8 Big Questions To Consider For The Next Shift IA Governance
It’s now been over half a decade since global corporate enterprise began the collective yet bespoke intelligent automation journey. Billions of manual processes had been automated, billions of hours had been saved and true enterprise digital transformation had begun.
Then everything changed.
No matter what intelligent automation governance model you built, you have had to amend and refine that model since the onset of the global pandemic.
Now with organizations grappling with how to ‘return’ to work around the world, we’re in store for yet another shift of how apply IA governance.
Ty Grandison was Deputy Chief Data Officer for the Department of Commerce in the last administration and is the current CTO of Pearl Long Term Care Solutions who blends FinTech capabilities with Healthcare needs. His advice is “don't build algorithms that are using test data that is based upon the old world pattern because that's going to get you the same old crap that you had before.”
Take the opportunity to move forward and do things differently.
Director of Data and Data Science for GM, Robert Welborn has upended governance. Rather than governance being a lagging principle, “governance now comes first.” He notes that everything now starts with governance as the team has learned if “we don’t follow our own governance principles, we don't follow our own rules,” results are suboptimal.
Every disruption provides the opportunity for sober evaluation. And so, before you take the next big step in refining governance along your IA journey, consider these eight questions:
1: What are your intelligent automation resources externally and internally?
External partner/technology outcomes
- How many solution providers are working with you on your IA journey?
- What outcome is each of those providers delivering?
- How does the outcome compare what each provider promised and what wound up in each original contract?
- How does that outcome compare to what they are promising to you now?
- Does your IA IP actually reside with your solution provider talent?
- How has your internal talent IP grown throughout your IA journey?
- Has your internal talent IP grown so much that you’re at risk of talent flight?
- Where is your data housed?
- Can you identify your enterprise’s most important data?
- How has your data evolved over the course of your IA journey?
You can only be sure of where you are going on your intelligent automation journey if you know what resources you have at your disposal. If your IP resides mostly outside of your enterprise, scaling will be impossible, but simple iterative changes will also be a challenge.
If your resources are in fact mostly within your enterprise but not evolving- your intelligent automation journey will not be a pathway to the digital transformation your enterprise needs. Your insights from intelligent automation must evolve for you to avoid the entire effort becoming the latest enterprise technical debt. For more resources, join IA Week December for free here.
2: How do you evaluate what intelligent automation projects to put into production?
- What is your current governance model?
- Who is involved in the decision to put projects into production?
- How does that group evaluate which projects should be put into production?
- What about past projects informs future projects?
Your roadmap details how you realize your intelligent automation vision. Simply automating repetitive tasks does gain the enterprise efficiency and reduces overall cost. But if simply automating repetitive tasks is still your only goal, taking a next step on your intelligent automation journey will be difficult. Iterative change should lead to holistic change for your enterprise. In addition to efficiency and reduced cost, each intelligent automation project can output insights for your enterprise.
3: How do you manage intelligent automation projects?
- Once a project is put into production, who in the organization is responsible for managing that project?
- Is there a timeline with milestones baked in to each IA project?
- What is the line or reporting on IA projects?
- How do you evaluate the success of that project?
4: How do you gain insights from your current intelligent automation projects?
- Are you simply rules based?
- Is it data in, data out- no additional questions asked?
- Are you evaluating the data that you’re inputting?
- Are you evaluating the data that you’re outputting?
The insights from your intelligent automation projects are procured through the management of those projects. Having the right minds focused on the information gleaned from each project along the lifecycle of that project is paramount. Knowing the outcomes for which you are looking will provide an understanding of the intelligent automation project success.
5: How do you apply those insights to your new intelligent automation projects?
- Have you engaged solution’s embedded ML?
- Have you sourced plug and play ML to evolve your enterprise data?
- Is there a human in the loop on gaining collective intelligence from your IA journey?
The first step to scaling your enterprise intelligent automation is managing the insights from each project. The output from each intelligent automation project can be ‘activated and amplified’ through human learning and/or machine learning. The biggest win you can provide your enterprise is to have it gain from a combination of human and machine learning which provides you with collective intelligence.
6: How do you broadcast intelligent automation success throughout the organization?
- For what outcome are you measuring?
- Is your entire organization aware of the successes you’ve had- no matter how impactful?
- Are you still outbound with how IA can help the broader enterprise?
- Are you drinking through a fire hose of IA requests throughout the enterprise?
The next step in scaling your intelligent automation progress is focusing the management, collection and application of insights into outcomes that resonate across the enterprise. Scaling intelligent automation can only be realized once you gain an ability to not only showcase success to your team and immediate management, but across business units and divisions.
7: How do you scale your intelligent automation to focus on end-to-end processes?
- Have you tried to automate end-to-end processes?
- Have you abandoned the possibility of automating end-to-end processes?
- Is your iterative automation roadmap scattershot or focused in on breaking up end-to-end processes into pieces, automating and then connecting?
Having taken the first two steps in scaling intelligent automation gets you to the third step of scaling end-to-end processes which involve more than one part of the enterprise. Once you are connecting the dots across disparate enterprise pieces- true insight that speaks to the overall enterprise vision can be realized.
8: How do you scale your intelligent automation throughout the enterprise?
- Are you still only engaging intelligent automation in back-end processes?
- Are you only engaging intelligent automation in front-end customer facing processes?
- Is the team responsible for the back-end automation the same as the team that is responsible for the front-end automation?
- Is there a separate internal AI and/or advanced technologies unit from your RPA-focused activities?
Once you’ve asked yourself these eight big questions and x small questions, Max Just, Global Director, The Coca-Cola Co. suggests, “make sure that you're focusing on the things that are really going to make the business successful at this given time, and get rid of the rest.”
To provide cogent governance over your intelligent automation journey, the above questions must be asked and answered within your enterprise.
If asked and answered these questions, you have scaled intelligent automation across multiple end-to-end processes connecting disparate pieces of the enterprise, you can take the ultimate step on your intelligent automation journey of scaling throughout the entire enterprise. Learn more at IA Week December! Sign up for free here.