ENGAGEMENT
The CIO at a large Cybersecurity company recognized that the future demand for his organization’s services would outpace his organization’s growth, resulting in an opportunity to identify and implement automation. The company had grown very rapidly by focusing on sales and marketing and adding labor to manage internal processes. DayBlink Consulting was engaged to identify, prioritize and recommend automation opportunities, forecast their future benefits and determine the technologies and other requirements to implement them. The project team was initially requested to cover the technology organization, but as other organizations learned of the effort they requested input and provided requirements and opportunities as well. The program was quickly expanded to include almost all of the business units and internal organizations.
PROBLEM
The company had grown very rapidly, solved problems manually, had many duplicative systems with extensive tech debt, and recognized that it would be challenged to scale without greater investment in workflow optimization and automation. Several team members had previous experience with both RPA and other tool-based automation, but had mixed results historically and hence sought to determine the best solution for each need rather than trying to apply the same tool to every problem. Once other organizations realized that the technology team was identifying automation opportunities, they proposed their own pet projects and requested assistance, resulting in an enormous backlog of requests with limited to no methods for comparison. The Automation Center of Excellence had a well-intended leader with several junior team members, all of whom were being asked to build automations without evaluation and prioritization.
SOLUTION
DayBlink Consulting conducted interviews with over 30 client team members across 8 business functions to identify the top capability areas and underlying pain points, impacts to business drivers and potential solutions. We captured over 500 pain points and identified 49 major automation opportunities to address them. Then we developed an automation cost-to-benefit “t-shirt” sizing method and model that could be used to vet future pain points and opportunities. Importantly, we needed to maximize the automation investment despite limited funding by choosing the opportunities with the greatest benefits. Our team enriched and analyzed the pain points to create a list of automation opportunities and standardized metrics for comparison (tech, value, time, resourcing, etc.).
After identifying candidates, we assessed the potential value indexing heavily on high-volume, low-complexity, manual activities (critical factors in having positive automation ROI), but also considered factors such as quality and compliance. We then assessed the level of effort to automate each, including SDLC effort, materials costs, care and feeding (among other factors) as well as general confidence in the estimate. Each of these data was developed through primary and secondary research and validated with external vendors when appropriate.
RESULT
We sized and scored each opportunity, assessed the current tech stack and contemplated potential/desired solutions in order to provide a groomed and prioritized backlog of the top opportunities. After validating the results internally, the CIO presented the highest value opportunities (including both short-term wins as well as transformational efforts) to the Executive Leadership team as his 3 year roadmap. DayBlink Consulting then assessed the organization’s posture and readiness to support automation initiatives at scale and provided a point of view on the existing tech stack and its efficacy to support automation, resulting in purchase and deployment of several additional technologies.
Through this effort, we immediately saved several person-years of effort. The initiatives selected as part of this three-year roadmap would culminate in savings of tens of millions of dollars in automation, reduced technology debt and system rationalization. Multiple follow-on projects resulted in deployment of our recommendations.