Tag Archives: AI

AI, ITSM

The 3 Pillars of Success for AI-enabled Service Management

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In her book[i], Dr. Kavita Ganesan suggests that any AI adoption be evaluated using three pillars:

  • Model success – Is the AI model performing at an acceptable level in development and production? (In other words, the model performs at the required levels of accuracy, execution time, and other factors.)
  • Business success – Is AI meeting organizational objectives?
  • User success – Are users satisfied with the AI solution and perceive it to be a valid solution?

Many organizations are rushing to incorporate AI-enabled technologies to improve their service management capabilities. AI technologies, such as AI-assistants, chatbots, intelligent process automation, generative AI, and more, can provide a next-level set of capabilities for service management. But are these organizations’ service management practices positioned to fully take advantage of these new capabilities?

Let’s be clear – AI is not a “magic wand.”  AI is a technology. And like any other technology, there are factors that must be addressed if an organization is to realize the benefits that AI can bring to service management.

First, AI needs data – and lots of it. The effectiveness of AI depends on the quantity, quality, relevancy, and timeliness of the data being used by the AI models and algorithms. Any limitations in the data being used by AI will be reflected in the outputs produced by AI – and the use of those outputs by service management processes. The old axiom remains true – garbage in will result in garbage out.

AI cannot be a solution looking for a problem. Just because AI is a “hot topic” now doesn’t mean that it is the solution for every business challenge – especially service management issues. If issues like ineffective workflows, undefined services, poorly defined measures, lack of continual improvement practices, or the absence of high-quality data already exist within the service management environment, the introduction of AI will only exasperate those issues.

Lastly, the use of  good organizational change management practices is critical. There is a lot of FUD (Fear, Uncertainty, and Doubt) surrounding the introduction of AI[ii] within organizations. Yes, there will be impacts to how humans work and interact with technology, but for whatever reason, there is a heightened fear associated with AI-adoption within service management.

Applying the 3 pillars for AI success to AI-enabled Service Management

Before rushing into incorporating an AI solution with a service management environment, let’s adapt and apply Ganesan’s three pillars for success with AI-enabled service management.

The first pillar is business success. How do current service management capabilities support business outcomes and enable value realization? How will the introduction of AI capabilities further enhance the realization of the outcomes and value delivered by service management? If the answers to the above questions aren’t clear, revisiting some foundational elements of service management is in order. Consider the following:

  • Have IT services been defined, agreed, documented, and measured in terms of business value, business outcomes, and the costs and risks associated with the delivery of services? Many IT organizations have defined what they call “services” in terms of
    • what goods and products (like laptops and smart devices) are provided
    • the service actions (like password resets) a service desk will perform, and
    • procedures for gaining access to digital resources (like a cloud-based resource or a shared drive).

Not only does this approach inhibit a mutual understanding of the vital role of technology in business success, but it also commoditizes what IT does. Secondly, this approach fails to establish business-oriented measures regarding results and value.

  • Are non-IT colleagues named as service owners? Are these non-IT colleagues actively involved in the delivery and support of services? This is a significant issue for many service management implementations. In many organizations, IT personnel, not non-IT colleagues, have taken on the role of service owner – the person that is accountable for a service meeting its objectives and delivering the required business outcomes and value. The service owner is critical to understanding what is needed and importantly, how business outcomes and value are realized and should be measured.
  • How might AI adoption enable organizations to consider service management practices that would enhance their business? For example, better service portfolio management would enable better utilization of and data-driven investments in services and technology.

The next pillar is employee success. Frequently (and counterintuitively!), service management practices have been designed and implemented with IT and not the IT service consumer in mind. As a result, interacting with the service desk or a self-service portal can be an exercise in frustration due to the over-technical nature of those interactions. Consider:

  • How might the introduction of AI result in friction-free interactions with services and the fulfillment of service requests? How might AI personalize end-user interactions with service management practices? Consider how AI could shift the burden of interacting with service management practices from the end-user to a personalized and proactive AI-enabled capability.
  • How might the introduction of the AI model result in friction-free interactions with supporting IT services? If consuming IT services present challenges to end-users, it can also be challenging for those that deliver and support those services. Will AI-capabilities enable service management practices to shift from a reactive to proactive stance by identifying and eliminating causes of incidents before they occur? Will AI-capabilities enable better issue resolution by suggesting potential solutions to IT technicians?
  • How might the introduction of AI enable employees to make better, data-driven decisions based on relevant, timely, and accurate knowledge? Knowledge management is among the most significant challenges of a service management implementation, as knowledge is ever evolving and continually being created, revised, and applied. AI may provide a solution – this blog explores how Generative AI could provide organizations (not just IT) with the capability of harnessing its collective knowledge.

The final pillar is AI / service management model success. Frankly, many service management challenges can be resolved through continual improvement activities. Some issues may be resolved through the application of effective and efficient automation. Questions to consider include:

  • How might AI adoption result in better and proactive detection and resolution of issues before those issues impact the organization? How might AI adoption result in improved change implementations through better testing or confirmation of positive business results?
  • Is there sufficient, good-quality data to enable AI-driven service management actions? If AI models are not supplied with sufficient, good-quality data, the results from the model will be suboptimal at best – or worse, just flat-out wrong.
  • What is the required level of accuracy for the model? A “100% accurate” model may be too costly to achieve and maintain; a “75% accurate” model may be perceived as a failure.

Get ready for AI-enabled service management

The introduction of AI to a service management environment can be a game-changer on many levels. Here are four steps to get ready:

  • Make the business case for introducing AI to service management. Think strategically about AI , service management, and how the combination of AI and service management will help the organization achieve its mission, vision, and goals.
  • Communicate, communicate, communicate. The mention of AI adoption may cause concerns among employees. Start open conversations regarding AI-enhanced service management capabilities, incorporate feedback, and proactively address concerns.
  • Identify and define success measures. The mere implementation of AI capabilities within service management is not an indicator of success. Define how the benefits articulated in the business case will be captured, measured, and reported.
  • Begin data governance now. The success of any AI initiative depends on the availability of good quality data. If service management is to leverage AI capabilities, the data being captured must be of good quality. Define and publicize data quality standards for service management practices and ensure compliance through periodic audits.

The introduction of good AI capabilities will not fix bad service management. Applying the three pillars described above will ensure successful introduction of AI capabilities resulting in next-level service management practices for any organization.

Is your service management approach “AI-ready”? An assessment by Tedder Consulting will identify any foundational gaps so your service management environment is “AI-ready”.  Contact Tedder Consulting today for more information!

[i] Ganesan, Dr. Kavita. “The Business Case for AI: A Leader’s Guide to AI Strategies, Best Practices & Real-World Applications”.  Opinois Analytics Publishing, 2002.

[ii] https://www.forbes.com/sites/jenniferfolsom/2024/03/28/meet-your-newest-co-worker-ai  Retrieved April 2024.

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ESM

Three reasons why now is the right time for ESM

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Enterprise service management (ESM) is an organizational capability for holistically delivering business value and outcomes, based upon shared processes, appropriate technologies, increased collaboration, and better communication across the organization.[i]  ESM, done well, enables positive customer and employee experiences, improves business agility, and enables impactful digital transformation.

ESM is nothing new. ESM, as discussed as early as 2005, was characterized as simply extending IT service management (ITSM) practices across the organization. Since then, organizations have realized that ESM needs to be an enterprise capability and competency – not just the domain of a single department.

Effective ESM gets the entire organization on the same page. Good ESM practices reflect and support the entirety of enterprise value streams, not just the IT portions. This enables teams to have clarity around how work and value flows through the organization, and how technology underpins that workflow and enables value realization. And in the digital age, knowing how work and value flows through an enterprise provides the organization with the ability to quickly shift and react to changes in market spaces – critical for business success.

But herein lies a couple of challenges.

Many organizations are “process poor,” so there’s been little effort in defining and documenting processes, depicting how inputs are transformed into measurable outputs. Secondly, individual departments often operate in isolation or function as if they’re at least somewhat isolated from others within the organization. Organizational workflows and value streams are poorly understood, and if value streams are defined, those value stream definitions are often limited to a single department.

Three reasons why now is the right time for ESM

Why is now the right time for ESM?

  1. Digital age organizations can’t afford to have siloed departments working in isolation. Everyone within the organization must understand not only how their work contributes to success, but also the upstream and downstream impacts of their work. ESM facilitates this shared understanding of how work is done within an organization.
  2. Automation and AI adoption benefit from effective ESM. Generative AI (GenAI) could be used to generate and maintain knowledge across the organization. Capturing, generating, and maintaining knowledge is among the most tedious activities within an organization. Robotic Process Automation (RPA) and Intelligent Automation (IA) could result in improved customer experience and more resilient and efficient operations of customer-facing activities.[ii] Good ESM standardizes workflows that underpin enterprise value streams, enabling the capabilities of these and other emerging technologies.
  3. The world is mobile. Despite recent organizational return-to-work mandates, the work-from-anywhere genie is out of the bottle. And if the workforce is mobile, it only makes sense that the customers of an organization are also mobile. Both employees and customers expect a frictionless experience when interacting with systems and technologies. Again, good ESM underpins enterprise value streams and workflows that enable efficient and effective workflows and experiences for both employees and customers.

The opportunities are here – is your organization ready?

Today’s digitally driven, consumer-focused economy demands that organizations conduct business at digital speeds.  At the same time, organizations must deliver a differentiated customer experience over their competitors. And if that’s not enough, organizations must also ensure that risks have been optimized as both employees and customers interact with technology. Seems impossible, doesn’t it?

But that is what effective ESM can do for an organization. ESM, done well, results in increased operational efficiency, improved collaboration between departments, reduced costs, enhanced customer satisfaction, and the capability to adapt quickly to changing business needs. ESM helps enforce better governance and compliance. And improved service delivery resulting from good ESM enables a differentiated customer experience.

There are two critical success factors for ESM adoption.

First, implementing ESM usually involves significant changes to existing processes, roles, responsibilities, and workflows. Organizational change management becomes crucial to address resistance, communicate the benefits of ESM, and provide proper training and support to employees. Managing this organizational change effectively can pose a significant challenge. But the result will be having enabled and confident employees that are more engaged, more invested, and that have and deliver a better experience.

Effective ESM requires integrating data and systems from various departments and teams. This can be challenging due to disparate systems and legacy technologies within an organization. Ensuring smooth data and system integrations is crucial for effective ESM implementation. Taking an iterative approach provides the opportunity to “learn by doing” and realizing some quick wins, while at the same time, optimizing risk to the organization.

Get started with ESM

The sooner that organizations begin ESM adoption, the sooner the organization will realize the benefits of an integrated and responsive organization. Here are four suggestions for getting started with ESM.

  • Define your digital strategy. How will digital technologies enable the organization to achieve its mission, vision, and goals?
  • Make the business case. Document the reasons why the organization should adopt ESM. Define the specific objectives for ESM, including opportunities, benefits, financials, and risks.
  • Establish a guiding coalition. Having a group of committed people to guide, coordinate, and communicate ESM efforts is a critical early step for success.
  • Map value streams. Understanding how value moves through an organization is critical if an organization is going to elevate itself above siloed work and into true enterprise service management.

The differences between organizations that are internally siloed and those that are truly agile and integrated will only become more pronounced as technologies such as automation and AI become more mainstream in the world of digital organizations. Now is the time for ESM.

If your organization is struggling in its digital evolution, it is time to develop your digital strategy, map value streams, and adopt ESM – and we can help. Contact Tedder Consulting today to find out how.

[i] https://www.dougtedder.com/2021/02/01/esm-business-strategy

[ii] https://omdia.tech.informa.com/om019736/more-vendors-squeeze-into-the-intelligent-automation-space-as- enterprises-embrace-the-technology

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AI, EX

The more AI we become, the more human we need to be

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Why are AI assistants given human-like names?

Apple provides Siri[i]. Amazon has Alexa[ii]. Samsung features Bixby[iii]. And there are literally dozens of other examples, in use both publicly and privately.

The attribution of human characteristics to non-human entities is known as anthropomorphism. Attributing human intent to non-human entities, such as pets, robots, or other entities, is one way that people make sense of the behaviors and events that they encounter. We as humans are a social species with a brain that evolved to quickly process social information.[iv]

There are numerous examples of anthropomorphism with which we are familiar, and honestly, don’t even think twice about. In Toy Story[v], the toys can talk. In Animal Farm[vi], the animals overthrow their masters and govern themselves.  In Winnie-the-Pooh[vii], Christoper Robbin interacts with Winnie, a talking bear.

Is this why AI-enabled chatbots and digital assistants are given human-like names? To make us want to talk to them? To make it easy to interact with them? To influence our thinking and behaviors?

Without over psycho-analyzing the situation (and I am far from qualified to do so), the answer to the above questions is “yes”.

The good – and not so good – of today’s AI capabilities

AI capabilities have been around for quite some time. While philosophers and mathematicians began laying the groundwork for understanding human thought long ago[viii] , the advent of computers in the 1940s provided the technology needed to power AI. The Turing Test, introduced in 1950, provided a method for measuring a machine’s ability to exhibit behavior that is human-like. The term and field of “artificial intelligence”, coined by John McCarthy in 1956, soon followed.

The past few years have seen a dramatic expansion of AI capabilities, from machine learning to natural language processing to generative AI. That expansion has resulted in impactful and valuable capabilities for humans. AI is well-suited for managing tedious and repetitive tasks. AI can be used to initiate automated actions based on the detection of pre-defined conditions. AI can facilitate continual learning across an organization based on the data captured from interactions with and use of technology. And most recently, AI is developing a growing capability to respond to more complex queries and generating responses and prompts to aid humans in decision-making.

But despite all the progress with AI, there are some things that are not so good. Miscommunication can occur due to limitations of a chatbot or an AI assistant in understanding user intent or context. A simple example is the number of ways we as humans describe a “computer”, including “PC”, “laptop”, “monitor”, or “desktop” must be explicitly defined for an AI model to recognize the equivalence. AI is not able to exhibit empathy or the human touch, resulting in frustration, because humans feel that they are not being heard or understood.[ix]  AI is not able to handle complex situations or queries that require nuanced understanding; as a result, AI may provide a generic or irrelevant response.[x]  The quality of responses from AI is directly dependent upon the quality of the input data being used – and many organizations lack both the quality and quantity of data required by AI to provide the level of functionality expected by humans. Lastly, but perhaps most importantly, the expanding use and adoption of AI within organizations has resulted in fear and anxiety among employees regarding job loss.

Techniques that will help humanize AI

Several techniques can help organizations better design human interactions with AI. Here are a few to consider that can help humanize AI.

  • Employing design thinking techniques – Design thinking is an approach for designing solutions with the user in mind. A design thinking technique for understanding human experience is the use of prototypes, or early models of solutions, to evaluate a concept or process. Involving the people that will be interacting with AI through prototypes can identify any likes or encountered friction in the use of AI technology.
  • Mapping the customer journeys that (will) interact with AI – A customer journey map is a visual representation of a customer’s processes, needs, and perceptions throughout their interactions and relationship with an organization. It helps an organization understand the steps that customers take – both seen and unseen – when they interact with a business.[xi]  Using customer journey maps helps with developing the needed empathy with the customer’s experience by identify points of frustration and delight.
  • Thinking in terms of the experience – What is the experience that end-users need to have when interacting with AI? Starting AI adoption from this perspective provides the overarching direction for making the experience of interacting with AI more “human”.

Start here to make AI use more human

AI adoption presents exciting opportunities for increasing productivity and improving decision-making. But with any technology adoptions, there is the risk of providing humans with suboptimal experiences with AI. Here are three suggestions for enabling good human experiences with the use of AI.

  • Define AI strategy – Success with AI begins with a well-defined strategy that identifies how AI will enable achievement of business goals and objectives. But AI success is not just business success or technical success with AI models, but also whether users are happy with AI and perceive it to be a valid solution. [xii]
  • Map current customer journeys – Mapping current customer journeys may expose where user interactions are problematic and may benefit from the introduction of AI.
  • Start and continually monitor the experience – Happy Signals, an experience management platform for IT, states that “humans are the best sensors”.  Humans are working in technological environments that are in a constant state of change and evolution. Actively seeking out and acting upon feedback from humans regarding their experiences with technology raises awareness of the user experience and fosters a more human-centric approach to technology use and adoption.

The best way to ensure that AI-enabled technologies are more human is to design them with empathy. Design thinking, customer journey mapping, and experience management will help ensure that AI stays in touch with the “human” side.

Need help with customer journey mapping? Perhaps using design thinking techniques to develop solution-rich, human centered solutions for addressing challenges with customer and employee experience? We can help – contact Tedder Consulting for more information.

[i] “Siri” is a trademark of Apple, Inc.

[ii] “Alexa” is a trademark of Amazon.com, Inc. or its affiliates.

[iii] “Bixby” is a trademark of Samsung Electronics Co., Ltd.

[iv] https://www.psychologytoday.com/us/basics/anthropomorphism , retrieved March 2024.

[v] Lasseter, John. Toy Story. Buena Vista Pictures, 1995.

[vi] Orwell, George. Animal Farm. Collins Classics, 2021.

[vii] Milne, A.A., 1882-1956. Winnie-the-Pooh. E.P. Dutton & Co., 1926.

[viii] Wikipedia. “History of artificial intelligence”. Retrieved March 2024.

[ix] https://www.contactfusion.co.uk/the-challenges-of-using-ai-chatbots-problems-and-solutions-explored , retrieved March 2024.

[x] Ibid.

[xi] https://www.qualtrics.com/experience-management/customer/customer-journey-mapping  , retrieved March 2024.

[xii] Ganesan, Kavita. “The Business Case for AI: A Leader’s Guide to AI Strategies, Best Practices, & Real-World Applications”. Opinosis Analytics Publishing, 2022.

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AI, GenAI, ITSM, Knowledge Management

AI-enabled Knowledge Management might be low hanging fruit…if we can only reach it

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AI-enabled technologies have captured the imagination of every organization. Organizations (both solution providers and buyers) are rushing to jump on the wave of adopting and integrating AI.

Indeed, AI-enabled technologies have already found their way into IT support. An AI-enabled chatbot of today makes its predecessor chatbot of just a few years ago look… well, archaic. AIOps solutions have increased the IT support organization’s observability capabilities by bringing disparate sources of real-time operational data into a single view, facilitating more proactive actions and automated responses when predefined conditions are met.

But one of the challenges exposed by this initial wave of AI adoption within IT support organizations is the inadequacy of its approach to knowledge management. AI-enabled chatbots and AIOps solutions need both data (lots of it!) and organizational knowledge (lots of this too!) to be effective for use.

Knowledge Management (KM) is a key factor in an organization’s capability for being responsive, for driving efficiency and effectiveness, and for making the best use of limited and precious human resources. I believe that effective KM provides organizations with the capability to adapt, shift, change, and respond appropriately, especially in today’s unpredictable and ever-changing business and technology environment.

But many organizations have found that their KM practices aren’t enabling such a capability. Contributing to this situation are a few factors.

  • Knowledge becomes stale very quickly – if not maintained. The business and technology environment are continually changing. Stale knowledge is not just “stale” – it can be just flat-out wrong, making it unreliable and worthless.
  • In many organizations, it is the IT department that is trying to capture, develop, manage, and use knowledge. Even worse, in many IT departments, it is just the service desk that is investing effort into knowledge management. And many of those service desks, knowledge articles are just a defense mechanism, developed in response to (irate) user demands.
  • IT-authored knowledge articles are usually written in “geek-speak” and often read like a technical manual. Such articles are not helpful with enabling consumers to self-service or self-resolve any technology-related issues.
  • We (IT) just aren’t that good at writing – not just knowledge articles, but anything that doesn’t resemble application code or scripts.

Enter GenAI

Could the use of GenAI as part of an organization’s KM practices be the low-hanging fruit that delivers the transformational return that organizations need?

Generative AI, or GenAI, are algorithms that can be used to create new content.[i]

GenAI adoption has huge potential to address both the challenges in current approaches to KM, as well as enable organizations (not just IT or the service desk) to better capture, manage, and use its collective knowledge. How could GenAI address the challenges organizations have with KM?

  • Overcome that writer’s block. Writing knowledge articles is often viewed as “extra work.” Moreover, those that feel that they are not good writers tend to avoid documenting knowledge in the moment. Using GenAI capabilities and its use of LLMs (Large Language Models), first drafts of knowledge articles can be developed, based on what is entered into systems of record, prior LLM training, and prior curated knowledge articles.[ii] This draft can then be reviewed by experts before being published for use.
  • Finally, self-service! The conversational capabilities of GenAI can replace the cumbersome “search and try it” approach with a conversation-like interaction for self-service. Conversation like responses create a compelling pull for the customer; when it works how they expect it to and gets them back to doing their work more quickly, they will return to using self-service.[iii]
  • Keeping knowledge fresh. Perhaps the most significant challenge of KM is keeping knowledge relevant and current, regardless of where knowledge is created. Frankly, organizations cannot afford to appropriately hire enough staff to perform this critical, yet often tedious, work. Using the machine learning capabilities of GenAI, new knowledge can be created by combining and synthesizing information from various sources.[iv]
  • Making KM an organizational capability. Organizations have long emphasized creating and maintaining documentation, from topics ranging from processes, policies, governance requirements, security, products, applications, and more. There is a wealth of information in different formats for specific needs. LLMs excel at transforming data from one state into another. In the knowledge management use case, this means enabling any knowledge worker to be a knowledge-creation expert.[v]

Warning – challenges ahead

With all the hype and early success around GenAI, it is understandable that an organization may develop a bit of FOMO (Fear Of Missing Out) if they’ve not started adoption. However, FOMO-driven initiatives rarely return any of the expected benefits, and often become money-pits. What challenges do organizations need to address before considering GenAI adoption?

  • Ethics and Integrity. Successful implementation will require a focus on ethics, privacy, and security. Guardrails within services and tools as well as ground rules for acceptable use will separate enterprise success from low-level experimentation. From the IT service desk to the software development pipeline and even outside of IT, generative AI is positioned to impact the way work gets done.[vi]
  • Data Governance. Organizations must realize that when it comes to GenAI and its use of LLM that “Garbage In” results in “Garbage Out” (GIGO). GenAI responses will only be as good as the data that is used to train the AI. Most organizations lack actively defined and enforced data governance policies.
  • Infrastructure impact. The algorithms behind AI are quite complex. LLMs require more computer power and larger volumes of data. The more data available, the better the training of the AI and its associated models. The more parameters defined within a model means the more computer power required. [vii] Investments in infrastructure will be required. AI complexity – LLM require more computer power
  • It’s not just about ROI or cost-cutting. It can be extremely easy to look at the introduction of AI-enabled technologies simply as a way to cut costs, reduce headcount, or increase ROI. AI-adoption requires investment, training, and competent people to have success, so view GenAI-adoption success in terms of reduced costs or reduced headcount. Increasing ROI sounds good but measuring ROI (as with most things technology-related) is often difficult. Success metrics such as scalability, ease of use, quality of response, accuracy of response, explainability, and total cost of ownership[viii] should also be considered.

Get ready for GenAI-enabled KM

As with any emerging technology, GenAI presents potential opportunities and capabilities for many organizations. Here are some suggestions for getting ready for GenAI.

  • Learn. Most every GenAI solution provider offers no-cost learning opportunities through webinars and publications.
  • Review current KM-enabling policies and strategy. What is working well in the current approach to KM? Where are there gaps and resistance? What are knowledge consumers saying about their interactions with knowledge bases? Answers to these questions provide a base for evaluating GenAI solutions for KM.
  • Identify areas where improved KM can impact organizational objectives. Identifying how improved KM capabilities can have a positive impact on organizational strategy and objectives is a critical first step in developing a strong business case for GenAI.

GenAI can provide a means for addressing many of the challenges organizations (not just IT) face with its KM practices. It may just be the key to success for the modern organization in the ever-changing digital world.

[i] https://www.mckinsey.com/featured-insights/mckinsey-explainers/what-is-generative-ai , retrieved January 27, 2024.

[ii] https://www.forrester.com/blogs/knowledge-management-id-like-to-introduce-my-new-friend-generative-ai/, retrieved January 22, 2024.

[iii] Ibid.

[iv] Ibid.

[v] Ibid.

[vi] https://www.ciodive.com/trendline/generative-ai/404/?utm_source=CIO&utm_medium=1-2BlastJan18&utm_campaign=GeneralAssembly, retrieved January 22, 2024.

[vii] https://www.ml-science.com/exponential-growth, retrieved January 23, 2024.

[viii] https://ciodive.com/trendline/generative-ai/404/?utm_source=CIO&utm_medium=1-2BlastJan18&utm_campaign=GeneralAssembly, retrieved January 23, 2024.

 

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Digital Transformation

You Can’t Automate What You Don’t Understand

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The case for automating workflows is a strong one. There are plenty of reasons why organizations are looking for the right automation tools, including but not limited to:

  • Frees staff from performing tedious, high-volume, low-value tasks
  • Creates cheaper and faster process execution
  • Improves customer experience
  • Makes it easier to scale

I’m not here to argue the case of automation. When done correctly, it can achieve all those benefits above. And many organizations see success when they automate simple, one-step tasks, like password resets.

However, automation can start to feel like a catch-22, especially for those organizations who realize initial success with their simple automated tasks. That’s because they start the automation initiative by looking for the right tools. Many automation conversations in organizations are about the various tool vendors and weighing the features of each tool. And for simple automations, perhaps that’s not a bad way to make decisions.

But if you want to automate multi-step, complex workflows, the tool is the last thing you need to identify. Let’s explore how to make sure you get these multi-step automations correct.

Principles of Good Automation

1. Automation often means orchestration
The term “automation” is often used to describe things that are actually service orchestration. Automation is the act of automating a single task, like password resets. Orchestration refers to automating multi-step processes to create streamlined, end-to-end (and often inter-departmental) workflows. When determining your automation needs, be clear on whether your goal is only to automate or orchestrate.

2.Don’t automate or orchestrate “just because you can”
Every organization has plenty of workflows and tasks from which to choose to automate. But just because you can automate something doesn’t mean that you should, especially in the first stage of your automation initiatives. You want to focus your initial efforts on the tasks that:

    • Are performed on a high-frequency basis, are tedious for people to perform, but are well-defined and produce predictable results.
    • Consume a disproportionate amount of a team’s time. This may indicate that the process is not well-defined to begin with! In this case, be prepared to first invest time into process design.
    • Drive the most ROI for your business. It doesn’t make sense to spend hours and hours defining and automating a task that is only performed on an infrequent basis.

3. Everyone involved must be ready for orchestration for it to work
Creating multi-step, complex workflows almost always involve more than one team or person. You have to have everyone involved in the entire process involved and that requires a level of transparency from everyone in the organization.

Too many organizations begin automation initiatives despite having little insight into the actual steps involved in a workflow—and therein lies the problem. Those organizations are trying to automate work that they don’t understand.

Gaining Transparency is key

The solution for avoiding automation and orchestration missteps is to start by gaining transparency into the work currently being performed – before you start to automate. Here’s how:

  • Get the whole team involved. Automation and service orchestration has to be a collaborative project, or it will never work. People are often resistant to automation initiatives because they do not understand the objectives of the initiative or were not provided with an opportunity to provide feedback. To help overcome this resistance, illustrate how orchestration and automation will not only improve productivity, quality, and efficiency, but will also improve the employee experience by removing toil from daily work.
  • Identify needed business outcomes. Business outcomes are king to all else. You’re going to burn precious resources spending so much time automating tasks and orchestrating procedures that don’t result in measurable and valuable business outcomes. Before automating, first evaluate how a particular workflow achieves business outcomes
  • Understand end-to-end workflows. Does everyone on the team have a shared understanding of each step in a workflow? Is there a clear understanding of how each team contributes to that workflow? Many organizations don’t have this type of insight and it causes massive breakdowns during the execution of a process. Getting insight into the steps involved enables automation. Otherwise, attempts to automate will only result in frustration.

Once you’ve gained transparency into the current work, now you’re ready to evaluate tools. While this may require more time at the outset, doing this foundational work is key to long term success with automation.

Good automation and good service management go together

To be clear, good automation will not fix bad service management. When you try to use automation to address poor service management issues, all that happens is that you screw up faster – and automatically. And your end-users and customers immediately feel the impact of bad service management.

But when good automation is combined with good service management, watch out. Good service management helps you do more with your resources, helps you get everyone on the same page – both from the technology and the business outcomes perspectives, and helps you deliver that differentiated experience. Good service management ensures that you’re taking a holistic approach to delivering IT products and services. And when you start automation efforts by understanding how value is delivered through IT products and services – you’ll automate the things that both make sense and deliver the most value for both the organization and the user.

Tedder’s Takeaway: Why it matters

Tools alone will not make automation work. Automation is only successful when there is a shared and agreed understanding of the resulting business outcomes, combined with having transparency into how work is being done. Augmenting good service management with good automation delivers the differentiated experience for both the organization and the end-user.

Are your automation efforts stuck? Are you not realizing the benefits of service orchestration? Let Tedder Consulting help! From value stream mapping to process design and improvement, Tedder Consulting can enable automation that is both impactful and delivers a great customer experience. To learn more, schedule a free, 30-minute meeting with Tedder Consulting today!

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ITSM

The New Role of the Service Desk Agent

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What is the future service desk agent? Instead of fearing the future, it’s time to redefine the role.

AI is disrupting almost every part of IT and the service desk is no exception. In fact, service desk agents may be more impacted by AI than any other part of IT.

This has some service desk agents worried about losing their job to a bot. Some of them may even be resistant to incorporating AI into their organization because of this fear.

IT leaders who want to embrace AI must work with their service desk agents to identify opportunities for AI success. Bots are already here and service desk agents should embrace that because bots are ushering in a renaissance for the service desk. The service desk agent role isn’t being outsourced or replaced because of AI. The service desk agent role is being redefined – and this new role is a reason for excitement.

But, before we talk about this new role, let’s first address a common question.

What is the role of the service desk agent?

The service desk agent is typically the first point of contact for IT consumers who need help. Their role generally involves troubleshooting IT consumer issues and providing basic support while escalating complex or more advanced problems to others within IT. Their role involves executing the processes in place to escalate those problems and managing IT consumer expectations and needs. Providing excellent customer service is a critical part of this role.

That description looks good on paper, but what does a service agent actually do?

Historically, service desk agents are performing menial and tedious tasks (like resetting passwords), answering and routing calls and contacts, and strictly following predefined scripts.

But now, bots using AI and machine learning can do those menial tasks service desk agents have historically done – but they can do it faster. Unlike humans, bots are available 24/7, so they’ll never miss a call. Bots will follow those predefined procedures and can perform tedious tasks, like resetting passwords, faster than a human.

So, of course, some service desk agents are looking at this new technology and thinking they can’t compete, or that they are being replaced. But this is where the new opportunity begins.
Finally, with the help of AI, service desk agents can get out from underneath those time-consuming, yet easy-to-solve issues that dominate their days. They are freed from the monotonous tasks that take up their time but don’t utilize their unique skill sets. AI is not going to replace roles. It’s just replacing how low-level tasks are performed.

So what will the service desk agent do when bots and other AI-related technologies are performing those low-level tasks? Here are three opportunities for the future service desk agent.

AI and Automation Experts

You can’t just plug in an automation tool or “turn on AI” and expect it to work perfectly. AI technologies and automation tools only work if they have the proper setup and are managed correctly.
Service desk agents can become AI and automation experts by configuring and managing those technologies. They can be the architects of knowledge bases and automation procedures. Service desk agents can become the go-to experts in helping the organization identify automation opportunities, as well as what needs to be done to implement that automation. We’re just beginning to see how impactful AI and automation can be for organizations and someone will need to continue to lead the organization into the automation age as more technologies are introduced. The service desk agent is perfect for that role.

Problem Solvers

Not all user issues or requests can be addressed by automation or by a bot. There will always be bigger and more complex issues that need to be addressed. With service desk agents no longer bogged down performing menial tasks, they can tackle those bigger user issues that exist within the business.

IT often becomes so busy with small technical requests that they end up applying too many fixes that are only short-term solutions. With bots and AI-enabled technology dealing with those small requests, service desk agents can use their time to create those long-term solutions. They’ll have the bandwidth to innovate and think creatively to identify their solutions. As an added bonus, this work will contribute to the business in larger and more valuable ways and service desk agents will feel more rewarded and appreciated for their work.

IT Ambassadors

Finally, service desk agents will have more opportunities to collaborate with key users. Service desk agents will be able to invest the needed time to understand the business impact of incidents, educate users regarding technology, and identify ways the IT consumer and IT can work together to create a better overall experience.

Good service desk agents will leverage those outstanding soft skills to communicate with empathy and operate from a place of patience. They become ambassadors for the IT organization. If more IT consumers feel seen, heard, and understood by service desk agents, then users will start to see service desk agents as partners, instead of order-takers. This opens the door for IT to be included in bigger conversations around business objects, goals and strategies.

What Should Service Desk Agents Do Now?

The service desk renaissance is here! IT leaders and service desk agents can help usher it in within the organization by championing AI. Service desk agents should aim to become the experts in this new technology, educating themselves on what is available, and then identifying opportunities for using automation and AI technologies within the organization. Upon becoming knowledgeable about AI and how it can support the business, service desk agents should build the business case for implementing AI into the service desk (just be sure you’re avoiding these 5 signs before you start to do so!).

Disruption due to technology is a good thing. It has been happening since the dawn of time and the best way to protect yourself and your team is to embrace it and learn how to work with it. The sooner that service desk agents and IT teams are able to see that AI use will be an asset and not a threat, the sooner your renaissance will begin.

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AI

AI: The Key To a Human Employee Experience

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Employees expect a personalized experience in their use of technology at work. This is due to the proliferation of technology in our personal lives.

Think about how employees interact with technology at home. When they wake up, they ask Alexa about the weather. They use their smartwatch to track their activity and heart rate throughout the day. They program their lamps to turn on automatically every evening and save their most frequented destinations into Siri for easy navigation.

Since employees know the capabilities of technology, they expect to be able to use technology at work in similar ways. In other words, the employee experience has become “consumerized.” Every process must be digitized and personalized.

While most organizations prefer to focus on customer experience, employee experience is just as important, especially in today’s market. It costs organizations to frequently replace team members, both in productivity and cash. Employee Benefit News reports that it costs 33% of an employee’s salary to replace them. Replacing departing employees rarely happens within a 2 week time period and remaining team members are often overloaded with work in the interim, causing them stress and costing the organization productivity.

Additionally, employees are more likely to leave after shorter tenures with a company. Workers are now job-hopping more often, typically staying at a company for less than 2 years. 64% of all adults in the workforce favor job hopping, which is a 22% increase from four years ago according to a survey by Robert Half.

If organizations want to attract high-level candidates and retain their best workers, they have to prioritize employee experience. Luckily, technology, especially AI, can help provide a better employee experience and perhaps, even a more human one.

It sounds counterintuitive – the idea that machines and robots can create a more human, interconnected employee experience. But it’s true. I’ll examine a few ways that AI can create a more human experience for employees.

Let’s start with one of the simplest but most important parts of the employee experience.

Listening to employees is one of the most effective ways leaders can provide a quality employee experience. In fact, according to a 2016 study by the Center for Generational Kinetics, managers can improve employee retention 75% just by listening to and addressing employee concerns. In small organizations, it’s not too difficult to do this. You can gather everyone into the same room and have a conversation about needs and wants. However, for larger organizations, it’s difficult to listen at scale to what employees want without the help of AI.

Standardized employee surveys are helpful for understanding how your organization compares to others in your industry but they rarely provide insight into the individual employee experience. However, AI-enabled surveys can help managers understand the unique needs of each employee. AI-enabled surveys can present qualitative, open-ended questions and can provide deeper learnings by utilizing sentiment analysis. If an employee answers negatively to a specific question, AI can trigger a follow-up question that will provide deeper insight into why that person responded negatively. This gives the manager an opportunity to act on the feedback and follow up with all of the details.

There are several AI-enabled communication analysis tools such as ADP Compass and Humu that can do this on a regular basis. These tools review anonymized emails and Slack conversations and will analyze keywords and word patterns to give managers insights on employee morale.

Other tools can track job performance and employee surveys and create suggestions for managers on when to provide positive recognition.

Another area where employees can use a human element to their employee experience is training and professional development. According to Gallup, professional career growth is a top job priority of 87% of millennials and it’s just as important to 69% of non-millennials.

But employees need more than online courses or quarterly workshops. Everyone learns differently and organizations can provide personalized learning experiences with the help of AI and machine learning.

Machine learning can determine how every individual employee learns and can suggest specific learning methods to managers so the manager can create a personalized training. AI can also be used to gamify learning opportunities that can engage employees. AI will provide managers with results and insights into the performance of their teams and help with planning for future training opportunities.

We’re just at the beginning of an AI-enabled workplace, but leaders should be looking now into how they can tap into the data that AI/ML can provide about their employees. The use of AI provides management with continual opportunities to engage on a personal level in response to continual employee feedback.

Before you start deploying these tools though, HR, the C-suite, and IT must collaborate to learn how best to manage these tools. The introduction of AI may cause some concern among employees and can take on a “big brother” quality if it’s not managed properly.

Enterprise service management best practices such as identifying and mapping value streams, creating collaborative, inter-departmental processes, and determining the proper metrics for success will ensure that your employee engagement technology will deliver the outcomes you want to achieve.

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