IA and Procurement: Keys to Success

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Canda Rozier

The decision has been made: You, or your company, have decided the time is right to start an Intelligent Automation project. It may be a discrete RPA implementation, a series of automation projects, or a full IA programme. Regardless of the type of IA project, or of the size or complexity of your company, there are some basic steps that will help ensure that you are successful.

These "keys" to success aren’t rocket science. They are best practices for project and programme management, for technology design and implementation (after all, IA projects are technology projects).

McKinsey nailed it when they said, “Automation has great potential to create value—but only for businesses that carefully design and execute it.” (Driving Impact at Scale from Automation and AI, Feb 2019).

So, what are the keys?

Let’s look at these and why each is critical to the success for your IA project.


You have to know what you want to achieve, and why you’ve targeted it. You have to define what process(es) are impacted, and what tasks are involved. You need to map the current state: workflow, processes, organization, resource requirements and costs, and system(s). Only then can you start to identify what can be automated, and what the impacts of that automation will be on the remaining processes (which likely may need be changed as well), and the business as a whole.


IA projects must be underpinned by the discipline of good project management, and a business case is a crucial part of this. Ideally there will be a business case for the overarching Intelligent Automation and Digital strategy, and then business cases (or mini ones) for each project implementation. This is where costs, timelines, resource impacts, and value drivers are laid out, and outcomes and deliverables are defined. The business case for an IA project should be no less detailed nor rigorous than for any other technology project . (And if your company doesn’t require business cases, that’s a whole different issue!)


You need to define the structure and organization for managing the project, implementation, support, and ongoing governance and oversight. Ideally you would create an Center of Excellence (COE) to oversee the whole of your digitization and IA journey, and all the component projects. If you don’t define “who is responsible for what”, you may well end up with an Abbott & Costello situation, except far less funny.


Although it’s not always essential, doing a Pilot or Proof of Concept (PoC) is certainly a best practice. And, if you are embarking on a full digital transformation, it’s a critical step to ensure that all aspects have been vetted and that an end-to-end IA programme has been created. It’s not just the automation tools that need to be piloted, but also your policies, support, and operating procedures. The results of the PoC also may highlight things that you need to change and rework in the business case or in the governance model.


This is one of the most important keys to success; skipping or skimping on change management can be the Achilles Heel in the project. It’s so crucial that it deserves a deep-dive all its own (coming in a future column). But at a high level, you need to ensure that organizational design and resource impacts, and the changes from the As Is to the To Be state are understood, and well documented. Training needs to be created and conducted to cover new procedures, policies, and workflows. And, a robust and engaging communications plan need to be in place throughout the project lifecycle, targeting not only affected teams and employees, but also all internal stakeholders and suppliers. Communication is not a “once and done” activity; it need to be continuous, open, and direct, and engaging.


Once the project has been implemented, you don’t just walk away. The long term success of an Intelligent Automation project or programme requires ongoing review and governance. This includes providing the Business As Usual (BAU) maintenance and support for the automation tools and processes, tracking results and validating value capture, reviewing the business case for outcomes versus projections, and evaluating ongoing opportunities and adjacencies for improvement and benefits. If you have set up a COE, it will be the basis for ensuing this is done. If you haven’t, these will need to addressed individually to ensure success.

It’s not one size fits all

How each of these keys is implemented depends on the specific situation. Not every IA project requires dedicated project resources; and not every company can justify a fully developed and staffed COE. How each of these is implemented will vary based on the your company’s size, complexity, and unique characteristics (i.e., is your industry highly regulated, etc.), as well as the complexity and scope of the tasks and functions being automated. But regardless, each of these needs to be considered and implemented to varying degrees if you are going to be successful with Intelligent Automation.

Some takeaways …

The keys for success are not unique for Intelligent Automation; they are similar to those for any technology project. That doesn’t make them less crucial, nor the benefits less important.

Remember, IA projects are multi-faceted – they are technology based, but in this case, they happen to affect Procure to Pay (or Source to Pay). All of the affected stakeholders need to be engaged; they are real key to the success and outcomes.

What “success” is varies for every project, company, and situation. Make sure you agree on a the definition and measures for success at the beginning of the project. These keys are foundational. If you don’t do them, the project may not be on solid ground; if you do, the benefits and value creation from you IA projects can be significant.

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