In this piece, we dive into important factors to consider when implementing AI into your PMO. There are so many new tools and systems that it can be hard to understand where to start or if it’s the right time to go on an AI/ML journey within your company’s PMO organization. 

Depending on who you talk to, you could glean two vastly different perspectives on artificial intelligence (AI) and Machine Learning (ML): 

1. AI/ML is here, and it is transforming the world as you read this piece, or  
2. AI/ML is here, but aside from a few cool use cases, it still has a long way to go before it can add real value to your organization.   

But if we examine the marketplace, we might find that both of those perspectives can be true, and that leaves us at “It depends” more often than we would like when making decisions on how our organizations should be strategically planning for and implementing AI/ML. This is no different for Program Management Offices (PMOs).  

Implementing AI into Your PMO: Is Your Organization Ready? 

If you’re leading a PMO and wondering if your organization is ready to implement AI/ML within your PMO, here are five questions you should be able to answer before looking at any technology, vendors, or drafting plans.  

1. What is the maturity level of my organization’s PMO?  

If your organization is at a level 1 or a 2 on the maturity scale, then maybe undertaking a larger AI/ML strategy is not right or feasible. This doesn’t mean that your organization shouldn’t be using AI features and tools to be more efficient, but embarking on a holistic AI/ML overhaul may be a hefty task given there are few standardized processes that lend a perspective on where there could be organizational optimizations with its implementation.   

On the other hand, if your organization is further on the right end of the maturity scale with more repeatable standardized processes and systems, your PMO may be ripe to implement and take advantage of all that AI/ML has to offer.  

2. What are your organization and industry data standards and regulations? 

Data is king, and depending on organizational (and industry) regulations, how you go about implementing AI/ML can be a cumbersome enterprise with such high sensitivities regarding where data is stored, who will have access to the data, and how the data will be used. Early in the discovery phase of your AI/ML journey, check with your CDO (Chief Data Officer) and legal teams to understand your data policies and whether AI/ML can be used.  

Many organizations aren’t enabling AI/ML capabilities in tools because they don’t have a defined data plan or holistically understand how those capabilities will affect the organization at large. Ultimately, before putting a lot of time and effort into a business case, know where your company and industry stand on the subject.  

3. Has my organization been exposed to AI/ML, and what level of change management will be needed to implement new processes or tools? 

When bringing a new system, tool, or capability to an organization, there are a few considerations that are often discussed: the cost savings from the implementation, the cool factor, and the productivity that can be realized by the organization. Rarely are the capabilities of the team discussed or thought about.  

If the PMO is to successfully shepherd innovations like AI, change management must be at the forefront of everything we do. In the case of having a successful AI/ML process or tool implementation within your PMO, it’s imperative that the current capabilities of your team be assessed. Having a clear understanding of where the team’s capabilities are in terms of AI/ML exposure and where they need to be in the future to realize the total value of the implementation is an important metric.   

The idea is to understand where you want your organization to be, and then pinpoint when and how to get there. Having early conversations, plans for training, and a keen understanding of the possible hurdles this new change could have on your people will give your team the preparation needed to overcome any challenges that may come up along the journey.  

4. What is the business case? 

Understand the “WHY.”  If there is not a clear organizational, business partner, or customer benefit – why put in the time, money, and effort to embark on such a journey? Knowing your “why” is going to ensure you recommend the right initiatives to get the right people and funding behind your AI/ML effort, ultimately helping to ensure the value articulated in the business case comes to fruition – and keeping your stakeholders engaged along the way.  

5. How much will it cost to implement? 

While putting together the budget, think about the holistic cost of implementation: software, hardware, training, and organizational impacts. It’s often overlooked that bringing in new capabilities creates holes to be filled in the organization, and while the big three (software, hardware, and training) are often covered, the organizational impacts don’t get the attention they need until a gap arises and there’s a need for backfill – or there’s no one within the team with the capabilities to lead the charge.    

AI/ML concepts and tools are new to most of us, and we are all learning together. Even for the most experienced of us on the topic, we often feel behind the eightball as technology and use cases are coming about so rapidly. To ensure that we are arming our organizations with the best chance of success when implementing an AI/ML initiative within the PMO, proper attention must be given to the organizational impacts in the budget to plot a clear course of success.  

Moving Forward 

Implementing AI/ML in your PMO at any level is a big decision, and as a strategic PMO leader, being able to answer the above five questions will arm you with the knowledge needed to be confident in your recommendations and next steps.  

If you’re thinking of optimizing your PMO processes and tools with AI/ML, Clarkston’s program management and analytics teams have the expertise to help throughout any phase of your assessment. Our experts will assist your team or lead the charge to establish a roadmap and plan of action that fits your specific PMO needs to ensure you successfully achieve your AI/ML goals.