Release Notes

4 Cognitive Automation
Executive summary
The application of Artificial Intelligence-based solutions has already started to fulfill the promise of business
process automation across industry after industry. As Engineering communities begin to realize this potential
and implement AI solutions to transform their own internal Software Development Lifecycle (SDLC) processes,
they should pay heed to the risks of underperforming against high expectations and over-simplification.
Initiatives such as these call for the identification of organization-specific pain points that, once optimized, can
be readily mapped into business value gains such as enhanced time-to-market, capacity scaling, or increased
quality through improved coverage. The importance of data readiness is often understated, leading to
unexpected project impacts due to data curation needs and process changes. It is also atypical for Engineering
organizations to have Data Science skillsets on their bench, but this skillset is critical to any Analytics or AI-
based implementation. Finally, Stakeholder engagement and ongoing alignment through the formulation,
implementation and deployment of AI-inspired solutions is essential to achieving business value goals.
Using the approach outlined here provides Engineering teams with a means of managing the optimistic and
sometimes unrealistic expectations of their leaders and their Subject Matter Experts with a pragmatic, yet goal-
oriented, approach to realizing the benefits of AI-based solutions within their SDLC.
Key findings:
• AI-inspired solutions in the form of Analytics Modeling, Machine Learning, Computer Vision, and Natural
Language Processing are beginning to be applied to engineering processes across the SDLC
• The application of AI techniques to processes within the SDLC represents the next generation of
Automation. We call this Cognitive Automation.
• Due to the proprietary nature of the data, processes, and tools found within any Engineering organization,
most tasks in the SDLC that are considered ripe for Cognitive Automation, are likely to require some form
of customization that will require the teaming of Subject Matter Experts and Data Scientists
• Data Science is a skill set that is essential to AI initiatives but is not a skillset that has yet established a
foothold within Engineering organizations.
• Your organizations data and processes have a high likelihood of not being ready to be leveraged for
analytics. Moving from highly manual, intuition-based processes directly to analytics-based processes is
a multi-step process.
Recommendations:
AI-inspired techniques offer the potential for automating highly Cognitive tasks across the Software
Development Lifecycle and tasks once thought to be firmly in the domain of humans. Engineering organizations
that have already instilled a culture of continuous improvement and automation, through prior adoption of Agile,
Test Automation, and DevOps practices, are the best suited to begin the exploration of the application of AI-
based solutions to their Engineering SDLC processes. They should proceed optimistically, but thoughtfully:
• Start with Why by evaluating and pinpointing the areas within their SDLC that provide the most value for
effort.
• Establish and maintain the deep stakeholder engagement that is necessary for projects of this visibility
and complexity.
• Skill-up in Data Science or select a partner who can guide the implementation of the appropriate models
to your specific tasks.
• Data Science skill sets must be combined with existing run-the-business practices and domain-specific
skill sets. Forming new, co-skilled, teams is required.