Building a steady pipeline of automation opportunities

Alexander Hübel

April 4, 2022

Building a steady pipeline of automation opportunities

-        The 6th  a inseries of 10 articles - “How To Successfully Scale Automation and AI in an Enterprise.”

As many as 70% of digital transformations fall short of their objectives (BCG, 2020). Among these digital transformation initiatives, intelligent automation is one of the larger investment areas for companies today and seems to continue to be so in the future (HFS research, 2020). How can one ensure they succeed with their automation & AI strategy and become one of 30% percent that fulfills their digital transformation objectives? Based on my experience as head of automation & AI at a 'Large Enterprise' - driving it from scratch to 1000+ deployed automation & AI solutions, giving millions of hours back to the business, and transforming hundreds of core business processes. I have seen the complexities of managing scaled-up transformations based on multiple technology platforms hands-on. 

This is the sixth in a series of 10 articles where I want to share what I learned along the way and what I would do differently if I did it again today. Once you have decided on a strategy and vision for your automation and AI initiatives (article 2), decided who is doing what (articles 3 & 4), and secured your funding (article 5), it becomes essential to have an optimized way of finding automation opportunities in your company. This sixth article regards the fallacy of suffering from an Inability to create a pipeline with opportunities to automate and why that can be.

I have touched upon the criticality of engaging stakeholders and building a model that enables a steady inflow of new opportunities in the previous articles. However, I believe the topic is of such vital importance that it deserves its own article. Companies failing with this will not succeed with automation & AI. In the best case, they will have siloed sub-scaled initiatives. In the worst case, they will miss the opportunities that new technology presents.

Optimizing your pipeline of opportunities

A common reason you become stalled on your automation journey is that you run out of opportunities. To run a multiyear successful Intelligent automation journey, there needs to be a constant and focused effort to generate a qualitative pipeline. 

Senior business stakeholders are generally not thinking so much of Intelligent automation. They focus on their product or market offering, sales efforts, and operational responsibilities. Intelligent automation must fit into that perspective, not be seen as a side effort.

Two common roadblocks to filling your pipeline of opportunities. 

As Gartner (2019) and many others state, cultural resistance is often at stake here, and employees’ fear of losing their jobs is often a problem when gathering ideas within the organization. Another problem with not optimizing your pipeline might be tracking the wrong metrics and therefore disqualifying relevant ideas. Next, I will discuss these two problems and my take on them.

1. Evaluate what you are tracking.

To ensure a pipeline of opportunities, it is essential to evaluate what you are tracking. You will probably disqualify many cases if you only track full-time employee savings (FTE) since many cases do not lead to FTE savings. However, they might generate many other benefits such as lead time reductions, quality improvements, employee experience, or customer experience. Start instead to build other metrics and ideas on tracking and following up into your framework and make priorities based on that. When deciding on these, it is also imperative that stakeholders such as the CFO understand and trust these processes; otherwise, the value you deliver will not be trusted.

2. Engage your employees and prevent cultural resistance

Employees’ fear of losing their jobs is a topic that has been well written about when it comes to automation efforts. The idea is understandable, but as with many others, I also believe that the goal of automation is not to substitute the employees but rather to free-up time from boring, repetitive tasks. A study from IDC (2021) examined how RPA impacted labor and stated that automation efforts indeed affect labor; however, most companies hire more people and reassign staff when adopting RPA to a greater extent than they are terminating employees. However, to succeed with your scaling initiatives, you need to change employees’ mindsets. I believe many engagement efforts can be made, and when I was at the large enterprise, this is what we did when implementing Intelligent Automation in the IT-organization;

There are other alternatives on how to engage your employees. For this specific case, the CIO was needed to communicate, but there are so many other options you should explore in parallel. Spread the word, organize internal automation conferences, write articles, do podcasts, host events, create awards, have the employees on stage, and make them heroes. Have your CEO build their own bot and have the HR head cut the ribbon when celebrating the new HR bot. Name the solutions and celebrate wins and milestones.

Key success factors for generating a steady flow of opportunities

Here is a list of advice that I have learned through the years, key success factors in building a strong engagement generating a steady flow of opportunities for successfully scaling Intelligent Automation.

  1. Engage on the right level in the organization. Unlock detailed opportunities by engaging on an operational and tactical level. However, you need engagement on a higher strategic VP/Operational budget owner-level to open doors to larger transformational opportunities. You need to master both.
  2. Engage with the right competence. The closer to the technology and process you are, the more detailed information concerning systems, applications, process pain points, etc. A higher level instead involves more information on business and financial value. Hence, you need to engage with competence that can speak the high-level strategy level while being knowledgeable enough about the technology.
  3. Engage with a transformational mindset. At the VP level, engagement becomes like sales. Unless the specific VP has a solid transformational mindset while being tech-savvy, you will not have “leads” coming to you. Instead, you need to start selling, so you need to be relevant enough for the VP to spend time on you. Therefore, you need to understand business objectives and pain points while translating the solutions into monetary value.
  4. Continuously invest in your pipeline. As a rule of thumb, invest 50 % of the central Intelligent Automation budget in stakeholder-facing activities, at least in earlier stages of the Intelligent Automation journey. Invest this in engaging the business to drive transformation and pipeline generation. This is important as you, in most cases, must present a business case to attract sufficient funding to move forward with execution. Presenting a worked-through business case means much effort; it does not happen by itself (I discuss this further in the 5th article regarding the financial model).
  5. Establish teams with the right skill-set. You need a senior transformation-focused team that fronts the business, complemented by more junior capabilities, to do the many time-consuming and detailed steps required to qualify a use case, draw the solution, and document it properly. The team needs to be able to talk to the people involved and convince and explain why it is essential and valuable for that person and organization to invest in Intelligent Automation and what steps they need to take to create a pipeline of opportunities to execute on. I believe that these senior roles are not something you can leave to consultants. You need to own this within your organization, and it needs to be embedded in the operational strategy if you intend to scale. Consultants can, of course, support in the background and with more operational and supporting types of work.
  6. Drive the engagement leads as you drive sales efforts. Put measurable targets on engagement leads to unlock a certain amount of value, break into new units, or get a certain amount of execution projects going. Steer the team as if it was a sales team.
  7. Engage broad and cross-functionally. Even if an initiative likely starts with a few focused functional or organizational-specific engagements, you will have to engage most of the organization to scale properly. After a while, you will also realize that many opportunities unlock first when you put cross-functional lenses on the problem. Hence, you will need to create structures so that you, for instance, can tackle the whole Purchase-to-pay-flow rather than only having a standalone engagement with finance our procurement stakeholders.
  8. Set up a process and framework on how to drive the discovery process economically, functionally, and technically. Initially, create a process for RPA but expand to the broader Intelligent Automation scope as you build additional capabilities. Train the people involved in these aspects, measure and follow up on progress (I will not write much more about this point as there are multiple other sources available explaining how to do this)

A few words on process mining 

Lastly, I want to mention a few words on Process mining as I find a lot of confusion and interest in the topic when talking to automation CoEs. Process mining or process intelligence does carry a lot of promise and keys for the self-driving enterprise. I am not an expert in the details of process mining, but I did lead a team that built over 100 process mining models over several years and spent quite some effort in linking that with the wider Intelligent automation effort. In hindsight, I can say that we failed as we gained limited new automation opportunities from these models that we spent millions on creating. 

Process mining will not “automate” your discovery process. It is not a plug-and-play that auto-generates automation opportunities as many in the industry seem to believe. Process mining is not even primarily intended to do that. Process mining is an analytics tool that, when rightly used, can unlock a variety of insights and opportunities for operational improvements and real-time follow-up of process performance. Some of those opportunities might be automation opportunities, but there will also be a lot of other opportunities.

Process mining requires significant involvement of both hard-core process and data subject matter experts more than it does for general Intelligent Automation. If you embed it right into your wider Business transformation and Intelligent automation efforts, it can provide significant value, but only with these two capabilities engaged combined with the strong transformation capabilities already mentioned in this article. For Process mining to play a more vital role in Intelligent automation portfolios, the industry should work harder to link process mining outcomes and models into; 1. automation opportunities, 2. real-time follow-up of process outcomes from automation initiatives, 3. much simplified executive management visibility.


In conclusion, many suffer from the inability to create a pipeline with opportunities when trying to scale automation and AI. One reason behind this is employees who are not engaged and scared of losing their jobs. The second thing can be that you are tracking the wrong metrics, resulting in disqualifying great ideas for the wrong reason. Widen your way of looking at metrics and engage your employees to reduce their fears by showing them the better reality. Combine this with strong, well-funded transformation capabilities targeted to drive the Intelligent automation journey.

BCG, (2020). Flipping the Odds of Digital Transformation Success

HFS research, (2020). Spending on automation and AI business operationsworldwide 2016-2023

Gartner, (2019) Considerations for implementing Robotic Process Automation

IDC (2021) The economic impact of UiPath Robotic Process Automation