We’re inundated with headlines about the power of artificial intelligence (AI) these days. AI is everywhere and most businesses know they will need to adopt it soon, if they haven’t already. A recent study from Gartner shows that 37% of organizations have implemented AI in some form. That’s a 270% increase in the past four years!
I suspect that there are many CIOs feeling the pressure from their boards or C-suite peers to implement AI-related technologies. But even with the increase in the number of companies implementing AI doesn’t mean that every organization should hop aboard the AI train right now.
AI isn’t something that you can just pop out of the box and have it work effectively. Like most technologies, it requires a little preparation. Trying to implement AI in an organization that isn’t ready is a disaster waiting to happen.
How do you know if your organization isn’t ready for AI? Look for these signs.
Your processes are undocumented or unclear
You can’t just “turn on” AI and expect it to magically – and instantly – solve problems or take on those tedious, repetitive manual tasks in your organization. The algorithms that power AI can only do what they’re told to do. This means that AI needs processes – and not just any processes. Your processes need to be clear, well-defined, and well-documented.
Organizations that are ready for AI have already identified and eliminated any convoluted parts of their processes. They’ve discovered and corrected gaps in process definitions. They’ve addressed process issues that caused human intervention and eliminated any waste or bottlenecks. They’ve already documented and polished their processes so that when they are ready to automate it, that automation can be implemented easily and quickly.
Your data is a mess
AI-related technologies rely on having data – lots and lots of data. And not just any data but accurate, reliable, relevant, and trustworthy data. One of the ways that the use of AI can be effective is that the algorithms that power AI have relevant and accurate data, in the proper context, on which to take action. If your company has taken a blasé approach to data capture and quality, this is a big red flag for AI adoption. Bad data is one of the main reasons that many AI projects fail.
It’s crucial that an organization has a robust approach to data capture, management, and quality before implementing AI. CIOs and IT leaders should investigate what data they already have, why and how the data is collected, and how that data is maintained.
Like any other technology-related initiative, bad data provided to AI only means bad data – and actions – out. Trying to adopt AI using unreliable data will only result in bad outcomes – only those bad outcomes will happen almost immediately.
Your team is resistant
Even though AI is all the rage, there are many IT professionals who are fearful that AI will automate them right out of a job. Implementing AI is an initiative that requires a purposeful approach to organizational change. If one member of the IT organization is resistant, the entire implementation could be at risk.
Leaders must help their teams understand that implementing AI does not indicate loss of jobs, but that some of the tedious, repetitive work done by people are better suited for AI – freeing up people to do the things that people do best – innovate, create, think, and plan. Associates should be provided with training to grow their skillsets for use in an AI-enabled world.
Communication and transparency across all levels are key for successful AI adoption. It’s important that those who will be working with AI are involved in the implementation process as early as possible. Team members will be more likely to engage and support the initiative when they have all the information upfront about how AI will be used.
There’s no business case for AI
The use of AI is trendy and exciting, but as I’ve pointed out already, AI is not a magic bullet.
It requires an investment of time and money. For an organization to realize the value in AI and for it to be implemented and managed correctly, AI implementation must solve problems that result in improved business outcomes. This is the only way AI is going to provide any ROI.
Yes, there are some eye-catching headlines around the use of AI out there. Don’t chase them. Look for the problems and opportunities in your company where AI use would help. Look for cases where the use of AI meets a need of your business or enables the achievement of a valuable business outcome. No, it may not be the most exciting use of AI – but it will be the most valuable.
You’re afraid to experiment
This is a real fear, especially among IT teams. You are too afraid of failing, so afraid of costing the business money and being unable to show any ROI, that you are paralyzed from experimenting with making AI work in your organization.
There are going to be stumbles and pitfalls along the way with AI adoption. They are unavoidable and inevitable, just like with any new or emerging technology. The key is to fail fast and learn so you can innovate, evolve and continue moving forward. You have to experiment to determine the right data infrastructure, the volume, and quality of the data, and getting the right people into the right roles. Adjustments will be necessary. AI will evolve and your business will evolve with it. Bottom line: be prepared to make those mistakes, find the learning opportunities and share those learnings across the rest of the business.
AI is not a passing fad. It’s only going to become more embedded in our world. So while there may be pressure to begin implementing AI right now, don’t make the mistake of getting in a race you’re not prepared for – it’s the fastest way to lose.
It’s not about being one of the first organizations to use AI. It’s about using AI correctly for your organization. Look for these signs to see if you are ready for AI and fix the foundation before you zoom off into an AI future. By starting from a strong foundation, you’ll be assured of success with AI.
There’s no doubt that if you want to be an efficient IT organization, you need efficient tools. Some might say that you need the best tools.
But when happens when those tool investments fail? And perhaps, more importantly, how do you prevent poor investments from ever happening again?
Here’s a story that might sound familiar to you of a (fictional) company who made an investment in a tool and then failed to see any return for it – and what they did to improve.
The Curious Case of the Wasted IT Investment
Dwight is a CIO for a mid-sized organization. He recently convinced his boss, the CEO, Lynn, that they needed to make a significant investment in a service management tool.
Lynn, recognizing that technology was more important than ever and there were increasingly more demands on the IT organization, agreed that IT needed the best tool on the market. They agreed that they needed a tool that would grow as that demand grew. They needed a tool that would help IT drive consistency and repeatability in process execution, but at the same time, facilitate innovation as new business drivers emerged. And while they had only developed and implemented a few service management practices, they anticipated that they would need the capability to support additional service management capabilities as the organization continued to digitize its operations. It wouldn’t be too long before the organization would need to leverage capabilities like automation, process orchestration, and chatbots. And frankly, their current service management tool had seen its better days – it was time to get a modern service management tool. Perhaps even a tool that could be used within other parts of the organization!
They decided to invest in the most expensive, fully featured service management tool on the market. It truly could do anything that they wanted to do…and more!
Dwight, Lynn and the entire team were delighted with their choice! The tool can do everything. It will undoubtedly solve all of their service management issues.
But a few weeks go by, then a few months… and both Dwight and Lynn are noticing that things aren’t improving. Even with the fancy new tool, Dwight can’t get all of the information he needs to present his updates to Lynn, who wants to see that improvement and consistent performance from the use of this tool. The IT organization still isn’t doing things in a repeatable way and many team members are still performing tasks manually. Processes are still disjointed and information does not flow well from process to process – and automation is nowhere close to becoming a reality for the IT organization. Dwight consistently ends up scrambling to gather data for the management reporting needed by Lynn.
Lynn is beginning to wonder why they decided to invest in modernizing the IT organization with this tool. Meanwhile, Dwight is worried that they failed in their modernization. He is seeing other departments prove their ROI and he is fearful that he blew their budget and won’t be able to convince Lynn ever again to invest in tools.
If you purchase the most capable tool, then how do things go wrong? The problem was never in the tool. The problem was before the tool and therefore, the tool can’t fix the problem. It’s like trying to build a house on quicksand. No matter what materials you use to build the house, it’s not going to stop it from sinking until you deal with the quicksand problem.
Let’s start with where Lynn and Dwight made their mistakes.
The first mistake is thinking a tool investment was the key to modernizing IT. A tool should never be your first investment. Are tools important? Absolutely! But a tool-first mentality ignores the most important part of your organization: the people using that tool.
Let’s start with the members of IT and how they need to be a part of modernization.
Lynn and Dwight should have asked themselves:
Do the members of IT understand why we’re investing in this tool?
Do they understand what role the tool will play in their everyday work?
Do they know how the tool will improve their work?
Have they been properly trained to use every part of the tool?
The mindset and buy-in from the team is important above all because these are the individuals who will be using the tool and ensuring it’s providing maximum return. When they feel they are part of the decision-making process, they will be more invested in learning and working with the tool. If everyone in the organization is invested in working with the tool, they’ll take the time to learn it and master it so that they are actually seeing all of the benefits of its many features.
The next thing Dwight should have addressed is the organization’s processes. Dwight should have ensured his processes were clearly defined, documented and adaptable. Then he should have identified how the tool will enable those processes, and communicate the processes and the tool’s role across departments and within the IT organization.
Defining (or redefining) processes will remove any ambiguity in service delivery. It ensures that there is transparency within IT. And Dwight and Lynn will have a clear idea of how the tool is working – and how well IT is able to contribute to business outcomes.
These steps seem simple, don’t they? But Dwight and Lynn skipped them because they were so certain that investing in the premium tool would instantly (and easily) fix all of their problems. Instead, they ended up in the exact same place they were before they purchased the tool – only now they’re spending a boatload of cash, and not getting the return they had hoped. A curious case of a wasted IT investment.
The lesson for every CIO, CFO, and CEO?
Don’t invest in a tool thinking it will solve the problem. If your car wasn’t working properly, you wouldn’t just purchase a new engine and think it will do the trick. You’d pop open the hood and find out exactly what’s not working then find the part that will fix it. If there’s somewhere in your organization that isn’t operating efficiently, try popping open the hood and doing the work to find the problem before you invest in a high-price, fully-featured tool.
If you google “AI ITSM,” you’ll receive almost a million results of various articles, predictions, and guides detailing how AI will transform ITSM.
The promises in every headline are exciting:
You can learn how AI can “make great things happen in ITSM”
There is a guide to how AI can “make your service desk great”
There’s an article on how AI-based ITSM apps deliver friction-less employee experiences
And that’s just a few of the hundreds of thousands of results!
As an ITSM consultant with decades of experience helping organizations implement healthy and effective ITSM practices, all of these articles make me feel confident and excited about the future of this industry.
But at the same time, they also raise deep concerns in me because I see history repeating itself. I’m concerned this initial excitement about the transformative power of AI will have IT organizations rushing to fix every problem they have – this time, it will be with AI.
And that just won’t work. It’s going to cause bigger problems down the line. AI is not a silver bullet. It won’t solve all of your problems and no matter how powerful your AI technology is, it simply won’t fix “bad ITSM”.
In order for AI to deliver the maximum benefits, you may need to clean up your ITSM act first.
What is Bad ITSM?
Before we get into everything AI can do for ITSM, let’s first take a look at bad ITSM. You might be wondering if you are suffering from bad ITSM.
Does any of this sound familiar?
Services are not defined. The IT organization has a list of applications, systems, and activities, but there is no discussion of how these things interact to add or enable business value.
There is no documentation describing the value of what IT is doing or how that value is measured.
Projects are not evaluated according to desired outcomes or opportunities for involvement. Instead projects are evaluated strictly by cost or resource requirements. Instead of doing the right things, IT is trying to do everything.
There is no business case for ITSM or a clear understanding of the return on investment on ITSM.
Solutions are “frankensteined” together with data from one area, tools from another, and whatever resources can be afforded. Or perhaps even worse, there are multiple systems (which means higher costs) that essentially deliver the same solutions.
Other symptoms of bad ITSM also include siloed departments, frustrated team members, and unexplainably long delivery times.
Many organizations notice bad ITSM, but they struggle to clearly diagnose the problem. They see the problem as an isolated one. But once you take a step back, you will be able to see that every symptom of bad ITSM is actually interrelated. This means that fixing bad ITSM requires a holistic approach.
What Role Will AI Play?
It’s important to note that while AI may not transform ITSM, AI can play an important role in ITSM. There are 3 common cases where AI can benefit ITSM:
Amplify IT resources
AI will enable IT staff to have more time to innovate, strategize, and focus on larger, more complex problems
The use of AI technologies will promote standardized approaches to processes and workflows.
Data drives actions
Effective use of AI requires good data and information. AI adoption can encourage IT organizations to develop good habits in capturing the data and information needed to make AI use effective. By capturing good data and information as part of ITSM activities enables the organization to take advantage of the introduction of AI.
Consider these roles if you have “Bad ITSM.” Can AI amplify resources if services are not defined or if the business value of those services is unclear? Will it eliminate silos if solutions are consistently “Frankensteined” together without any guiding process? Can AI take effective and appropriate actions when data and information cannot be trusted?
While AI can be extraordinarily powerful, it needs the right environment to thrive. Organizations with bad ITSM practices don’t have the right environment.
How can you cure “Bad ITSM”?
ITSM is not just about one process or one tool. There needs to be a bigger picture of how ITSM fits into the organization, drives business value and provides services to end users.
You can start to cure bad ITSM by using outside-in thinking. Look at your ITSM efforts from the business perspective. Define how IT contributes to the needs of the customer. Then work inwards defining the services, designing the processes, and implementing the tools needed to meet the needs of those customers.
Then ensure your organization has the skills necessary to exploit and maintain AI are available:
Do your ITSM processes consistently deliver expected results? Have you clearly articulated processes for frequent tasks? Do you periodically review these processes to ensure they remain relevant?
Value stream mapping
A value stream illustrates a process as part of the larger ecosystem and is made up of all the people, activities, and departments necessary to create and deliver value. Value stream maps establish a holistic look at the process and prevents tunnel vision.
Knowledge and data management
AI can only learn from the data you provide. If your knowledge is not properly captured or your data is not well maintained, AI will struggle.
Once you’ve cleaned up your bad ITSM, you’ll be in a better position to exploit the benefits of AI. You’ll have a solid grasp on the challenges AI can solve and you can predict the desired outcomes it can provide. Then you can make a compelling business case for implementing AI.
Remember AI isn’t a silver bullet. It’s only going to thrive in an environment that has built the right foundation, and that foundation includes good ITSM. So if you need to clean up some bad ITSM, do that work now, so your AI investment will pay off in the future.
Intelligent machines aren’t coming, they’re already here. The question is: how will they change things? We’ve already discussed the future of AI and ITSM, but today I want to take a deeper look at how AI will impact DevOps.
Much of DevOps is about the automation of tasks. It focuses on automating and monitoring every step of the software delivery process. DevOps encourages enterprises to set up repeatable processes that promote efficiency and reduce variability. Artificial Intelligence (AI) and Machine Learning (ML) can help improve those efficiencies and automate even more of the process so DevOps practitioners can focus on bigger, more complex initiatives.
DevOps experts have a lot to gain by adopting AI and ML. According to ServiceNow’s report “The Global Point of View”, 85% of C-level executives believe AI can offer value in terms of accuracy and rapidity of decision making. 60% of C-level executives surveyed said decision automation can contribute to their organization’s top-line growth. But according to that same report, only 27% of have hired team members with skills in machine learning.
For current DevOps practitioners and IT leaders, it will pay off to start understanding how AI will change DevOps and how you can exploit these newer technologies
Perhaps the biggest impact AI and ML will have on DevOps is the capability to access and correlate data from disparate sources. DevOps activities generate large amounts of data. That data can contribute to many aspects of IT: streamlining workflows, monitoring systems, and diagnosing issues. However, the quantity of data can often become overwhelming for teams. So rather than looking directly at the data developers define tolerance thresholds and use only breaches of those thresholds as conditions for action. But by doing this, they are identifying only outliers and ignoring the majority. That can create larger problems because IT organizations are unable to see an informed, deep view with only outlier data.
AI can be used to collect data from multiple sources and prepare that data for evaluation. ML can then be used to identify and predict any alarming patterns and create recommendations based on those patterns. This helps to keep DevOps in a “predictive state” as opposed to a reactive one and provides continuous feedback throughout the process.
And even when there are alerts that may cause DevOps teams to be “reactive,” AI can help with that as well. Many DevOps teams may be accustomed to receiving a high number of alerts, but ML can help manage those alerts and prioritize them based on factors such as past behavior or the magnitude of the current alert. This way DevOps teams can continue to move quickly and efficiently and “fail fast,” as they are often encouraged to do.
Mask Operational Complexity
AIOps is an emerging application of AI and ML that helps DevOps teams have a consolidated and unified view of all components of the toolchain (and more). Using AIOps, an engineer can view all the alerts and relevant data produced by the tools in a single place and the team will have a holistic view of an application’s health. In many cases, the AIOps tool can take automated actions in response to data patterns and conditions, using ML and associated algorithms.
While integration testing is done as part of trunk code updates, AI and ML can be used to perform deep integration and regression testing to identify potential poor development practices.
Even more automation
DevOps wants to automate as much as possible, but for many organizations, automation is focused on code deployment. Through the use of AI and ML, infrastructure configurations or builds can be automated, tasks within processes automated, processes orchestrated, as well as remediation responses to alerts.
Where to Start Adopting AI with DevOps
Before you rush out and invest in an AI or ML tool, you need to establish your DevOps foundation so that you’re ready and capable of handling the changes AI can bring to your organization.
Remember, AI can only do what it has been designed to do. Just as with ITSM, good AI will not fix poor DevOps practices. You can start preparing for adopting AI into your DevOps environment by reviewing three key factors first.
Processes – If your processes are not documented, clear or follow then AI can’t automate them! Start with a clear, documented process and then AI can step in to help it operate more efficiently.
Standardization – The more standardized the environment, the easier it will be to introduce AI / ML into the environment. Standardization reduces variably and integration challenges. Standardize not only on the infrastructure, but also standardize tools and APIs as well.
Map the complete value stream – Some DevOps teams only look at the IT value stream as including only software development and deployment, which are important, but not inclusive of everything that is done to deliver value to a business. The complete IT value stream must include not only operations and support, but QA, security, portfolio management, and the consumer.
The future of DevOps is bright. AI and ML can revolutionize how DevOps operates but your team needs to be primed and prepared to handle these changes prior to purchasing an AI tool. Additionally, working with your team to embrace AI will contribute to their career advancements as AI and ML continue to play larger roles in DevOps and IT as a whole.
Interested in learning about AI? Contact Tedder Consulting to learn about AI workshops and consulting.
Looking for DevOps training? Learn about our DevOps training here.
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.
There’s a brand new rising star in the Service Management world.
She’s tech-savvy, has fantastic people skills and is extraordinarily productive.
Her name is Alexa. But I hate to break it to you- I don’t think she’s going to be as revolutionary as everyone says.
If you couldn’t tell by now, I’m not talking about a “real” person. I’m referring to Alexa, Amazon’s much-loved voice assistant. While Alexa has been in the consumer market for years, she’s now making the move into service management. There have been many signs that Alexa is about to become the hot new tool in service management.
Amazon has already outlined Alexa for enterprise and business solutions
ServiceNow is showing partners how to build and integrate Alexa Skills with the ServiceNow platform
FreshService is already outlining ways Alexa can assist ITSM
There’s no question that AI, machine learning and digital assistants, including Alexa, will play a role in the future of service management. I’m not here to argue that. However, I will argue that we shouldn’t be focusing on the technology but the environment where the technology will play a role. If you put Alexa in the right environment, she can thrive (and so can your organization) but if you implement Alexa with the hope that she’ll make the environment a better one, then you’re going to have useless technology on your hands and you’ll still have a long line of tickets, frustrated users and stressed out service desk technicians.
So let’s discuss how you can put Alexa (or any voice assistant) in the right environment.
What Role Will Alexa Play?
Let me start by saying that the idea of AI in ITSM is a fantastic concept. Natural language processing (NLP) can make it easier for users to find the content they need to fix their problems. Machine learning looks at data, identifies patterns or conditions, and develops new actions in response. Virtual assistants combine the two to automate tasks for technicians, providing faster solutions for end users. This allows service desk technicians to have more time and energy to focus on bigger, more complex issues.
It’s exciting to think we can live in a world that could nearly eliminate the need for manual opening, closing, and management of support tickets. It’s thrilling to someday see a sales rep saying “Alexa, open a support ticket for a broken printer,” and then Alexa quickly assigns the ticket in the correct way to the correct person. And in the not far off future, Alexa may be able to provide context for possible solutions for more complex problems using past cases, making it even easier for technicians to troubleshoot. Just imagine how remarkable that would be!
And while all of this is exciting, there’s something to remember: Alexa doesn’t come “out of the box” with this capability. She’ll never replace the humans who currently work on the service desk because she can’t gain any knowledge or accomplish any process without guidance from them.
Who is The Future Star of SM?
Like any new service desk technician, Alexa won’t be ready or able to do any of those things without the proper training, frameworks and an accurate and relevant knowledge base. She’s not the rising star of Service Management. In fact, the star of Service Management is something you already have: the foundations provided by your service management implementation.
I know what you’re thinking. Knowledge management, frameworks, and communication aren’t as exciting as AI. Who wants to pay attention to that when you can say “Alexa, tell me how many tickets are open”?
But, Alexa won’t know how many tickets are open unless she can access that information. She can’t access that data if it is not set up for her. Simply put, without the foundations of Service Management. AI won’t work in your organization. You must have proper frameworks, the right data, and inter-department communication in order to enable Alexa (or any voice assistant) to work properly.
If you’re not sure if your foundations can be put to the AI test, check on these three things.
1. Knowledge Management
AI can’t work well without good data. You need to document, gather, record and store all your knowledge into an easy-to-read knowledge base. According to Gartner, “Through 2020, 99% of AI initiatives will fail due to a lack of established knowledge management foundation.”
It takes time to optimize a knowledge base system that is all-encompassing and easy-to-access. You already have a great knowledge base: it’s your team. Use this as an opportunity to engage your team and begin preparing them for AI. No one understands what needs to be in a knowledge base quite like the people who field tickets and solve issues every day.
2. Create flexible frameworks
There’s no space for rigid approaches to the use of frameworks. Flexibility is key to success with AI. Are your frameworks and methodologies capable of adjusting to keep up with evolving projects and services? Luckily, in recent years there have been updates to traditional ITSM frameworks, such as ITIL® that allow for such flexibility. There have also been new approaches introduced, such as VeriSM™, which allows for flexibility in delivering service management. If you haven’t updated your approach to using frameworks or offered your team the opportunity to achieve new certifications in these frameworks, now is the time to do so!
3. Extend Service Management outside of IT
The success of Alexa and other voice assistants doesn’t just depend on IT. It depends on an organization of self-service, shared processes and communication. Alexa won’t have the capability to change her process depending on the context who is requesting support – unless the entire enterprise works together to manage data, share information and create effective processes that work for everyone.
Enterprise Service Management is now gaining steam. As more of these technologies are introduced, I predict ESM will become more and more commonplace. Innovative leaders are jumping on the bandwagon now and you should too.
I am just as excited about the possibilities that Alexa and other digital assistants can bring to service management as everyone else. I share these thoughts because I want a world where AI plays a major role in delivering good service management. That’s why I want every IT leader to know and master these foundational pieces for AI enablement. Because they will pave the way for massive success with Alexa or any other voice assistant or AI technology that comes your way.
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.