Applications for decision automation are as varied as the enterprises that use these powerful platforms. Mortgage applications, insurance claims, frequent-flyer promotions are all now largely determined by the power of automated decisions. Forrester Consulting recently reported the composite user ROI of InRule Decisioning at 421%. As varied as its use cases, private and public concerns looking to harvest the fruits of decisioning have similar questions surrounding the ultimate question of when to move automation initiatives forward in earnest.
Consult the following FAQ for answers we’ve culled over two decades and hundreds of customers.
Can we afford automation?
Engaging a first-class, no-code decisioning platform is not an insignificant investment. and can your solution scale to manage evolving needs and growing complexity. Read more on platform pricing here.
Will automated decisioning replace human decisions?
Absolutely not. The most advanced decisioning depends upon the skill and experience of human author/managers. Automation excels at repetitive, straightforward determinations based upon human-set parameters, leaving senior staff and subject matter experts to focus on the complex, outlier decisions that only they are qualified to make. Read more on the importance of keeping humans in the loop.
InRule Decisioning features robust access controls. The specified automation gatekeeper, either a technical or business specialist, holds ultimate power to restrict or democratize logic authoring and deployment capabilities as they desire. Read more on choosing decisioning managers in Good Beginnings: How to Start a Rule Project.
Who should author decision logic?
While an IT or business manager controls access to automation logic, true decisioning optimization requires direct authoring capability by those best informed to create and update automations. No-code systems remove technical barriers, enabling business users to manage complex decisioning with robust business-language commands and menus. Decisioning democratization eliminates transcription errors and improves corporate agility and response time to changing market conditions. Read further in Good Beginnings: Discovering Rule Authors.
How soon can I expect to realize returns?
Nailing down ROI requires determining and properly weighting the right mix of key performance indicators and establishing baseline data for the “before” metrics. Now comes the fabled string of “ifs”. If you establish accurate baseline measurements, if you can properly track the “new” state and if your decisioning logic is working as intended then you may realize meaningful returns soon after deployment. Six-month and periodic assessments and logic refinements will bump ROI even higher. Read more about choosing and tracking KPIs in Good Beginnings: Thinking in Metrics.
What if decisioning outcomes go askew?
The complexity and dynamic nature of automated decisioning requires human-in-the-loop oversight, control and ongoing governance to make sure outcomes align with expectations and intentions. When outcomes do go awry, robust explainability provides a detailed digital reverse roadmap, enabling logic authors to quickly trace any automation misfires to their source and update logic accordingly. Read more in AI Explainability: Why You Need It, Why It Matters.
Which decision process should we automate first?
Basically, keep your first automation foray focused, simple, measurable and meaningful to customers, staff and your bottom line. For a deeper discussion on the big issue of which decision-based process to automate first, download the white paper Taking Digital Transformation a Level Higher Through Automation: A Working Guide.