At its core, software is written to automate functions – fundamentally that is through workflow that orchestrates over actions. The most essential action within software that affects business outcomes are business decisions.
But oftentimes, the employee who initially established the guiding rules for a software decision will eventually leave the company – only for their replacement to tweak the criteria and alter the code accordingly. Over time, this pattern repeats itself, and no one other than a developer really knows how the decision is being made.
As a result, seeking to make changes to improve business results is challenged by the lack of visibility to what the business rules really are.
Enter: The Separation of Concerns framework – a new concept aimed at transforming this approach to application development. This framework combines artificial intelligence (AI), machine learning (ML), and decision management (DM) – all strategies that enable software businesses to deliver high-quality products to market faster.
Divide and Conquer
The separation of concerns approach centers around the extraction of both declarative decisions – those that generate the same answer from a certain input – and AI/ML decision-making processes – those that return a probability score and adapt over time. This frees applications from the inherently complex web of decision logic, paving the way for increased efficiency.
Let’s say a piece of software contains ten different decision algorithms within its array of workflows. The aim of the separation of concerns approach would be to isolate those decision-making processes and treat them as individual assets, each of which can be versioned, tested, and deployed autonomously. In doing so, it may turn out that the same decision is needed in several other use cases such as calculating an insurance quote, formulating an underwriting assessment, or detecting evidence of claims fraud, and so on.
By breaking down intricate systems into manageable insolated components, developers can focus on optimizing specific functionalities without compromising the integrity of the overall application. That way, they can easily hone the most appropriate decision-making protocol and relay it in clear terms to the employee who ultimately needs to establish the rules.
Streamline, Unlock, Enhance
Streamline the Decision-Making Process
A primary advantage of the separation of concerns approach is its capacity to streamline decision-making. When decisions are separated from workflow, the technology powering a company’s application suite can change as needed without undermining a business’s wider operations or objectives. Afterall, managing a business decision should not have to require a deep understanding of the programmatic code logic behind decision criteria.
Moreover, organizations can more easily adapt to shifting market forces and update their decision-making accordingly without having to apply extensive, subsequent modifications throughout their workflow – the alternative would be like rebuilding a whole house when kitchen renovations would suffice.
Just as bookkeepers are able to manage company finances independently via Excel without having to write up a spec or involve a software engineer, business leaders should be able to do the same when formulating decisions and adjusting their criteria. This agility is vital when responding to emerging trends and accommodating new user needs.
Unlock Compatibility Between AI/ML and Decision Management
With any given segment of decision logic extracted and managed as a separate corporate asset, integrating advanced AI/ML algorithms becomes a seamless process. This integration opens up an entirely new realm of possibilities – especially when combined with declarative decisions – allowing organizations to harness the full potential of data-driven insights and intelligent decision-making.
Enhance Adaptability and Scalability
The baseline goal for business leaders is always to accelerate better products to market, but the separation of concerns approach is able to accomplish much more.
Notably, it affords direct and ongoing visibility into any business decision and the criteria that influenced it, enables seamless incorporation of new technological capabilities without requiring an overhaul to the base application, and creates opportunities to bring AI/ML deeper into core business operations. In other words, decoupling decision-making from in-house applications presents companies with additional ways to adapt and scale alongside the evolving software application market.
More Than a Theory
Separation of concerns is more than a theoretical concept; it’s a practical strategy for bolstering low-code and no-code solutions, transforming how businesses operate in the digital age.
Finance enterprises, healthcare firms, manufacturing facilities, and more, are experiencing increased operational efficiency, shorter development cycles, and greater compatibility between AI/ML algorithms and decision management.
Providing transparency to business decisions as well as the ability to manage them independently of the criteria written into complex blocks of code grants companies a significant competitive edge. The fact that this approach to application development is augmenting the adoption of AI/ML systems is further proof-of-concept.
By liberating decision-making processes and fostering collaboration between AI/ML and decision management, organizations can unleash a new era of innovation, shifting businesses into a position where they can thrive in the face of technological disruptions.
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