July 24, 2019 – Doug Tedder, principal consultant of Tedder Consulting, and Dan Turchin, Chief Product Officer and co-founder of Astound, teamed up to deliver “AI and the Future of Work”, a highly informative and interactive workshop discussing the impact of Artificial Intelligence (AI) on how IT will do its work.
The workshop, conducted on July 24 in Indianapolis, covered topics ranging from what AI is (and is not), key principles of AI, what AI means for the future of work, and what must be considered for any AI initiative.
AI is one of the fastest growing tech trends across all industries. 20% percent of business executives said their companies plan to implement AI across their enterprise in 2019, according to research from PricewaterhouseCoopers.
AI is the approach of using technologies like machine learning or bots to automate simple and repetitive tasks. The power of AI is clear. It allows for services to be delivered faster to the end user. It eases the burden of resource-strapped teams by automating simple tasks, allowing those teams to focus on larger or more strategic initiatives. It also keeps organizations competitive as new technology has created new consumer expectations that demand speed and agility.
While AI is making a splash for good reason, it is not a sole solution. Investing in AI won’t fix every issue in an organization. In fact, if implemented in the wrong environment, AI can slow down an organization and cause even more problems.
Before you jump and invest a chunk of your budget into an AI tool, you need to first review your ITSM environment. If you want to win at AI implementation, you need these plays in your playbook.
1. Clean up or create your processes
It’s simple: automation only works if you have a process to automate. If there’s no process, your AI tool has nothing to automate. AI will only master what it’s fed. You need to evaluate your current processes and workflows. Look for gaps where the process is slow due to human intervention, bandwidth issues or approval processes. Identify what is too convoluted, unclear or undocumented, too fluid or constantly shifting. This exercise will give you a clear view of what’s needed in your process and what is prime for automation.
When cleaning up your processes, you’ll want to get your entire team involved. You want buy-in from every member and you need to see the big picture of how each member contributes to a process. Meet with your team to map out your processes. Work with them to understand what each step requires and where automation can play a role.
2. Enable cross-department collaboration
AI will not work well in a siloed organization. Many AI tools facilitate integration with multiple backend systems and work across departments to deliver solutions. If your marketing team has a completely different tool, process and system than the sales team and those two departments are unable to come together to create shared processes and systems that deliver an end result, then AI won’t be able to make it better.
Every department must work together to effectively implement AI. They have to create shared processes, enable communication and clearly understand what is needed from each department to deliver a service, product, or result. Handoffs have to be smooth for automation to be able to step in and handle it.
Where in your organization is there confusion over how departments interact with one another? Are there communication issues that need to be addressed? What are the expectations and outputs of each department? It’s absolutely required that every team be on the same page when it comes to processes, approvals, goals, communications, and expectations.
IT leaders should find buy-in from other leaders to help teams integrate successfully. The goal for every leader should be a successful AI implementation that actually speeds up results. When each leader understands that this is only successful with inter-department collaboration, they will be more willing to encourage their teams to work with IT.
3. Identify and map value streams
Mapping value streams evaluates the tools, people and processes in the lifecycle of a service. Mapping value streams gives you two important things: visualization and metrics. Value stream mapping helps organizations visualize of how value and information flow through an organization. By doing so, organizations can see if any steps can be eliminated, refined, consolidated or most, importantly — automated. These metrics and data will help you be able to pinpoint exactly where AI can work, how it should work and what metrics you should use to measure it.
Mapping value streams will make it clear how AI could drive business value. This makes it easier to prioritize future implementations and integrate more AI solutions within your organization.
There’s one last important note for every IT leader to address.
It’s the elephant in the room, so to speak. Staff often feels threatened by AI so every IT leader must be able to express to their teams how AI can fuel their success. There should be no worry that staff will automate their way out of a job.
Instead, focus on the opportunities this can create. What projects are you unable to accomplish because your team is stuck doing manual, tedious, and mundane tasks? What successes are you held back from due to the limitations of manual work? Successful use of automation does require a shift in organizational culture. To create an atmosphere of acceptance, you need to focus on the potential for new projects, more exciting initiatives and a larger role in contributing to business goals.
Lastly, recognize that AI implementation is not one big project. Start small automating something of use and value. Pay attention to your metrics and adjust as the organization needs. Keeping an open mind and flexible approach to these implementations will be key to keeping them successful.