Release Notes

13 Cognitive Automation
becomes especially important if you require the assistance of Data Science skills not currently residing on your
team. Why? Because if the data science skills are coming to you via Consulting Services providers, there’s a
natural inclination for them to recommend solutions that they’ve previously implemented, which may or may not
be top priorities for your organization.
Once the context of your SDLC landscape is well understood, it’s essential to assess the value and effort that
an AI-inspired solution has to offer. It’s necessary at this stage to enlist and engender deep Stakeholder
engagement at this early stage because their domain experience and availability will be called upon throughout
the project. Brainstorming sessions, accompanied by analysis with the help of AI Planning Canvas tools such
as the one in Figure 7, can help your team comprehend the stakeholders, data required, and value of each
proposed initiative. Prioritizing the initiatives is the final step prior to engaging your data science partners.
Once you’ve begun work with your data science partners, their education begins on your domain-specific
business problems and available data. Part of any proof-of-concept in this space generally begins with an
assessment of the data and simple visualizations of that data for anything of use or insight.
Your data science partners will be the ones to recommend the appropriate modeling approach, whether it be
an Analytics-based solution, or any number of algorithms powered by Machine Learning, Computer Vision, or
Natural Language Processing. Sometimes an Analytic model is all that is needed to start deriving value from
Cognitive Automation. Analytics may offer a more transparent and straightforward debut to the adoption of
Cognitive Automation practices since it also depends on the same practical enablers of AI: Data Collection,
Data Curation, and changes to Business Process and Tools.
3.2 Proving
As you begin to build out your proof-of-concept, be prepared to have your Subject Matter Experts available for
ongoing engagement. This should be an iterative, and highly engaged process. In parallel to the development
of the prototype, the team will be assessing the viability of the critically dependent data, the processes involved
in the capturing and retention of that data. So, as an additional deliverable of the Proving phase, the team must
be prepared to deliver an assessment of the data and business process gaps, along with recommendations to
close during the Enabling phase.
Figure 7 - The AI Canvas Planning Tool