teams dig through huge amounts of data to find the problems they knew to look for.

However, as the amount of data generated by businesses has grown and the complexity of decision making has increased, there are two serious limiting factors in play. First, the amount of data analysis required to make the best decisions (often stretching to millions of rows of structured data) is beyond the best team’s capacity. Second, even the best teams are limited by their own experience and biases that can skew decision-making outcomes.

In the past, these shortcomings were just accepted and teams did the best they could. However, we are now in a competitive environment where even small decisions can have significant financial and regulatory impacts, and these limitations are no longer acceptable.

Furthermore, BI tools have significant constraints regarding accurate and comprehensive data governance, their ability to ingest and process data in real time, and their ability to understand and process unstructured data. BI tools’ functionality is backwards-looking and thus unable to create a reliable prediction or path forward for users.

Combined with a dependance on IT teams to modify these solutions when the need arises, today BI tools are too rigid to deliver the competitive advantage companies need in an uncertain, fast-evolving ecosystem.

Advanced Planning Systems (APS)

While the first two tools can be used horizontally, the third type of typical investments often compared to Decision Intelligence are Advanced Planning Systems, or APS. For decades, planning systems have focused on perfecting “the better mousetrap” to address supply-chain challenges. And by rights, they have succeeded in that effort. Built-for-purpose planning system functionality has evolved tremendously in the last two decades. For the most part, an APS is a great investment for companies that want to create faster, more accurate functionality to handle a narrow set of problems.

Thanks to APS platforms, supply and demand forecasts have improved over time, and along with them companies have seen an improvement in outputs. However, even if an APS is built with a single data model (a substantial improvement over those platforms that have been built through acquisition of different planning technologies) the outputs are still siloed into the one system.

This means that improved forecasts are of limited value to the entire organization that is desperate to improve cross-functional visibility, executional speed, and collaboration. To put this simply, if a forecast plan changes, or a supplier can’t deliver on their commitments, the action and information stays within the planning system and the users of that system.

The outcome of this is that, while the planners may know that inventory needs to be moved from one DC to another to meet targets, or a supplier with a longer lead time is now the vendor of choice, other teams that need this information – sales, marketing, manufacturing, and finance – aren’t clued in to the change, which results in organizational discord.

How Decision Intelligence Succeeds Where Other Technologies Can’t

Gartner® postulated in Predicts 2024: How Artificial Intelligence Will Impact Analytics Users that “Decision intelligence (DI) practices are emerging: This stems from the recognition that businesses must transform their decision-making processes to stay competitive, agile and innovative.”*

There is good reason for Gartner to make this statement: they also anticipate a future where Decision Intelligence delivers on business imperatives to achieve greater visibility, collaboration, accuracy, and scalability across all decisions that need to be made.

The limitations of the three closest systems have been outlined above, but it’s equally important to highlight the functional advances of Decision Intelligence platforms, particularly Aera Decision Cloud™, to truly grasp what’s possible.

First, there is a wide gap in value between the static outputs, or even recommendations, generated by these other systems versus a Decision Intelligence platformI. Snapshots in time are useful, but they lose their value quickly if not acted upon. Decision Intelligence does not create a mere data point or a single notification that a problem has been detected and needs attention – it goes several steps beyond identifying and analyzing the root causes of the problem to create a recommended solution that remedies the issue.

From this point, users can take action using a glass box solution that builds trust in the platform by detailing the rationale behind the specific recommendation; they can execute a decision that improves the financial, customer satisfaction, or operational performance of the business. Or, going several steps further, as more decisions are made over time Aera Decision Cloud learns from the outputs of each decision, improving future decisions by understanding the context and outcomes achieved, then recording them into the data model for future analysis.

This is the Aera Decision Data Model™, and it is an important functionality that separates our Decision Intelligence platform from the other technologies mentioned above. As mentioned earlier, each of those niche solutions can ingest data from internal and external sources, and in some cases they can even handle unstructured data. However, they are not designed to interconnect across the entire organization to every relevant application (ERP, CRM, SCM, etc.). Through issues such as latency or poor integration, these solutions often miss the bigger context of the information being analyzed, leaving room for error, miscommunication, or lost opportunities.

Now is the Time for Change

The reason that companies need Decision Intelligence, particularly Aera Decision Cloud, is because our Decision Data Model is a patented capability that sits on top of your enterprise architecture – a system of intelligence that not only serves up the best possible recommendations, but maximizes the value of the technology investments that you have already made.

The question is often asked, “Why do I need another technology in my organization? I’m trying to remove complexity, not add to it.” While this is a reasonable question, the answer should now be clear: Existing investments in RPA, BI, and APS solutions alone cannot analyze data horizontally; understand contextually what it means to the business; provide optimized recommendations to maintain your competitive advantage; execute these decisions (freeing up time and resources); and continue to improve business outcomes, day in and day out.

To learn how your business can benefit from Decision Intelligence, across any vertical or special circumstance, you can watch any of our recorded webinars or request a demo here.

*Gartner, Predicts 2024: How Artificial Intelligence Will Impact Analytics Users, 4 January 2024.