automation journey stages

4 steps to starting your automation journey

A blog that clears up the confusion about how to move forward

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September 16, 2022

The automation journey, like any other journey, begins with a single step — but what if you don’t know what that first step should be?

This is a conundrum for many of our customers. They know automation can deliver business-transforming efficiencies. They may even already own some of the latest automation tools, such as  robotic process automation (RPA)  or workflow automation.

Tushar Patwardhan

But when it comes to the automation journey, these organizations don’t know where or how to start. Their tools often exist in operational silos. And just as often, there’s no road map for everyone to share.

That’s unfortunate because the benefits of automation can be powerful. For example, we recently helped a large U.S. technology provider implement a customer-facing chatbot. The company now enjoys significantly fewer customer calls to the help desk, higher levels of customer satisfaction and greater revenue generation.

Sound compelling? Then get your organization’s  automation journey  started on the right foot. Follow these 4 steps:

STEP 1: Work with stakeholders to define your destination

The automation journey should involve more than just IT. All stakeholders, no matter which function they’re in, should be involved in early joint-planning sessions. The purpose of these meetings is to assess your current state and set goals for the future.

To be sure, IT’s role is important. Your CIO and other senior IT executives should set expectations around what’s feasible. They can also help by identifying technical and skills gaps, explain the most serious challenges and present potential solutions.

These meetings are just the beginning. Ongoing communication is a must throughout any automation project’s full duration. This creates a positive feedback loop, one that helps the organization make continual adjustments, fixes and improvements.

It is also important to involve senior business leaders. The CEO and other top executives may not want to dig into the details of automation implementation. But they should share their views and expectations, and you’ll want to secure their support, whether financial or otherwise.

STEP 2: Automate low-hanging fruit to build confidence and momentum

As you create your automation action plan, start with relatively quick and easy projects. The benefits are many. You’ll give your staff a chance to develop their skills. You’ll build confidence among your stakeholders. You’ll build momentum with both your staff and senior leadership. And you’ll convert early naysayers to your side.

The right collateral can help, too. This can include standard solutions, toolsets, use cases and case studies. All can help ease and speed your deployments.

At this early stage, remember the need for training. Your staff may have little or even no experience with modern automation. But if they’re willing to learn, and you’re willing to invest time and money in their training, all will be well.

You’ll also want a plan for scaling up quickly. Once your early automation projects launch, you need to be ready to capitalize on the momentum and growing interest.

STEP 3: Improve effectiveness by shifting to next-gen automation tools

Once you have a stable state of automation, it’s time to pick up the pace. Now you can channel your progress in the direction of continuous improvement.

The most effective way of doing this is with next-generation tools. For example, let’s say you’ve automated a business process. Now you might go deeper with tools for process mining. Or you might explore tools that use artificial intelligence and machine learning to not only react, but also predict.

Whichever next-gen tools you consider, start with a proof-of-concept (PoC). This approach will let you compare tools with speed and relative ease, then pick the one that best fits your organization and its needs.

For especially complex systems, you may also want to take the additional step of running a pilot phase. For example, instead of rolling out a customer-service automation solution across your entire organization, you might prove the value proposition by running a pilot with just one department. This way, you can catch and fix the inevitable glitches on a relatively small scale. Later, when it’s time to roll out the solution across the organization, you’ll feel a lot more confident.

STEP 4: Re-evaluate completed projects for more advanced automation opportunities

In a sense, the automation journey never ends. With technology advancing so quickly, modern solutions have a short lifespan. Even a two- or three-year-old automation project may already look like legacy technology.

The solution? While driving ahead on your automation journey, also keep an eye on the rearview mirror. That means continuously re-evaluating your past projects and asking whether there are new technologies, new tools and new approaches that could boost their efficiency. If so, then add these refreshes to your to-do list.

For example, we often advise customers to migrate RPA bots from on-prem to a cloud solution. They may also have opportunities to upgrade to a more modern RPA toolset coupled with a  cloud approach  that can both lower their costs and boost their workflow efficiency.

Follow these 4 steps to start your organization’s automation journey. Need help? Then  get in touch with DXC . We’ve been helping customers automate their most important workflows for years. We’re ready and able to help you, too.

Learn more about  DXC Data and Analytics  and   DXC Cloud .

About the author

Tushar Patwardhan is the leader for applications service line innovation and automation at DXC Technology, and he’s also in charge of the company’s hyperautomation program. Tushar has deep experience in managing innovation for applications, leading and delivering application services projects for customers across various industries, and directing large business-transformation projects.

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Want to maximize value? Map your automation journey

3-MINUTE READ

May 13, 2022

My experience helping organizations to realize the full benefits of automation shows it should be applied with an enterprise-wide perspective. A key lesson? It’s really important to create structure and prepare for scale if you want to deploy automation technologies faster, embed them across the enterprise and unleash their full potential. 

All too often I see companies jump into their automation journeys with isolated pilots and piecemeal implementations. The result? Longer implementation times, higher costs and an ROI that’s not what was expected. This can leave the business asking whether the return was really worth all the effort. 

As I’ll explain, this is where an automation roadmap can create real value. It allows your organization to steadily grow its intelligent automation capabilities and realize maximum benefits through structured and continuous innovation. 

Know where you are on the map 

One of my favorite quotes comes from Watts S. Humphrey, an icon in software engineering: “If you don't know where you are, a map won’t help.” Before a business sketches out its automation journey map, it first needs to benchmark the current level of automation maturity.  

It must also identify processes that are optimal candidates for automation. Doing so will allow the business to focus its efforts and investments on opportunities with the greatest potential for success. The right candidates will be different for every organization. But applications that present themselves as automation opportunities tend to share some common characteristics including the need for frequent and manual updates, rapid scaling, data extracts, or a high degree of personalization. 

Once opportunities have been identified, it’s time to qualify and evaluate them. In doing this, it’s incredibly important to establish a set of measurements tied to business goals to track progress and results. This can be done by assessing the value that can be achieved from automating a process versus the complexity involved in doing so. 

And remember: value isn’t just about cost savings that you can measure. Organizations could and probably should also consider qualitative measures of success. These can range from increased process performance and improved data quality to stronger governance, better control and greater standardization. 

Create your roadmap 

Once you’ve identified opportunities that your business wants to target, it’s time to lay out the roadmap. This plots the journey from today’s “as-is” automation environment to the “to-be” environment envisioned for some years into the future. 

A good roadmap should suggest the optimal pace, track and deployment strategy for automation. It should take the long-term journey into account too, covering the automation of everything that will need to be automated—at the right time and in a way that will best serve business goals. 

The journey must also include people and talent development at every step along the way. This means mandating development of new IT skills, developing them in a structured manner and creating an “automation-first” mindset throughout the company’s culture. To achieve this, there has to be an emphasis on encouraging and equipping everyone, not just IT, through ongoing learning, “fail fast” prototyping, co-creation and other kinds of continuous improvement and innovation. 

Follow a structured approach

I advise organizations to sketch out their roadmap on two levels: (1) progress to business results and (2) progress to greater automation maturity. For the latter, it’s best to plan this as a series of steps from where the organization is on the maturity index today to higher levels of automation maturity ahead.

Where progress to business results is concerned, you should view the journey in three phases: 

• Phase 1: Establish.  Make it a priority to assess the automation potential of processes and identify pilots for specific applications across the enterprise. 
• Phase 2: Scale.  Next, develop, deploy and scale solutions. Scaling automation capabilities demands a holistic strategy that cuts across all types of talent segments and skills. Automation should never be solely the responsibility of a small team. It has to be everyone's. That’s why, to build automation capabilities at scale, enterprise-wide investment in talent and skills is so crucial. 
• Phase 3: Operate.  My experience shows that long-term automation should be a deliberate, well thought-out process. Decision-makers at every level need to be involved in process rationalization. In this phase, a business should be extending automation coverage to maximize value realization. It’s also vital to fine-tune the ongoing automation strategy based on what happened in earlier phases. 

Scale automation for real advantage 

Keep in mind that the automation journey isn’t a straight line. And it doesn’t end once your organization has worked its way through the three phases I’ve outlined here. Driving automation at speed and scale should be a continuously evolving journey, giving the enterprise the flexibility to adapt, improve, innovate and realign along the way. Doing this through a structured roadmap with the right measurements will mean the organization is well-positioned to rapidly scale automation and leverage its full power for competitive advantage. 

 In our book "The Automation Advantage", we explore all the facets of a successful intelligent automation implementation—from strategy to sustainable transformation and measurable business benefits. Find out more at  accenture.com/automationadvantage . 

Rajendra Prasad (RP) is co-author with Accenture’s Dr. Bhaskar Ghosh and Gayathri Pallail of “The Automation Advantage” from McGraw Hill.

Rajendra Prasad (RP)

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The Four Steps in a Successful Process Automation Journey

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As part of Solutions Review’s Contributed Content Series —a collection of articles written by industry thought leaders in maturing software categories—Bernd Ruecker, the Co-Founder and Chief Technologist at Camunda , identifies the four steps needed to develop and maintain a successful process automation journey.

While surveys show more than nine in ten enterprises already deploy process automation and consider it a mission-critical force in their operations, many are only scratching the surface when it comes to generating value from the practice. Most are only automating single tasks. Few are taking full advantage of all that automation has to offer.  

To either get started with automation completely or improve the end-to-end perspective, you generally need to follow four steps: Discover, Design, Automate, and Improve your processes—and then repeat.  

Let’s start with the obvious. Before implementing automated processes, you must know what you’re working with. What processes do you perform, and how are they working? What tasks need to be done in what sequence? Which people are involved, and what systems are used? You need to discover and understand the status quo first.  

Part of the discovery process is to mobilize your audience to gather all the information you can. Identify key stakeholders and set up meetings. Pull together a list of critical tasks and determine at a holistic level what’s going right, what’s going wrong, and what appears to be the most impactful changes you can make through process improvement.  

However, the discovery process should go beyond collecting anecdotes. Ideally, you can gather data to back up the assumptions you make. Real-life data can also point you to insights nobody in your organization had. One way to use existing data to gather process insights is through process mining.   Process mining tools help users better understand how a process is automated within core systems like ERP or CRM. Typically, this requires loading and analyzing log files from these and other systems to discover correlations and process flows. Process mining tools can discover a process model and display it graphically. They also can turn up data that can identify bottlenecks or optimization opportunities.  

Once you’ve gathered the data and developed some conceptual plans, you can start on the design. A critical step is getting business and IT stakeholders to communicate as one. That’s not always easy . End-to-end business processes are complex and often span people from various departments, systems, and devices. The complexity of the process flow and the diversity of technology running in the background can make it difficult for stakeholders to visualize exactly what’s happening and communicate with each other as they work.  

Modeling languages ​​like Business Process Model and Notation (BPMN) can help. They visually represent process flows and dependencies through a flowchart, and a workflow engine can execute these BPMN models directly. This means the visual diagram is also a piece of program code simultaneously.  

Visually representing a complex process helps to break down communication barriers between different roles to better discuss what’s needed from the business and what’s technically feasible. It allows teams to rethink processes in general and agree on a chosen design before writing code. Also, later in production, generated audit data connected directly to the process model helps teams to iteratively improve a process. In this regard, BPMN can help teams take a more agile approach to solving problems. You can quickly create a minimum viable product (MVP) solution that addresses the issue you’re trying to fix. From there, you can make data-backed improvements iteratively and deploy the newest version during the next development cycle.   

Designing a process forces teams to take a closer look at it. Bringing all the stakeholders involved together on one table offers the chance to improve processes in general and to do a better job creating processes from scratch and to improve processes in general. Combining the visual nature of BPMN with a user-friendly way to model processes speeds up the creation of innovative solutions.  

The next stage is where an organization starts actually automating tasks and processes. This is where process orchestration comes in. It often happens that only a task is automated locally with a single endpoint. But real business processes follow a much more complex logic, which isn’t easy to automate. Process orchestration software connects and coordinates the endpoints (systems, people, devices) of all business process tasks and allows for end-to-end automation.  

The workflow language is of importance once again. The above-mentioned BPMN, for example, can express many common workflow patterns out of the box, bringing a lot of clarity and understanding to every process detail. ​​These patterns handle complex business process logic, such as executing process flows in parallel, message correlation, escalating events, or dealing with a fatal error.  

One good example is dynamic parallel execution. BPMN makes it easy to coordinate many concurrent tasks. Imagine a retail setting where customers purchase multiple items in an online store. As the customer orders, the back-end system checks product availability and updates information in other systems, including finance, logistics, and CRM, for every ordered item in parallel.  

In a second pattern—message correlation and abortion—BPMN sorts out confusion in a process where someone is canceling a task. Picture a situation where a customer cancels their order via the web portal. Interrupting a workflow with many tasks spread across multiple distributed systems can be challenging at scale, especially as the handling might need to be different depending on the order’s current status. This gets much easier to master using BPMN and a workflow engine.  

In a third example, with the help of BPMN and a workflow engine, a process instance can be escalated if it’s not completed in an agreed-upon period. Essentially, if a bill isn’t paid on time, a process can be triggered to remind the customer with an automated email. Using BPMN, the process is coordinated across the firm’s business email and accounting system.  

The work doesn’t end once a process is automated end-to-end. The automated functions are an excellent basis for continuous improvements. For example, it can make processes more efficient, less taxing, and more cost-effective. So, there should be a mechanism in the process improvement loop where the organization learns how to perform processes better.  

This needs to incorporate live data. How many transactions went through smoothly? What were typical cycle times? Are there outliers, and what do they have in common? Analytical tools and dashboards can help designers spot bottlenecks in the system and recommend ongoing improvements in process flows.  

Process automation is a mission-critical function for organizations across geographies and industries. Those who have put off implementing end-to-end automation plans are missing out on opportunities for ongoing improvement. Starting slow, planning ahead, and executing a strategy based on four basic steps—Discover, Design, Automate, and Improve—can get them on track for success.    

This article was written by Bernd Ruecker on September 1, 2023

Bernd Ruecker

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Co-Founder and Chief Technologist

Bernd Ruecker is Chief Technologist and co-founder at Camunda, an open-source software company reinventing process automation. He is a software developer who has been innovating process automation deployed in highly scalable and agile environments of industry leaders such as T-Mobile, Lufthansa, ING, and Atlassian. Bernd has contributed to various open-source workflow engines for over 15 years and is the author of "Practical Process Automation" and co-author of "Real-Life BPMN." Additionally, he is a regular speaker at conferences worldwide and a frequent contributor to several technology publications on new process automation paradigms that fit into modern architectures around distributed systems, microservices, domain-driven design, event-driven architecture, and reactive systems.

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The Automation Journey

Posted by Michelle Millar in: Real Estate , robotic process automation , Automation , RPA

Do the difficult things while they are easy and do the great things while they are small. A journey of a thousand miles must begin with a single step. - Lao Tzu, Chinese philosopher

Automation has been proven to work and is here to stay. Whether we’re talking about Robotic Process Automation or Artificial Intelligence, attended or unattended automation, or any of the combinations thereof, Real Estate companies have welcomed the transformative potential of this new technology. However, the industry is challenged by questions of where to start, what processes to automate, how to prove value, and how to scale from successful pilot to enterprise adoption.

At Open Box Software, we recognize that automation is not a project, but a journey. Let’s look at the steps your company will take on the road to automation success, and the questions to ask along the way.

Step 1: Evaluate

Choosing the right automation platform or partner is going to be key to your success; but knowing what your challenges and objectives are from the start will help you to make the right choice.

Many companies will set out to do the market research on automation and be quickly lost in the fog of available options. The questions roll in thick and fast:

• What’s the difference between attended and unattended automation?
• Are Robotic Process Automation (RPA), Intelligent Process Automation (IPA) and Artificial Intelligence (AI) all the same thing?
• What are controllers and why do I need them?
• Do I need to be concerned about data privacy with cloud hosted tools?
• What are my operating costs going to be?

In our experience, the challenge of tool selection can be simplified by preparing well. First and foremost, make sure you’ve addressed the most important question of all: what does success look like for us?

We recognize that automation is not a project, but a journey

To answer this, you will need the right people in the driving seat from the start. Champions or evangelists should come from both business and IT, and have the insight and authority to ensure the journey is in line with the organization’s strategic direction. Consulting with or hiring people who have experience in the field is also advisable. Why spend months re-treading ground that someone else has already walked?

Many companies will engage in a Proof of Concept test during this phase, to decide between shortlisted tools or to validate tool selection. Before you do so, be assured that the question of whether automation works has already been answered by dozens of case studies. Design your tests to find out if the tool will work in your environment, for your processes, and will meet your objectives.

Step 2: Evangelize

Education and promotion only go so far in bringing the organisation as a whole on board with your automation strategy. Evidence of benefit will speak louder than words.

Running a pilot to production with a single process is often a critical first step in convincing process owners, budget owners and other key stakeholders to invest their time and money in a long-term program of change. Selecting the right process to pilot is critical:

• The process should be well documented, mature and stable.
• There should be few exceptions to the process, and few dependencies.
• The input to the process should be well-structured, machine readable, and require minimal validation.
• The process owners and subject matter experts should be fully committed.
• The expected benefits should be clearly measurable.

When it comes to proof of benefit in a pilot project, cost saving is often seen as the easiest to prove and motivate at this stage. However, automating a single process is very unlikely to dramatically impact headcount in most Real Estate teams. Benefit at this scale is more likely to be found in freeing up time for people to engage in more value-add work, whether it be through direct time saving or reduction of errors and risk. Being clear about how you plan to calculate, measure and present the benefit is therefore key in pilot process selection.

Be careful not to become too focused on the delivery objectives of the pilot and lose sight of other factors that can impact your pilot’s success. Change management, training, communication and expectation management are vital to ensuring the new process fits in seamlessly with minimal disruption to the team. Losing the faith of the process owners through a perception that the automation has cost more of their time than it has saved is the surest way to end your automation journey when it has barely begun.

Step 3: Establish

So, your pilot was successful, and everyone is clamouring for you to automate their process next; how do you make the transition from innovation project to business as usual?

The trickiest stage of your journey will be preparing to scale your automation program. During this phase you will have many challenges to overcome:

• Growing from a few automation champions into an established, expert automation program team with governance and oversight.
• Creating an opportunity pipeline to allow you to gather, qualify and develop automation opportunities while keeping stakeholders informed and engaged.
• Establishing best practices for everything from project documentation to deployment management.
• Creating an operational support team to monitor and maintain the automations that have been delivered into production.
• Scaling your automation environment to meet the increased demand.

These challenges are by no means unique to automation, but as always, the devil is in the details. Because automation is often seen as a process rather than an application, it can easily fall through the cracks of an otherwise robust support ecosystem. For example, if you have automated data input into a key system, is the system’s upgrade schedule taking the automation into account? Is the automation team tied into your change advisory board? Does your helpdesk know how to distinguish between an automation issue and an application issue?

Automation does not exist in a vacuum

As you grow your program, the nature of the projects you select for automation will also change. The pilot is unlikely to have covered every aspect of what you want to test or prove, so the next tranche of projects must be carefully selected to fill in the gaps. Objectives should include automating against different key applications in your environment, demonstrating different types of process (e.g. data collation vs. data input) and demonstrating benefits that are not as easily measured (e.g. compliance, quality and risk reduction).

This is the time when expertise and experience are invaluable. Knowing where the potholes are, and how to avoid them, and knowing what other companies have done to successfully navigate this stretch of road can be the difference between reaching your destination or wandering aimlessly in the wilderness of failure to scale.

Step 4: Expand

The destination of your journey is automation as an established enterprise tool, and an enabler of digital transformation.

Automation is not simply a case of doing what you to today, only faster. It is an opportunity to tackle important organisational objectives that other technologies may have failed to address. Expanding your scope from straightforward process automation to automated solutions is the key to achieving the greatest value from the technology. When you begin to see automation as an enabler, great things become possible:

• Using the process focus of automation projects to encourage a transformational culture that balances people, process and technology.
• Supporting your integration strategies by providing more options for interfacing with legacy or external systems.
• Supporting your data strategies by helping to cleanse, transform and validate data efficiently and consistently.
• Augmenting knowledge workers with Artificial Intelligence tools that fit seamlessly into their workflows.
• Creating scalable processes that can flex up and down to respond to the varying volume of demands throughout the year, to market fluctuations, and to rapid growth.

Automation does not exist in a vacuum. Your approach to implementation should be a holistic one that aligns with your broader business and technology roadmaps. It should also be able to move quickly to capitalize on and respond to changes in the technologies, the market and the organisation. To this end, establishment of an automation Center of Excellence that brings together a multi-disciplinary team of stakeholders, automation experts and business users will ensure the company is getting the most out of its investment.

Take the First Step

If you are looking to augment and free your resources, redefine your standards in quality and efficiency, and transform the way your business operates, automation has the potential to deliver. Embark on your automation journey with a map, a compass, and some experienced travelling companions, and you’re sure to reach your destination!

To find out more about how Open Box Software can help you on your journey, take a look at our Automation Consulting and Robotic Process Automation  services, or email us to get started.

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Open Box Software awarded MRI Trusted Client Advisor 2022

For over 20 years Open Box has had the pleasure of working with prominent businesses within the commercial Real Estate industry, providing services across the application solutions spectrum. Our success is a combination of our talented Open Boxers (as we call ourselves) working with our incredible clients and partners. Our philosophy is there is always a better way, and we are committed to finding and delivering that in everything we do.

For over 20 years Open Box has had the pleasure of working with prominent businesses within the commercial Real Estate industry.

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Open Box Software, a leading global provider of software and services to the Real Estate industry, announced today the appointment of the company’s North American MD, to Chief Executive Officer.

Open Box Software, a leading global provider of software and services to the Real Estate industry, appoints the company's North American MD, Brendan Canny, to Chief Executive Officer.

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RPA Journey Phases – How to Build a Program from Scratch

Organizations wanting to start an rpa program within their organization may not know where to begin. we’ll cover the phases of the rpa journey, and what you’ll need to get started., rpa journey phases – “going 0 to 60”.

M any organizations today recognize the need and urgency to implement Robotic Process Automation (RPA) technology within their repetitive processes. However, these organizations, especially those less tech-savvy, may find themselves at a stand still and unable to get their RPA journey up and running.

Contrary to popular belief, businesses just starting out don’t need to have software automation experience to begin their journey, and they have the capabilities to start a program from scratch by following a set of phase-related guidelines. Recently, RPA Today hosted a webinar outlining these steps, from the expectation phase to the build and implementation phase, which we’ll dive into here.

Setting the Right Expectations

If you’re an organization at the beginning stages of a RPA implementation, you’re looking to digitally transform your processes. This involves placing repetitive, paper-based, mundane tasks in the hands of software bots, leaving innovative and thought-provoking initiatives to the human workforce. However, it’s in this stage where RPA beginners might find themselves making the following mistakes, which you should actively avoid:

• Thinking RPA is a fix-all solution – RPA cannot and should not be viewed as an “easy button” solution. Further, RPA will not fix an already broken process just because it’s implemented. While RPA can automate end-to-end processes through hyperautomation , it’s crucial to gain a developed understanding of the process at hand before the implementation begins, and where/how RPA as a tool will come into play.

Forgetting about other potential technologies at play – RPA will help you understand your processes better than you ever have before, even if the said process is going to avoid human intervention in the coming future. However, don’t forget about other technologies that can also come into play for the non-workflow portions of this process, such as handling unstructured data, and enhancing data analytics practices.

Involve the Right People

Of all the RPA journey phases, implementing your Center of Excellence (CoE) is one of the most important components. This involves encompassing the right champions around your newly implemented RPA process, which should involve both IT and business resources. When implementing your CoE, here are some core fundamentals to remember:

Build an opportunity assessment framework – Build a framework of your business processes, and note those you wish to automate.

Engage your business partners – Engage your partners on a frequent basis during the development process, and ensure every member understands their role.

Measure the ROI – When evaluating what processes need to be automated, question the investment through and through.

Define What Success Looks Like

Without first defining what a successful RPA implementation looks like, the automation journey will feel more like an aimless drift at sea. While success value will vary, you can define it through two aspects – business value and technical viability .

How are your end-goal business successes going to affect your IT practices going forward and vice versa? What will the unison be like moving forward within your CoE, primarily between your business development champions and your IT support staff? These are important aspects to consider.

Missed the webinar? Watch the recording!

Select the Right RPA Software

Perhaps one of the most crucial (and long-term impactful) phases of the RPA journey is to select the right software. While many RPA providers can offer the same benefits, a distinction needs to be made on user experience, support, and functionality. Here are some examples of RPA providers:

UiPath : A leading provider of Robotic Process Automation technology for both business and IT. UiPath places an especially heavy focus on the user experience within their developer studio through their drag-and-drop platform functionality.

Microsoft Power Automate :  Power Automate allows users to construct time-saving workflows into everything from individual tasks to large-scale systems with seamless integration using hundreds of prebuilt connectors.

Select the Right Process

After you’ve set the right expectations, built your internal CoE, defined success stipulations, and selected an RPA provider, you’re ready for the final step before a full-scale integration. When selecting a candidate for automation, it’s important to start with a low-risk process , which will have little to no drawbacks should the initial integration not go as planned. This is why Smartbridge always recommends the creation of a Demand Management Playbook to ensure the right process is chosen next for automation purposes.

Further, expect to fail fast through this implementation, which goes back to setting the right expectations. Layout the business value and examine the process details throughout this phase of your journey. As said before, you will get to know your process more intimately during and after  an RPA implementation. Limit RPA application use and interference at first to allow for enough assessment time.

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Explore more insights and expertise at smartbridge.com/automation, keep reading: the different ways to learn rpa, there’s more to explore at smartbridge.com.

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Information Age

Insight and Analysis for the CTO

Understanding your automation journey

Ravi Dirckze, technical product manager at HelpSystems, explains the need for businesses to understand their automation journey

Many organisations are seeking to tap into the perceived benefits of automation, such as higher production rates, increased productivity and more efficient use of materials. A report by McKinsey found that two-thirds of organisations were piloting the automation of business processes in one or more business units or functions.

But while automation is an attractive option, businesses need to make the right decisions over their automation strategy and how they use the capability of robotic process automation ( RPA ) to streamline manual processes and reduce the burden on human employees.

To gain a greater understanding of their automation journey, businesses need insight into four essential areas: the emerging RPA landscape, the existing need for automation, considerations for scaling automation over time and the role intelligence will play in their approach.

How automation is powering work transformation: five examples Robert Frati, chief sales and success officer at Slack, provides five examples of how automation is transforming work for the better. Read here

The emerging RPA landscape

RPA is changing the nature of business today. The digital shift to RPA is already firmly embedded across organisations and industries, from banking and finance to healthcare and insurance to human resources and accounts payable.

There are three categories of automation providers. Large enterprise providers offer solutions to tackle processes that have a significant impact across the organisation. Their solutions and services are usually implemented by consultants or well-trained users, the solutions are difficult to scale on their own, require a more consultative approach and typically come with a higher price tag. The benefits of enterprise-wide automation include a wide range of essential RPA features and advanced capabilities like cognitive and AI functionality.

At the other end of the spectrum are RPA vendors offering impressive low-code or no-code automation focusing primarily on individual or personal productivity. These tools offer solid, single user automation that is intuitive and straightforward and can boost efficiency at the individual level. But these solutions tend not to focus on automating process complexity and can lack more sophisticated automation functionality.

In the middle are solutions that offer powerful UI with back-end automation capabilities and more flexibility to scale across people, teams and processes. Providers in this category enable individuals and teams to streamline processes and automate end-to-end workflows across the business.

Current needs for automation

In order to achieve shorter-term automation goals, businesses need to evaluate their existing automation needs and ask a few key questions. Are they seeking to automate mundane tasks to increase personal productivity, such as processing emails, setting up notifications or organising files? Personal productivity automation is employee-driven and used to tackle multiple tasks for productivity gains at the individual level.

Are they seeking to streamline business processes, such as processing a high volume of invoices or moving data from one system to another? Business process automation (BPA) is also employee-driven but it streamlines business processes to deliver efficiencies and productivity gains across users and departments.

Automation might also be an ongoing project, often referred to as an automation Centre of Excellence (CoE), which focuses on intricate, enterprise-wide automation and orchestration. CoE-driven automation is fairly complicated and has a significant influence on automating connected processes.

Each of these use cases requires a unique set of characteristics. By understanding its automation goals and strategies, a business can choose the place to start that best meets its automation needs.

The hottest hyper-automation trends disrupting business today This article will explore the most prominent hyper-automation trends disrupting business in today’s post-pandemic landscape. Read here

The cost of scaling automation in the future

It is essential that organisations know the cost of scaling their automation solution. No business wants to be hit by unforeseen price increases as its automation strategy progresses. Unexpected costs diminish ROI as businesses scale, making it difficult to justify the additional spending for expanding their automation initiatives.

With some RPA providers, it may not be cost effective to scale because they expect customers to pay extra for add-ons, employ linear pricing structures or charge for attended versus unattended bots. Some RPA solutions may not have the usability to support scaling. They might require intensive consultation services, high-level programming support or be narrowly focused on individual productivity for single users.

The role of intelligent automation

RPA is well-suited for automating routine tasks and as artificial intelligence, machine learning, and predictive analytics are introduced and refined, automation will become more sophisticated, augmenting human capabilities and enhancing decision making across organisations.

Intelligent automation , or intelligent process automation, integrates intelligence in lockstep with RPA to broaden the range of processes that can be automated. It considers the entire lifecycle of an automated process across tools and functions and with regard to the overall complexity.

Intelligent automation is already happening. According to Deloitte , 47% of organisations have combined RPA and AI as part of their intelligent automation strategy. Augmenting automation with intelligence will also lead to greater cost savings for organisations. Gartner estimates companies that leverage automation and intelligence will reduce operational costs by nearly 30% by 2024 .

RPA can transform how work gets done

As more and more organisations rely on automation to increase productivity, reduce costs, eliminate errors and relieve employees, RPA will continue to address these major challenges with a scalable, digital workforce.

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The RPA Lifecycle: Process Automation from End-to-End

Robotic process automation (RPA) is an unstoppable digitization trend, but its complexity is frequently underestimated.  In this article, you’ll learn what RPA is and can do, why many RPA projects fail, and how MuleSoft RPA can safely guide your automation initiatives to success throughout the RPA lifecycle.

Automation now: jobs for bots 

It seems like magic and the ultimate answer to cross-industry challenges such as innovation pressure and skills shortages: RPA is a software technology that automates repetitive tasks in a cost-efficient way. RPA bots perform routine digital tasks flawlessly, fast, and steadily. They compare prices, answer standard inquiries, prepare payrolls or generate passwords. 

All these tedious and error-prone tasks that cost companies tons of time and money while overtaxing and tiring qualified employees in all departments are a thing of the past with RPA. About one-third of all tasks can be automated in at least 60% of jobs, and you can save an average 20% full-time role equivalents after fewer than a year of RPA operation . RPA offers some serious potential.

Anyone can RPA: the no-code hype

RPA bots imitate the activities of human users when operating digital systems: they open and close programs, identify content on the screen, and perform mouse clicks or keyboard entries. In doing so, they follow a predefined script that automates the previously manual workflow. 

To create these scripts, modern RPA tools typically offer a graphical no-code approach: the required user activities are available as predefined building blocks that are combined step by step into automation workflows using simple drag and drop actions. 

This intuitive approach also enables business users to automate their workflows on their own, opening up vast digitization capabilities beyond the limited resources of IT departments. By 2023, major companies will have four times as many citizen developers as professional software engineers.

Fail to scale: common RPA mistakes

Thus, more companies are waving the RPA magic wand, expecting significantly greater efficiency and lower costs in no time at all. But the miracle frequently does not materialize: 30–50% of all RPA initiatives fail , which is quite a disillusioning finding in the wake of the initial hype on the market. And ironically, this is due to the user-friendly approach most automation tools offer.

Ambitious departments often start the topic of RPA on their own initiative. Well, why not? After all, business employees know their workflows best and can actually automate some tasks themselves thanks to no-code. Nevertheless, the results are poor and the problems are pre-programmed. There are 3 reasons why RPA projects fail:

1. Having a business-only mindset 

If business departments run RPA as shadow IT, it will lack everything that a professional software project requires: a proper and audit-proof documentation, development and delivery standards, a solid infrastructure, plus ongoing maintenance and monitoring of the RPA bot operations. On top of that, the digital coworkers access and operate in production systems, so the adherence to compliance and security policies is particularly critical. This all sums up to: Don’t do RPA without the IT department.

2. Not having a plan at all 

Quickly automating tasks somehow and somewhere is possible, but not very effective. Like any digital project, RPA demands a strategy and a roadmap that is aligned with the company’s overarching goals. Without in-depth planning and analysis, it is highly likely that unprofitable processes will be automated with poor outcomes. As a result, your RPA ventures will hardly ever win the commitment of the top management level and will not be supported in the long term.

3. Losing control 

Who is developing the bots? Which tasks are automated? Where and how frequent do the automations run? What business results are they driving? If you can’t answer these questions, you lack overall visibility, centralized control, and evaluation of success. Without these governance aspects, your automation journey becomes a blind ride.

Key success drivers for RPA 

The complexity of process automation should never be underestimated. RPA is more than simple task automation. If companies want to fully tap the potential and profitability of RPA, they must plan process automation as an organization-wide IT and business project, set up compulsory project standards, and centrally and securely manage the collaboration of all players. Only then can the impact of RPA unfold on a large scale. These points are critical for RPA success:

Selecting the right processes 

Not every process can be automated – and not every process automation pays off. So you need to comprehensively examine which processes can be automated from a technical point of view as well as assessing the economic viability of the automation. 

Orchestrating RPA teams

An RPA project requires developers to build and test the RPA bots, but it also needs process owners from the business to identify potential RPA candidates and fill the RPA pipeline, IT administrators to provide the infrastructure and monitor ongoing bot operation, and project managers to control the project flow and keep an eye on costs. All the relevant roles, skills and privileges must be clearly assigned to ensure efficient and secure collaboration of all players involved.

Steering RPA centrally while empowering users across the board

Companies that successfully adopt RPA manage and control their entire automation landscape centrally while ensuring the highest compliance and security standards, as with any IT project. At the same time, they empower business users across all divisions to actively engage in process automation, thus unlocking additional digitization capacities and alleviating the strain on the IT department. They achieve this balance between RPA governance and RPA enablement by taking a platform-based approach and using suitable no-code technologies.

Standardize bot development

An RPA bot is ultimately a piece of software that is designed to reliably meet specific business requirements. Thus, the project phases of the Software Development Lifecycle apply perfectly to the development of high-quality RPA bots. A standardized RPA lifecycle simplifies and directs the course of RPA projects and the teamwork of the players involved while ensuring that RPA delivers optimum business value.

MuleSoft RPA: Manage process automation end-to-end

MuleSoft RPA is a modern enterprise platform for process automation, providing user-friendly no-code tools and powerful robotics technology for successful process automation. Every automation project passes the entire RPA lifecycle. Here’s the key benefits:

• Central management and full control of the company-wide RPA landscape
• Intuitive and systematic user guidance through all phases of an RPA project
• High profitability and financial transparency
• Efficient collaboration of all players and RPA stakeholders
• Standardized, high-quality bot development
• Stable, secure RPA operation

You want to know in detail how the magic works? Join the journey through the RPA lifecycle and the different stages of process automation!

RPA lifecycle phase 1: Evaluation  

Assess and prioritize processes.

All RPA projects are managed centrally via the cloud-based server RPA Manager . This is where the automation journey begins: In the first phase of the RPA lifecycle, persons with in-depth process knowledge, such as business unit managers or process managers, check whether potential process candidates are actually suitable for automation.

Release processes and prepare them for automation

Reliable project control through the RPA lifecycle includes release steps for each next project phase. After successful evaluation, the process owner releases the process for automation and hands it over to the responsible project manager, who prepares all the settings required for RPA development, puts together the RPA team and performs a cost-benefit analysis.

Manage RPA teams, roles, and privileges

RPA projects involve many players performing different tasks, ranging from process analysts to bot developers to IT administrators. A fine-grained and flexible role and privilege concept specifies and manages responsibilities and access permissions, facilitating a controlled and cross-phase collaboration of all people involved in the development process.

Calculate automation costs and RPA ROI

RPA project management also includes business analyses that reveal the profitability of planned and ongoing process automations. This enables RPA project managers to easily overview the monetary success of automation initiatives and report this information to relevant stakeholders.

RPA lifecycle phase 2: Design 

Model processes.

In the design phase of the RPA Lifecycle, the business process is mapped as a flowchart in the standard graphical language Business Process Model Notation (BPMN) . The BPMN flowchart documents the sequential steps of the high-level process and serves as the “skeleton” for the subsequent RPA implementation. Besides bot tasks, BPMNs can include manual user tasks for attended bot operations: here, bot and human activities alternate and complement during the course of the process.

Record RPA requirements

Once the business process is modeled in BPMN, it is filled with detailed information for the development of the RPA bots. The RPA Recorder simply records the user activities to be automated, e.g. the RPA requirements. This easy to use recording feature significantly accelerates bot development and was a key factor in MuleSoft’s positioning as a Visionary in the Gartner Magic Quadrant for RPA 2022.

RPA Lifecycle Phase III: Build  

Implement rpa workflows.

Now it’s the bot developers’ turn: For each bot task in the BPMN process, a robust RPA workflow is to be created based on the pre-recorded RPA requirements. MuleSoft RPA Builder is an intuitive and powerful low-code tool with a wide range of automatable user activities. These are available via a comprehensive action step toolbox and include 

• screen recognition technologies such as image and pattern search
• intelligent OCR for reading text from application interfaces, images, and PDFs
• typical user actions such as mouse clicks or keystrokes 

along with numerous other RPA building blocks that make it easy to automate common office applications and to map the logic of digital business workflows. Local functional testing for each step and bot workflow ensures high-quality bot development.

RPA lifecycle phase 4: Test

Assure rpa quality before going live.

Like any software, the fully implemented process automation is tested before it is accepted for operation. For this purpose, the RPA process runs on bot instances through various test plans that cover possible “real-world scenarios”. The test execution can be tracked in real time.In case of failed test runs, the process moves back to the build phase, and developers can use an automatically generated analysis package for troubleshooting (see RPA alerting and troubleshooting).

RPA lifecycle phase 5: Production

Plan and start process automation.

Just ahead to go: Now is the time to specify when and how often the process automation should be executed – and on which bot instances it should run.

RPA Lifecycle Phase VI: Operation  

Monitor bot operations.

Your process automation is running – the RPA bots are at work! You can watch the operation of individual bots via livestream or analyze all running process automations at a glance using dashboards.

RPA alerting and troubleshooting

Stable RPA operation requires reliable recovery tools. If RPA bots run into errors, the alerting module immediately informs the responsible IT administrators – and a detailed analysis package helps RPA developers to quickly and precisely fix the issue.

RPA lifecycle: Your automation journey starts now

Now that you know what the RPA lifecycle looks like, are you ready to start your own automation journey? Our free training guides you step by step with exercises and video tutorials through all RPA project steps, so get started with MuleSoft RPA .

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Red Hat Services Journey: Automation Adoption

Introduction.

Organizations adopt automation to eliminate the time and errors associated with manual configuration processes. The promise of enterprise-wide IT and business automation is appealing, but without well-defined tooling and streamlined processes, benefits are quickly eroded by constant change and lack of clarity. A successful automation strategy with predictable workflows and rich auditability requires accountability, governance, security, and standards from the onset.

Automation is attractive because it provides scalability and agility, accelerating outcomes with consistent and repeatable processes that support the evolution of DevOps culture . According to IDC, organizations unable to add personnel but needing to keep up with increasing operational complexity will need to rely on system management software, including automation. However, getting an entire organization to adopt automation is complex. Businesses need a sustainable automation strategy and must create modern IT with new approaches to process and collaboration.

Red Hat® Services Journey: Automation Adoption provides a framework for managing an organization-wide automation adoption journey. Red Hat Consulting will work with you on all stages of automation adoption from introducing technology to aligning teams on standard practices to orchestrating powerful workflows that fit with your evolving business objectives.

.b284a514-74e3-4814-977d-399a0e82e3fa { fill: #e00; } Icon-Red_Hat-Media_and_documents-Quotemark_Open-B-Red-RGB Red Hat Consulting provided a powerful mix of expertise, bestpractice guidance, and mentoring to help us design a defined DevOps process, with regular reviews to help us continue maturing our approach. Jufri Fan Senior Director, Head IT Solution Architect, BTPN

Figure 1. Stages of automation adoption.

During an automation adoption journey, Red Hat Consulting experts help customers define a unified strategy focused on their unique business needs. Consultants then introduce and scale technology, process, and culture to support business with automation. Our experts will guide you across the phases shown in Figure 1.

Discuss goals, solution approaches, and next steps to:

• Identify use cases, challenges, and potential issues. 
• Identify viable approaches and technologies, necessary participants, and desired outcomes.
• Outline current state, target state, opportunities for change, and operational benefits.

Develop an overarching plan that delivers:

• Analysis of current-state automation approaches.
• Organizational practices for the program.
• Strategy across people, processes, and technologies.
• Architecture definition that addresses target environments.

Work alongside Red Hat subject matter experts, who will help your team:

• Build a collaborative culture, determine responsibilities, foster new skills, and accelerate innovation.
• Adapt existing processes and tooling to realize the full benefits of automation.
• Deliver security-focused, flexible infrastructure and practices to automate and orchestrate efficient workflows that support business strategy.

Expand, promote, and optimize adoption through:

• Process evolution and toolset integration to increase efficiency.
• Reusing consumable automation from previous increments.
• Using internal sponsors and community support models to socialize techniques.
• Methodology and application governance to promote sustainable growth.

A comprehensive journey to automation adoption

Red Hat Consulting helps customers evolve capabilities in four critical areas. These areas comprise a strong, flexible framework for growing automation within teams, across organizations, and in support of changing business requirements. 

• Standardization of content, systems, and security practices
• Task automation to deliver repeatable and scalable results quickly
• Workflow orchestration to integrate disparate tasks and management systems for end-to-end process automation
• Process transformation to realign approach to self-service catalogs and business-aligned workflows 

The Red Hat Services difference

Working directly with Red Hat engineering and support, Red Hat Consulting teams bring advanced skills to deliver solutions using Red Hat products and open standards. With exclusive insight into upcoming features and product roadmaps, Red Hat is better positioned to align teams, streamline processes, and help enterprise systems and applications work together using open source and proprietary technologies.

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Talk with a Red Hat consultant  to learn more about automating your enterprise.

Red Hat case study. “ BTPN launches Innovative Banking Services Faster with Red Hat ,” Apr. 2018.

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.css-14us1xl{box-sizing:border-box;margin:0;min-width:0;white-space:pre-wrap;} What is AI Customer Journey and How to Use it

Explore everything about the AI customer journey — its definition, evolution, importance, the difference between traditional customer journeys and optimization strategies.

Share this Topic

• What is AI customer journey?  

Evolution of AI in customer journey  

Why is ai important to the customer journey  , ai customer journey vs. traditional customer journey   , 7 stages of ai customer journey  , 9 ways to optimize ai customer journey for businesses   , .css-1qemmfx{font-weight:700;} what is ai customer journey  .

The AI customer journey represents the seamless integration of artificial intelligence (AI) into every touchpoint of a customer's interaction with a business. Encompassing all stages, from initial awareness to post-purchase support, it leverages AI to elevate customer experiences, fostering heightened satisfaction, loyalty and revenue. In an era where the distinction between pain points and friction becomes increasingly indistinct, the strategic deployment of AI-driven best practices — automation, predictive analytics and sentiment analysis—paves the way for crafting an intelligent, adaptive and fulfilling AI customer journey.  

Advent of AI in customer service: With this, customer journeys became more agile and responsive, handling routine tasks and offering information promptly. The narrative evolved further as sentiment analysis capabilities were infused, enabling AI to gauge customer emotions and tailor responses accordingly. This marked a pivotal shift of AI in customer service — from reactive problem-solving to proactive, empathetic engagement.  

AI-powered chatbots and virtual agents: Chatbots and virtual agents seamlessly integrated into the AI customer journey, becoming the first line of defense. They adeptly answered frequently asked questions, resolved simple issues and escalated complex ones to human agents.   Today, AI in customer service has expanded beyond FAQ chatbots and intelligent virtual agents, permeating every aspect of the AI customer journey.  

AI-driven recommendation engines: They suggest products and services tailored to individual preferences, while predictive analytics pre-empt customer needs and address potential issues proactively, reducing contact volumes and agent caseloads.  

Emergence of generative AI in customer experience: This adds another layer of sophistication. Generative AI, with its ability to create context-aware and human-like responses, enhances customer interactions, providing a more natural and seamless experience throughout the AI customer journey. This advancement underscores the ongoing evolution and sophistication of AI in shaping positive customer experiences.    Interesting read: Impact of generative AI on customer experience  

The customer journey has long grappled with challenges such as limited accessibility, slow response times, a dearth of personalization, siloed data systems and inconsistent support, resulting in a fragmented and disjointed experience. Recognizing these pain points, AI intervention in the customer journey became imperative.  

Managing customer service bottlenecks: Managing the workforce is critical as customer expectations and call volumes escalate. However, the workforce need not be scaled in direct proportion. Optimal workforce management is paramount and the strategic deployment of contact center AI plays a pivotal role by forecasting contact volumes and scheduling agents well in advance, preventing over- or under-staffing.  

Anticipating customer needs: Unlocking the real power of AI lies in transforming data from contact center CRM and other customer touchpoints into actionable insights. With the AI customer journey, tracking customer behavior extends beyond website visits. It delves into every scroll, registering likes and dislikes. AI captures every intent and autonomously recommends ideal solutions without requiring agent intervention. 

Overcoming data overload: The influx of data with each customer interaction surpasses manual monitoring capabilities. The AI customer journey takes charge by automating call listening and monitoring. It adeptly identifies trends and patterns in customer behaviors and preferences. This automation not only relieves the burden of manual tracking but also significantly enhances the probability of discovering new business opportunities, facilitating customer engagement , upselling and cross-selling.  

Download eBook: Learn how to bridge CX gaps using AI and automation  

Learn more: What is customer experience journey?

While customers may not explicitly articulate their needs, they unmistakably express what they do not want. Understanding their preferences and requirements allows you to tailor your offerings. This marks the inception of the first stage of the AI customer journey — not vastly distinct from conventional customer journey stages but profoundly different in its approach, as the entire journey is infused with AI. 

AI customer journey: Presale stage  

1. Awareness 

Customers at this stage are often inquisitive and receptive. They are open to discovering new products or services. Aligning live agents to attend to these visitors is not scalable and does not confirm a conversion either.  How AI helps: AI identifies user intent through advanced customer interaction analytics , understanding behavioral patterns and preferences. During the awareness stage, you can engage customers through intelligent virtual agents and live chat (for high-intent engagements), offering personalized recommendations based on their behavior. This tailored engagement helps capture attention and foster a positive initial impression.  

2. Consideration 

In the consideration stage, customers evaluate options and compare products or services. This is the phase where, as a brand, you must convince customers that your products and services are the best fit for their needs. Repeated page visits and increased browsing time on your website and app are indicative signs that customers are considering your offerings. This presents an opportune moment to engage with them.  How AI helps: AI aids by leveraging predictive analytics to discern user preferences and predict product interests. In the consideration stage, you can use AI to engage customers through intelligent suggestions, offering options aligned with their preferences. This proactive assistance offers conversational intelligence and guides customers through their decision-making process.   

3. Decision 

Customers in the decision stage are ready to make a purchase. Their mindset is action-oriented and they are looking for seamless and efficient processes to finalize their decision.  

How AI helps: AI plays a crucial role in the decision stage by furnishing detailed information and comparisons through chatbots, live chat and assisting agents on live calls. It identifies user intent, responds to queries instantly and recommends suitable solutions.  

Good to know: With a modern conversational AI platform , you can seamlessly send product catalogs to customers via digital channels. Each message can showcase up to 30 products, complete with images, detailed descriptions, prices and more. You can also track user engagement and leverage proactive prompts tailored to users' behaviors on websites, apps and digital channels, delivering contextual assistance in real-time.  

AI customer journey: Purchase stage  

AI ensures a seamless purchase process by facilitating transactions and guiding customers through the checkout journey. Virtual assistants contribute to an efficient and user-friendly buying experience.  

AI customer journey: Post-sale  

5. Post-purchase experience  

Customers in the post-purchase stage seek enhanced product usage and satisfaction. Their mindset is geared toward exploring complementary products or services that align with their recent purchase. 

How AI helps: AI-driven recommendation engines come into play, leveraging the customer's purchase history to suggest complementary items. This not only enhances the post-purchase experience but also aligns with the customer's evolving preferences.  

In addition, AI simplifies customer onboarding and enhances product adoption by intelligently analyzing customer interactions. Through guided contact center workflows, it proactively engages passive customers, providing step-by-step assistance. Plus, AI also ensures continuous improvement by sending reminders and soliciting feedback through customer service workflows, fostering a seamless and user-friendly onboarding experience. 

Read more: Beyond the purchase — how can you excel at after-sales service?

6. Customer support and service 

Customers post-purchase often require support for queries, troubleshooting and issue resolution. Their mindset is focused on ensuring a seamless and personalized post-sale experience. It is critical to note that inefficient customer service can increase customer churn rate and adversely affect your customer retention efforts.  

How AI helps: AI-driven virtual assistants and chatbots take center stage in post-sale support. Across various channels, they address common queries, provide troubleshooting assistance and escalate complex issues to human agents when required.  

Spread the word: AI-powered unified agent console enables your agents to have immediate access to pertinent information and an exhaustive record of customer interactions spanning various channels. Through continuous monitoring of ongoing conversations, AI offers instantaneous support by suggesting corrections, recommending a knowledge base and proposing optimal actions. This proactive assistance significantly diminishes average hold times , leading to a substantial decrease in the average handle time , potentially up to 30%.   

7. Feedback and loyalty  

Customers at the feedback and loyalty stage typically reflect on their overall experience and contemplate the value they've gained. Their mindset is inclined towards expressing opinions, contributing feedback and assessing the potential for continued loyalty.   How AI helps: AI intervenes by systematically running customer surveys and collecting and analyzing feedback across various channels. By identifying sentiments, preferences and areas for improvement, AI categorizes customers into distinct segments, enabling personalized targeting. Here's precisely how AI accomplishes this. 

Loyalty modeling: Utilized to identify and segregate loyal customers who have demonstrated sustained engagement with the brand. 

Customer lifetime value models: Applied to discern customers based on their lifetime value, distinguishing between high, medium and low-value segments. 

Customer churn model: Pinpoints customers with negative sentiments and are at the risk of leaving.  

Uplift model: Targets customers likely to respond positively to promotions, optimizing engagement strategies for post-purchase interactions.  

Good to know: With omnichannel survey software , you can gather customer feedback across channels and fill in the missing gaps with sentiment analysis and AI-powered CSAT prediction. You can use this predicted CSAT to set up alerts, escalation logic or analyze agent performance.   

Optimizing the AI customer journey involves leveraging technology to enhance various customer touchpoints . Let’s explore the integral components that envelop the AI customer journey, shedding light on how these elements seamlessly come together to elevate the overall customer experience. 

1. Personalized recommendations  

Leveraging predictive analytics, businesses can analyze customer preferences and make informed predictions about forthcoming consumer behaviors. Understanding individual preferences and anticipating their requirements enables precise audience targeting , delivering highly personalized content, product recommendations and offers. This not only enhances conversion rates but also builds customer loyalty .   Moreover, predictive analytics facilitates the judicious allocation of resources, empowering marketers to focus efforts and budgets on channels, campaigns and customer segments most likely to yield optimal returns. This results in cost efficiencies and heightened contact center ROI.      

2. Intelligent chatbots and virtual assistants

By integrating AI-powered chatbots across voice and non-voice communication channels, businesses can provide instant support and information. These bots adeptly handle inquiries, offer solutions and guide customers through purchases, ensuring a seamless experience that extends beyond traditional business hours.  

Also loved by readers: 15 best chatbot examples from groundbreaking brands     

3. AI-fueled workforce planning  

AI enhances scheduling processes by analyzing historical data, employee preferences and business demand to create optimized schedules. This ensures that the right agents with the right skills are available at peak times, improving overall operational efficiency. Similarly, AI-based forecasting models leverage historical data and real-time insights to predict future customer service demands. This allows you to allocate resources proactively, ensuring workforce management aligns with anticipated customer needs.   

4. Omnichannel consistent experience  

A customer can initiate a support chat on a website, continue the conversation via a mobile app and receive follow-up emails, all with consistent information and context. AI ensures that customers are attended to with context every time they reach out, eliminating the need to repeat information. 

Good to know: Advanced omnichannel routing software goes beyond conventional call distribution, taking into account the unique skill set of each agent. By intelligently matching customer inquiries with the most qualified and available agent, it ensures that complex issues are directed to experts, resulting in quicker resolutions and increased customer satisfaction. 

5. Informed decision-making with real-time analytics  

AI-powered real-time analytics enable you to monitor ongoing customer interactions, identify patterns and assess the effectiveness of your current strategies. This timely information empowers you to adapt and optimize customer service processes promptly, ensuring that responses align with evolving customer needs. 

Do you know: Today, you can respond promptly to events of critical business significance in real-time with AI-powered analytics and reporting software . Simultaneously visualizing the customer journey and agent activities allows you to gain actionable insights into your business operations at multiple levels.  

6. Automated follow-up communications  

When a customer initiates contact with your inbound contact center , an AI-powered system can instantly trigger follow-up calls using a predictive dialer . This proactive approach ensures that customers receive timely callbacks, checking in on the resolution of their queries or issues. Predictive dialers play a crucial role in managing and optimizing call volumes, ensuring that follow-up calls are made at the right time and by the right agents. 

Moreover, predictive dialers use algorithms to predict agent availability, customer availability and other factors to dial numbers automatically. This not only reduces idle time but ensures that agents are connected to live calls more often, enhancing the effectiveness of outbound campaigns. 

Deep dive: All you need to know about dialers

7. Multilingual assistance   

AI's role in providing multilingual support ensures a diverse and inclusive customer experience. In a global customer service scenario, seamlessly cater to customers from different linguistic backgrounds by employing AI-driven multilingual support. This removes language barriers and enhances customer satisfaction by delivering support in the preferred language. 

8. Accessible knowledge base  

AI enhances customer self-service resources through the creation and maintenance of comprehensive knowledge bases. These repositories are equipped with AI-driven search functionalities, ensuring that customers can swiftly find relevant information. For instance, in a customer support scenario, an AI-driven knowledge base can offer detailed articles, FAQs and troubleshooting guides, aiding customers in resolving issues without direct human intervention. 

Deep dive: Must-have features for free knowledge base software  

9. Visual recognition for troubleshooting  

In scenarios involving product troubleshooting or technical issues, AI-driven visual recognition comes into play. Customers can use their smartphones to capture images of problematic components and AI algorithms can analyze these images to provide instant solutions. This visual self-service resource enhances efficiency and addresses customer concerns in a more tangible and user-friendly manner. 

In the intricate web of customer interactions, you are bound to face a perennial challenge — the need for a seamless, adaptive customer journey. As we navigate this AI-led, transformative era, the synergy between specialized AI models and Generative AI capabilities, such as found in Sprinklr AI+ , emerges as a reliable solution.  

With Sprinklr AI+, efficiently map the customer journey and streamline conversation while utilizing:  

Conversational AI platform : Assess customer intent and sentiment instantaneously. 

Quality management software : Pinpoint strengths and areas of improvement for each agent through AI-scoring parameters 

Agent assist software : Instantly analyze case context and suggest predictions and next best actions to agents during live conversations on both voice and non-voice channels. 

Schedule a demo with our experts today, or take a 30-day Free Trial and explore the platform at your own pace.  

Frequently Asked Questions

AI transforms the customer journey by providing personalized experiences at each stage, from awareness to post-purchase, through automation, predictive analytics and intelligent virtual agents. 

Analytics is crucial in understanding the AI customer journey, offering insights into customer behavior, preferences and trends, enabling businesses to optimize strategies for better engagement and satisfaction. 

AI addresses security concerns in the customer journey through robust encryption, authentication processes and continuous monitoring to detect and prevent potential threats, ensuring a secure and trustworthy experience. 

Common misconceptions about AI in the customer journey include fears of job displacement, lack of human touch and over-reliance on technology, while in reality, AI enhances human capabilities and augments customer interactions. 

Businesses can measure the success of AI customer journey initiatives by tracking key performance indicators (KPIs) such as customer satisfaction, response time, conversion rates and revenue growth, providing quantifiable metrics for assessment. 

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Automation Strategy

Where are you on your automation journey.

Morgan Stern

Vice President, Automation Strategy ‐ Itential

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In my time delivering network automation solutions with Itential, I’ve seen organizations and teams at every level of automation adoption — from those who are implementing ambitious self-serve networking offerings to those who are only nominally familiar with automation at all. As a company, Itential has spent much of our time positioned ahead of the market, speaking to customers who viewed our solutions as aspirational or even science fiction-like. But in recent years we’ve seen IT leaders and organizations make big strides toward more mature, organization-wide network automation strategies.

With Itential’s position as a market leading vendor, we’ve had the opportunity to take part in key automation conversations from day one. Our team has a lot of experience offering context and insight around how the automation journey works. After all, an organization only moves through their automation journey once – we see team after team move through the process. Based on that knowledge and perspective, we’ve created a Network Automation & Orchestration Maturity Model that organizations can use to understand where they currently stand on the automation journey and what next steps will lead to progress.

The evolution model represents the 4 stages that an organization or a team within an organization moves through on their automation journey, from zero automation to a stage where they’re able to deliver network infrastructure automations for consumption by end users (self-serve). Each stage represents a significant evolution in the scale of automation efforts.

It’s helpful to try to understand where your organization or team is to define your immediate and future automation goals and gain an understanding of the steps you’ll need to take to reach them. Let’s break down the four stages, the types of tools and processes that make up each stage, and the key factors for determining when you’re ready to evolve.

The first stage in the model represents network management that is done without an overarching automation strategy. Network activities are often done manually, with disparate individuals writing scattered automations for only their own tasks. At this stage, change management is slow, human error is too frequent, and the network team is often seen as the roadblock to rapid IT service delivery.

How to Evolve

If your network team engages in little to no automation, the first step is to pick up open-source tooling that enables automation such as Python or Ansible and start small, working your way up and building an automation library to handle routine, repeatable networking tasks. You can check out our tips to get started with network automation here .

This stage is where many network teams sit today. The average team has started their network automation journey, leveraging scripting technologies like Python or Ansible or packaged, off-the-shelf offerings like HPNA, SolarWinds or Cisco DNA Center and has built automations for day-to-day activities such as software upgrades, device backups, and configuration management. This stage is characterized by a focus on day-to-day networking activities, with engineers looking to automate to alleviate their own workloads. 

The value of automation at this stage is clear – time and effort engineers would have previously spent manually performing routine activities is freed up for solving more complex networking issues. But these task-focused automations are limited to the specific execution of the network modifications rather than automating the full change management process . The time it might save an individual engineer, or even a team, does not extend to the end-to-end process, where activities such as data gathering from sources of truth, manual pre- and post-checks, and managing tickets that naturally come along with any network change are still sources of delay.

If your network team builds automations for specific network tasks, how do they ensure security? How do they address pre- and post-checks, input validation, and other guardrails to an automation script? And can they share their scripts for others in their team or wider IT organization to use?

One of the hurdles teams can sometimes face when they start to hit against the limits of scripts is a kind of inertia – a potential vendor solution may change up how they need to approach automation, or it might not work well with the smaller task automations that are already built. The best way to ensure your task automations are delivering the value they’re capable of and to extend and evolve without losing investment you’ve made is to adopt an integration-focused automation solution. A platform that can ingest your team’s existing automation assets and expose them for use in automation workflows enables teams to add automation to the processes that surround a network change – not only does this maximize your current automation efforts, but it also sets the stage for making your next step on your automation journey.

At this point, most teams will have hit the limits of what they can accomplish with scripts and similar tools. The transition from task automation to process orchestration requires extensibility and scalability that can be found in third-party automation and orchestration solutions. Teams and organizations who have progressed to the orchestration stage have developed maturity with their automations and have shifted to an automation-first mindset: automation workflows are built out from ticket opening to ticket closure , enabling the network team to operate at a speed that brings them in line with the rest of the IT organization.

This is the first stage in our model where the business value of automation really starts to skyrocket. Teams can perform network changes at speed while maintaining security, and they can update any required ITSM systems to ensure all the parts of a change process are taken care of. The automations can integrate east and west across different network and IT systems, so that processes such as opening or closing a ServiceNow ticket can be included in an automation workflow within the automation platform itself. Network teams are enabled to manage the network across greater scale and at greater speed than before.

To maximize the value of process orchestration, you should look to ensure an automation-first mindset where the team is comfortable using automation to solve problems. This is achieved with time and practice as the team begins to automate across larger processes, but also by utilizing the right platform that provides the network team with the tools to scale their automation efforts. A tool like Itential that enables process orchestration through wide-ranging integration capabilities enables network teams to approach any aspect of their infrastructure with automation in mind.

How does your team ensure automation delivers value and fast service to other IT end users? How does network automation fit into the IT architecture overall – does it provide significant value to other teams and to leadership? How are business goals factored in? 

As a network team matures in its process orchestration approach, these questions start to arise – and despite the power of end-to-end automation and orchestration, they can be left temporarily unanswered. But with the right automation strategy and a powerful, comprehensive vendor solution that enables your team’s automation efforts to scale with knowledge, the team can progress to a stage where they’ve answered all those questions and more – reaching what we’ve identified as the final target state for an automation journey, where automation provides maximum value across the network infrastructure and the IT organization as a whole.

For teams and organizations taking a forward-looking approach to automation, self-serve network automation is the end goal. In the self-serve delivery model, end users can request network services just like cloud services, and the team is fully enabled to adopt NetDevOps and treat their network infrastructure as code – which includes doing things like pipeline regression testing for more robust validation. Also, the scope of automation efforts extends beyond just the network team to encompass internal and external teams.

Automations will integrate with other systems northbound via the automation platform’s API. This enables automation assets to exist in other systems as distinct services. A prime example of northbound integration for self-serve automation is Itential’s recently launched certified ServiceNow app . Itential integrates with ServiceNow via northbound API exposure to add network automations published by the network team to their organization’s ServiceNow environment. Then, a ServiceNow user can select an automation from a dynamically discovered list, input the required variables as dictated by the network team, and run the automation after an input validation step. This cloud-like user experience greatly increases consumption of automations the team has already built, raising the value of existing investment.

The Itential ServiceNow app is just one example, but it illustrates the larger mindset and approach that enables self-serve networking. A team that can leverage the full potential of automation will see unique benefits of self-serve networking that enhance their capabilities. By providing comprehensive integration, enabling easy exposure of automation assets, and integrating sources of truth and service assurance with each service offering, a self-serve model will meet the scale, speed, and security needs of today’s (and tomorrow’s) network infrastructure.

Whether you’re looking to start from square one, or take the final jump to deliver network services to all your end users, Itential’s API-first, integration-focused platform has the capabilities to help you progress on your automation journey. 

Take a deeper dive into the Network Automation and Orchestration Maturity Model to learn where you are today and what practical next steps will help you evolve.

Tags: Automation Value   Network Automation   Network Orchestration

Morgan Stern serves as the Vice President of Automation Strategy for Itential, where he is responsible for assisting Itential’s global customers in developing, implementing and deriving value from their automation strategies using the Itential Automation Platform. For the majority of his career, Morgan has focused on assisting large enterprise and service provider organizations in maximizing the business impact of new technologies as an architect, consultant, author, and industry speaker.

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