The 7 things successful automation teams do to scale

By 2030, automation will drive 75 to 375 million people to reskill and even
change occupation – do you agree it’s time to upskill your workforce?

By 2025 97% of business will have started using Automation

By 2030, automation will drive 75 to 375 million people to reskill and even change occupation – do you agree it’s time to upskill your workforce on how to adapt and adopt these new technologies?

Over the last 5+ years we’ve communicated regularly with our international Automation and AI network, we’ve also observed, interviewed, worked with, worked for, shaped, trained, learnt from and researched teams globally in Europe, US, India, Hong Kong, China and the UK

We’ve extensively researched the automation industry and seen it evolve from when Robotic Process Automation (RPA) was brand new in the mainstream in 2017, to how it has become intelligent automation. Our research has been focused on two metrics that have remained unchanged all this time, globally! We asked:

Why do 50% of all RPA and AI projects fail?

What were they doing right, what was everyone else (even big companies and large automation teams) doing wrong? We started to see patterns, repeating cause-and-effect situations and coming from a Lean-Six Sigma world, the instinct was to understand the root-cause.

Why do less than 5% of businesses succeed at scaling automation?

We discovered that each automation team or business had their unique (and sometimes not-so-unique) issues and problems, but they all tended to stem from an array of similar challenges that they had all come up against (see the Top 7 challenges RPA teams will face). After reviews and tracing we identified that most of these challenges could be traced back to a handful of mistakes that had been made and in turn we were finally able to boil these down to just THREE root causes (see the 5 key mistakes of automation and AI).

These 3 reasons seemed to be the cause of all the issues and problems that was causing all these Automation teams to not get value and fail at scaling their automation!


By understanding these root causes and mistakes, we were able to notice the patterns of what successful teams were doing and time and time again saw SEVEN themes in successful teams, and saw a lack of these 7 things in struggling teams.

Here are the 7 things that successful automation teams do to roll out automation at speed and at scale which we put into the acroymn: A. E. I. O. Y. O. U. and the AEIO YOU methodology which we have explained in our book Business @ the Speed of Bots (the AEIO YOU method)

The AEIO YOU methodology

Based on the 7 things successful automation teams do, AEIO YOU is a unique blend of Lean Thinking, Change Management, Intelligent Automation best practices to maximise ROI.

This 7-stage process (and its 36 repeatable steps) can be used to adopt and roll out new cutting-edge technologies across an entire business, to improve Automation project success (current industry success rate is 50%). This methodology ‘changes how we change’ with technology so that the entire company is involved and comes along the digital transformation journey, vs having change ‘enforced’ on them


this is the Awareness and alignment stage, where a company becomes aware of the new technology from a senior level and learn about how this capability fits into the overall strategy. However, an important step sometimes missed in organisations is to also make staff teams aware of the technology, how it will help them personally as well as has the business will be strengthened.

This is also the stage which aligns the different key business areas to agree on how to use this technology, how this new capability will be governed (in a centre of excellence) and where this CoE will sit (e.g. in operations, IT or perhaps in the business change department.


Once a Automation roadmap and operating model has been established for the RPA CoE team, the staff should be educated in the new ways of working. This is your core RPA/Automation team, Automation Champions and staff who will be helping with the automation. They should also be educated in their part in the CoE, what is expected of them, how to use tools and techniques

Coupled with this, is the provision of standardised tools from the CoE to empower core CoE staff and stakeholders to be part of the digital transformation. This will ensure that your teams produce successful results in their automation projects repeatedly


This is the application of tools and training to identify and accurately assess opportunities. This is also where your teams follow strict guidelines to design (ideate) the right solutions


This stage in the implicating of the optimised solution, be that RPA, intelligent automation or a lean intelligent automation


This is a way to measure return on investment or yield and prove the benefits were realised as predicted


This stage focused on the support side. As your virtual workforce grows you will need to re-organise and develop your support team with role profiles and procedures which were needs when your bot factory was small, so that you can better oversee your bots.


This final stage is a reflection of the first and second stages, where you become discover of new technology, upskill your team to match the new technology, and upgrade your automated processes which AI

Tony writes for the Digital Transformation network, educating businesses in using new cutting-edge technologies.

To learn more about using Process automation and other technologies, check out the AEIO YOU® Portal, a one-stop centre of excellence enablement platform for getting value from RPA and IA – speak to your local AEIO YOU® partner to learn more

tech savy 1

4 Steps to building a Tech-Savvy workforce

By 2030, automation will drive 75 to 375 million people to reskill and even
change occupation – do you agree it’s time to upskill your workforce?

”Empowered teams transform businesses”

If you’re a business leader, the COO or transformation director keen to bring intelligent automation into your business this is exciting news – but it’s important to note that “resistance to change” and “choosing poor use cases”, being the two most common reasons for why 50% of RPA and AI projects fail. This all boils down to a lack of education amongst analyst and consultant teams new to automation as well as technology knowledge gaps throughout the stakeholders who are impacts by it
There are tonnes of training programmes for how to build an automation software robot, and how to develop chatbots and programme AI solutions – but there is a lack of strategy, analysis and project delivery training in this automation and AI space
This type of training is one of the 3 things that we’re focused on providing our clients – we want to show you that -even if you’re late to the automation party, you can still get ahead of the game

“Automation is an anyone thing”

Robotic Process Automation aka RPA was specifically designed to elevate the pressures from the IT department from the demands of the tech-hungry companies. This demand was driven by customers wanting faster, more accurate, and more accessible, 24/7 omni-channel services. RPA gave business operations teams the power to control their destinies so to speak and build solutions to solve technical roadblocks caused by legacy systems, in a matter of weeks, instead of waiting for months or years for software upgrades.
By being a business led technology, in many instances it is the COO and operations directors who determine how it is used. Besides, they know the processes better than any technician or analyst and know how to get the most value out of their resources. There is a great need for the senior leadership team to have awareness and solid understanding of new technologies, benefits and use cases to navigate teams through the new digital world – upskilling needs to start from the top
Automation and AI requires investment in both training and technology so that the whole workforce is empowered, however many companies only train a handful of people in the core automation team

The empowered workforce approach

RPA market grew over 63% last year: an article in Tech Crunch a leading technology publication in mid 2019 saw an explosion on interest in robotic process automation. In fact by 2025, 97% of all businesses would have invested somewhat in RPA – we can safely say this technology will great impact all industries and business models over the next 5 years – as well as change the competitive dynamics in each market sector


70% biz believe RPA allows employees more human interaction: this recent study by business wire showed that “keeping employees engages and happy” led to successful digital transformation of businesses (take a look back at our “can’t get started, no momentum” webinar to get an overview of the RPA benefits

Even UiPath one of the world’s largest RPA vendors expressing the need to “keep employees at the core of RPA efforts”

McKinsey Global Institute report estimated that by 2030, automation will drive 75 to 375 million people to reskill and even change occupation. So on that final point, do we agree it’s time to upskill you staff? – and build a tech-savvy workforce?

If you’ve looked for training how to use cutting-edge technologies, most likely you’ve struggled to find the right training plans for your team – well let me explain why this is:

About 90% of all RPA trainers provide development and architecture training, the other 10% also do some basic analyst training to provide information for the developer, but you’ll probably be hard pressed to final a provider of training focus around the application and strategic delivery

This is also echo’d in other technologies, such as cloud and AI training – like Azure, Google cloud platform (GCP) and Amazon’s AWS – where all training focuses mainly on development and technical architect

We totally appreciate the importance of developer training however we would suggest that this lack of non-technology training for teams cause be 1 of the root causes for why 50% projects fail, and 98% can’t scale

 “ 50% projects fail, and 98% can’t scale” 

Teams are able to build the technology but teams struggle to manage projects and execute strategic plans for enterprise wide roll out – and by teaching 5000 students to help teams worldwide, helping firms and consultancies to set up and manage teams to both build and support automated solutions I’ve had the unique opportunity to peer behind the curtain and identify the roots to these grave statistics

Furthermore, by coaching and managing newly formed teams of analysts in RPA, I can see how an imminent flood of new analysts, consultants, programme and project managers in the market, with minimal tech experience will further reduce the success rate of RPA and AI projects.

Here are our 4 recommended steps for you, if you see the value that investing in teams to be more tech-savvy will bring to transforming your company:

STEP 1: AWARENESS – have a communications plan with a consistent message
  1. Awareness training for your senior leadership team
  2. Awareness training for Stakeholder teams (namely HR and IT)
  3. Awareness training for staff

Core stakeholder teams such as IT and HR should be the first to know about these new changes so your business is physically, technically and emotionally prepared for the new changes

STEP 2: GET INVOLVED – provide Digital Transformation training for teams to get involved
  1. Training the core team on the technology and how to implement it
  2. Training staff on how they can contribute so they and their teams can benefit from the technology (this is augmentation not replacement)
  3. Run regular workshops and lunch ‘n’ learns for staff to learn more, develop themselves and prepare for the new digital age
STEP 3: TOOLKITS – provide training on how to use tools and templates to implement automation
  1. Training for tools for each role in the implementation process (portfolio manager, analyst, automation champion, other stakeholders etc)
STEP 4: AUTOMATION & AI ECOSYSTEM – train on a suite of new technologies
  1. Training on various automation and AI technologies,
  2. Learn the most common technologies such as:
    1. Process Mining and Process Discovery
    2. NLP (natural language processing) and chatbots
    3. Machine Learning and Deep Learning
    4. Computer Vision and ICR (intelligent character recognition)
  3. Discover and learn new technologies that match the use cases in your business such as swarm intelligence, sentiment analysis, Datalakes etc.

So in summary, in order for a smooth and wide-spreading automation roll-out we encourage Centre of Excellence teams bring the whole company along on their digital transformation journey by; Building Awareness, Starting from the top down, training both the core team and stakeholders.

Robot doing repeatable tasks with a lot of folders and magnifier. Robotic process automation, service robots profit, automated processing concept. Pinkish coral bluevector isolated illustration

RPA’s biggest myth is also our biggest concern

“Bots will take all our jobs.”

Job loss is by far the biggest fear in companies to date. Understandably no one wants to feel like their job is at risk; I had even read in the news how analysts were the most at risk as their roles were highly logical and data driven. As an analyst myself this was a delight to hear. When your RPA team start going into different departments to find automation opportunities fear of job loss is the biggest challenge they will face. It’s a very sensitive topic which needs to be handled carefully, otherwise it can stifle any momentum you were hoping to build.

Change management is vital to ensure the right messages are communicated but businesses need to be thinking about how their staff (their most value assets) can be upskilled and look at redesigning career roles and paths to align with digital transformation.

Bots should be seen as enablers to a workforce not substitutes because human workforce’s experience and intellect is too valuable to lose. In many circumstances RPA has seen an increase in jobs as teams become more productive and cause companies to grow. New technologies always give rise to new types of jobs. Bots can remove the burden of tasks from the workforce, but this gives businesses the opportunity to upskill their staff – by attending specialist courses. I once heard the phrase that ‘RPA should take the robot out of the human’ so that the human can do more interesting and creative work.

There’s a lot of scaremongering about a Terminator-style uprising where Ai becomes as smart as, if not smarter than humans and takes over. There was even a recent debate (August 2019) where Jack Ma and Elon Musk debated the benefits or threats of Ai becoming smarter than humans.

Whatever the theories about what Ai could achieve, what we know now is that Ai is miles away from being smart enough to replace humans. Yes in March 2018, an Ai system was able to read handwriting faster and more accurately that humans could, and for other specific tasks (like playing chess or Go), Ai can out performance humans. Ai can even understand natural language, it can learn, and it can recognize images, but it still is a fair way from replicating human intuition and reasoning and having general intelligence.

Many leaders in technology believe we are already effectively Cyborgs, fully dependent on our technology. Your mobile phone is pretty much a prosthesis as many of us could not exist without it for more than a few waking hours. However, technology has always been an enabler, now allowing us to move through life at lightning speed compared to 30 years ago.

Intelligent automation and Ai bots are no different. They are virtual assistants and digital workers that improve staff productivity and enhance their careers. These intelligent bots are owned by their team (not by IT) to do the lower straightforward work, leaving the higher-level thinking to us humans. Bots are there to enhance our efficiency and effectiveness, not replace it.

However, as business leaders, an important stat you need to be aware of is that when companies introduce RPA about 65% of staff feel their roles are in danger. Like a cancer this can be harmful to your organization as fears spread to two thirds of your staff. If your company is serious about its digital transformation, it’s imperative that you engage with HR and address emotional resistance so what your most valuable assets feel valued.

Granted, RPA will remove the need for a many jobs and reduce the size of teams, as it allows individuals to become more productive. Where a job had required ten people to manage the volume of work, augmented with RPA, it would now only require 7.

So, the big ethical question is, what happens to the other 3?

This seemed to be the most sensitive and generally most avoided question in the media, though of late I’ve seen it become more talked about as technology advances and automation becomes more ubiquitous. Understandably every business has a natural attrition rate, due to performance, retirement or people moving on to other careers. However, businesses should have a plan for how to redeploy their top resource if possible.

  • Can they be re-deployed to another team that would otherwise need to hire external staff?
  • Will the company itself expand its workforce in the near future, and thus does it make more sense to keep these staff who already know the business, customers and processes?
  • Can we upskill them so they can move into a more technical role to support the new digital workforce?

As a responsible RPA leader, now is the time to start engaging with HR to review the re-structuring of employee progression paths in line with these new automation capabilities. This not only will mitigate fears of change and job loss spreading through your organization, but will also keep knowledge in-house

Perhaps a new way to answer this question could be; where a job had required ten people to manage the volume of work, augmented with RPA, the same team of 10 can now do the job of 14 in your ever-expanding business. Re-deploy, upskill, grow

You can learn more about implementing RPA (Robotic Process Automation) and intelligent automation at The virtual consultant. Online support 24/7

You can read about Lean IA’s trademarked AEIO YOU method in their new book Business @ the Speed of Bots, Succeeding In The New Age Of Digital Transformation


Overcoming the 5 key mistakes and 3 root causes of Automation and AI

…that are causing teams 99 problems to digitally transform

“Transformation is a process, not an event” – John Kotter

If you’ve been involved with change management you most likely would have heard of these words – which are from the world-renowned change management and business thought-leader john Kotter, and he’s written several books on the subject such as his New York best seller “Our Iceberg is melting”. The essence of his message and approach to change still runs true in this new digital age, as I hope to show you today


With the tech trends of the last few years suddenly accelerated by recent global events –  it is becoming increasingly difficult to direct teams and strategize as the world becomes more digitally reliant without technological awareness steering the ship.

As technology begins to infiltrate every industry, market sector and every job, CIOs, as a Mckinsey article put it, need to “move from being a functional to a strategic business leader”. If a company does not have a CIO, the non-techie business leaders need to become more tech-savvy to navigate in the new technology age to leverage cutting-edge tech and create value.

There are common traps that business leaders who are new to automation technology fall prey to. These surprisingly common pitfalls and mistakes end up causing an array of consequential problems and challenges down the line – so I’d like to address this at the root from which most or all problems and pitfalls stem from

—————– digital workers, not software ——————

But firstly, robotic process automation (RPA) is functionally quite different to traditional software changes – firstly because RPA is strongly business led (mainly by the COO and the operations team). The reason lies in the history of RPA which was designed to put power in the hands of the business and alleviate pressure for the IT department. RPA allowed the business (with expert guidance and support) to implement solutions and navigate technical roadblocks of old legacy systems faster, in a couple weeks or months, rather than waiting on cumbersome technical changes which could take years.

The other difference is that RPA is used in a very new way to other software, because effectively RPA ‘robots’ are virtual workers who do specific menial tasks – repetitive and mundane processes like copy-and-pasting customer data from one system to the next, or processing invoices.


RPA is a software platform, alike a virtual worker, that can mimic any menial and repetitive tasks your staff do on their computer, as long as the process has a logic workflow, with rules-based decisions and it doesn’t require human intuition.

So no matter what type of business or industry you are in, if you have staff doing repetitive tasks, this is a waste of their time and intellect, and waste of your money – as an RPA virtual worker, costs one tenth the cost of a full time employee, doesn’t take breaks and can work 24/7,365

Intelligent automation (IA) combines automation tools like RPA with Artificial Intelligence to further enhance the scope of what your virtual workers can do, be that; reading emails, extracting information from scanned invoices or forms, or even replying to customer via a chatbot

This suit of powerful technologies can save vast amounts of time on menial tasks and unlock six, seven or even eight figure financial savings annually from your business, whilst also increasing speed, throughput, accuracy of service and improving compliance and customer and employee satisfaction.

Many business leaders in industries not yet on the forefront of automation do not feel that this is right for their business and this is fair to think as vendors have focused mainly on Finance, Manufacturing and Insurance industries. However currently about 50% of businesses have started to use RPA, though it is forecasted that by 2025 97% of all businesses will be taking advantage of this modern technology.

Further exploration on how businesses can get started with automation is covered in another article, however I’d like to draw your attention to one data point:

“50% of all investments in RPA (and AI) fail”

This is a concerning reality I have been investigating for several years now, as almost all businesses in a few short years will be utilizing automation and AI, but only 50% of their projects will succeed – so let’s take a look at common reasons for this

1. Lack of senior or business leader tech-awareness

Where senior leadership teams are not fully aware of or can’t articulate what process automation is, it can be difficult for these innovative leaders to get their peers or seniors bought in. Furthermore, it becomes even more of a challenge to fit this into the corporate strategy and develop a roadmap for rolling this out enterprise wide

Leadership team that have a good and aligned understand of how robotic process automation and artificial intelligence (aka intelligent automation) should be used to transform the business and build centre of excellence teams to govern this will led in their market. Most businesses know that they need automation, AI, a chatbot and other buzz words to modernise, but not every leader truly understands how to leverage these in practice

2. Matching wrong technology to use case

“Easy when you know how”

choosing the wrong process for RPA or AI is by far the most common mistake we see businesses make in every industry, and it becomes an expensive PROBLEM, both in money, time and patience. Furthermore, it can result in projects being shut down, and then reopened a year later under new management. There’s also the cost of hiring external consultants to fix the issues and help teams unlearn bad habits – as well as re-gaining commitment from frustrated stakeholders

Identifying automation suitable processes is really quite straightforward when you have a tried and tested method for identifying, assessing and implementing processes. Having a data-backed approach and mechanism is a logic challenge against certain politics and influences and ‘teams who shouts the loudest’. It’s a challenge I’ve come up against many times however by using business data and a scientific approach it was much easier to persuade stakeholders on the best course of action

3. Poor stakeholder education and communications

“Your most valuable assets – your staff”

Implementing process automation to save hours and days of menial tasks from your employees working lives may sounds good financially, but actually can sound very daunting to employees, as it can stinks of replacement and redundancy. Poor (or lack of) communication from the business is the reason for what is by far the biggest cause of RPA and AI project failure – staff resistance to change

Staff need to be empowered with the know-how and the understanding that technology as always is here to augment our work so we are more productive. Teams need to b shown that the benefits of automation means less late nights trying to meet deadline, better job satisfaction due to doing more creative and human-to-human work instead of tedious, repetitive, mind-numbing tasks.

Granted, it would be naïve to believe that no staff member will become redundant or lose their jobs. As an analyst for many years, I have repeatedly heard that analyst roles were most likely to go as they were highly logic and methodical– perfect for automation

However, for societies and businesses to advance, old jobs (just like old business models) that no longer work will need to be modified to those that do. And having a training plan for employees serves everyone two fold.

1 – employees are upskilled and trained to do their job in a new way or do newly created jobs, or staff are re-deployed into different teams that are growing

2 – employees leave companies and enter the marketplace newly skilled to be repositioned into a faster growing industry

4. Lack of time and commitment from SMEs

“Making automation a corporate priority”

Stakeholders of RPA projects generally have unrealistic expectations as they believe implementing automation is a lot faster and simpler – but unless they’ve been made aware, they wouldn’t know what actually goes into automating a process.

Process owners and subject matter experts are who automation teams interact with the most to identify the problems, design, build and test the solutions. As they play such a central role it’s imperative that they and especially their managers understand at a high level the general process for delivering automation, as the project will literally live or die with them. They hold the knowledge of the process, the have the access to the process data.

What I’ve witnessed and heard from peers is that team managers (either un-informed or resistant to change) will dedicate an hour or two of one or two SMEs initially but then re-deploy them back into Business-as-usual work and have limited availability and put it as low priority going forward – again RPA is a fairly new concept and stakeholders must be shown that these changes is a process, not an event

5. Not streamlining process first

“Garbage in, garbage out”

There seem to be two schools of thought in RPA. Roll out automation quickly, leaving the processes as it is. This immediately positively impacts the bottom line with financial savings. However, re-engineering a process to optimise it first doesn’t speed up a bad system (which could put immense strain on bottleneck or result in an unstable robot), but provides a better return on investment and gives you a more robust bot due to a simpler process

Lean thinking is a great combination to RPA and AI, as many companies are starting to form Lean teams in tandem with their RPA team.

These 5 key mistakes cause hundreds of problems and can be boiled down to these 3:

Businesses failed to transform their automation plan because:

  1. They didn’t know how to get started and couldn’t build momentum
  2. They had technology skills gaps throughout their core and stakeholder teams
  3. They didn’t have a clear plan which showed how automation would be rolled out or how help achieve various corporate objectives

Problem 1: Can’t start, No momentum:
1- Why couldn’t teams get started?

Because they didn’t create a repeatable formula

2- Why didn’t create a repeatable formula?

Because they didn’t have a tried and tested framework to use

Problem 2: Technology Skills gaps:
1- Why did these companies have skills gaps?

Because they didn’t educate their core delivery team and stakeholder teams uniformly.

2- Why didn’t they educate their team?

Because they didn’t have a comprehensive training plan

Problem 3: No Plan, Can’t scale.
1- Why didn’t they have a clear enterprise plan for RPA?

Because they didn’t have an automation strategy

2- Why didn’t they have a strategy?

Because they didn’t have a scientific approach to build a data-backed automation strategy

We believe that in this new age, business leaders who empower their workforce to augment themselves and keep discovering new technology will achieve full digital transformation. Our training package is helping businesses solve these root causes and overcome common problems.

You can contact LeanIA to request a seat on their webinar series “Leaner, Faster, Agile” to discover what you’ve been missing out on.


So you want to identify the business processes/areas that are very suitable for RPA?

There are departments in your business crying out for automation, that are full of manually intensive computer-based processes, with staff doing boring, monotonous tasks. Look for teams with high attrition rates, highly frustrated employees, and large backlogs of back-office work.

You’re looking for areas that have a high amount of these types of processes:

  1. Logical repetitive steps
  2. High amount of data entry and data validation
  3. Syncing same data into multiple systems
  4. Inputting, exporting, migrating data
  5. Transactional processes handling high volumes
  6. Errors that have a massive impact on the business
  7. High percentage of manual errors
  8. Processes with high queues which delay delivery to customers
  9. Business areas with high employee turnover
  10. Large seasonal spikes in work volume
  11. Processes requiring large teams
  12. High amount of data searching, data gathering, or data cleansing
  13. Repetitive updates to databases or form filling
  14. Moving data from one system to the next, or between multiple systems or databases
  15. Highly regulated activities
  16. Financial, compliance, auditing

Avoid teams with these types of processes:

  1. Low volume
  2. High variety, multiple ways of completing a task (many exceptions)
  3. Unstructured data inputs (free text forms rather than dropdown lists)

You can follow us here, and you can learn more about implementing RPA (Robotic Process Automation) and intelligent automation at Your virtual consultant

You can read about these tips and our tools used in the AEIO YOU method in their new book Business @ the Speed of Bots, How to implement RPA that scales


Top challenges your RPA team have/will face, and how to overcome them

When starting your RPA and intelligent automation journey your team will inevitably come across some or most likely all of these challenges – let’s discuss how to overcome them, and potentially minimise the chance of them arising

Missing or unavailable data

When it comes to running successful RPA programmes, data is key. You want to measure whether the opportunities you’re exploring are worthwhile, and you also want to prove that benefits from the change have been realised.

Missing or inaccurate data gives a false view on the potential savings or return on investment of your automation incentives. You want to measure the weekly volumes and average handling times for each process as accurately as possible, so you can calculate the amount of FTE (full-time employees) effort these processes use, which will allow you to start prioritizing your opportunities.

It’s best to collect sample data and observe the process to sense-check the metrics. Some teams don’t collect data like weekly volumes or AHT (average handling times), and sometimes it’s difficult to extract data from the system. Also, unless you’re in manufacturing, it’s generally frowned upon to walk around with a stopwatch to time staff on how long their processes take to complete. To get a rough idea of AHT your team may have no other alternative but to ask several subject matter experts (SMEs) for their gut feel, however, recording the screen as staff carry out the process would be better

Staff’s resistance to change

One of the biggest challenges is resistance from staff who may be directly affected by the change. If they can’t see how it benefits them, why should they be interested? If they feel their job could be at risk, why would they want to cooperate? A good communications plan can mitigate the resistance, and conveying opportunities to upskill or re-deploy staff alleviates fears of being replaced and shows staff they are valued.

Getting buy-in should be first on the agenda and maintained continuously. You can keep staff involved with regular lunch-and-learns or periodic workshops that makes staff aware of the technology and shows them how to get involved. Keeping the door of your Centre of Excellence open and continuing to communicate with staff can relieve many tensions about introducing automation technology.

Loss of traction

It’s great if you have made a start by rolling out some POCs but perhaps things have gone cold and stakeholders have lost interest. If you’ve lost traction it could be because you’ve made the same mistake that countless other businesses make: you’ve tried to solve a really big problem or your team have chosen a very complicated process. These opportunities take far too long to deliver. When starting up, you need to deliver some quick wins regularly, mixed with some good-sized opportunities to get the momentum going. I’ll show you how to choose the right mix of processes to start with and how to prioritise the rest

No clear governance

If there are no checkpoints or no gated process, and roles and responsibilities have not clearly been defined, then no one is accountable for each step of the progress. Your project is at risk of going around in circles or halting all together. Having clear, written governance that is agreed by all parties upfront, and with senior sign off, is the only way to avoid this pitfall.

Not only do you need to know who is responsible for each part of the implementation process, there needs to be an agreed turnaround time for each step. Assemble questions such as ‘How long should it take to get robot access to applications, or for the Process Definition Document (PDD) or Solution Design Document (SDD) to be signed off?’ and ‘How long will it take for the bot to be handed over from the developer to the support team?’ Also consider that at each checkpoint or gate you need clear acceptance criteria.

Process clarity

The PDD is the most important document in this whole process. This is what translates a business problem, into a technical solution. RPA business analysts must manage the relationship between the SME & developer, so that the developer creates a bot that does what the business wants.

Its important that the process steps are well documented at a detailed level, showing the clicks and keystrokes the robot should make. The developer will most likely be unfamiliar with the process so there must be no gaps or jumps between one step and the next.

This document must include everything the developer needs to effectively become an immediate expert of the process. The developer needs to know:

  • Where the bot takes the information from
  • How it will receive it and in what format (e.g., what is the folder location, website address or email address, and will it have a specific subject title? Will the file be in xlsx or pdf format?)
  • The precise format of the data (e.g,. what type of data is in each field, what is the file naming conventions are, and other data integrity information).
  • What the bot must do with the information
  • Where or to whom to send it.
  • What time(s) the process needs to run, what days, and if there are any service level agreements (SLAs). This may determine whether multiple bots might be needed to process all cases in time to meet the deadline.

We take a closer look at the PDD document structure in the book Business @ the Speed of Bots

Documentation can differ from reality.

This links to the previous point. A major faux pas of your RPA team would be to rely on work instructions and other process documentation to design the automated process. What’s written down tends to be quite different to what actually happens. There are 2 simple reasons for this:

1.     Work instructions are usually written by the experts who know the process inside out. This is susceptible to ‘the curse of knowledge’, which means assumptive leaps may exist between steps, and the author may automatically believe this to be obvious.

2.     The team or individuals might have found potentially better way(s) to do the process after the instructions were written. In lean thinking, the best way should be the standard, so maybe it’s time for the team to find out which team member is the most efficient and use that person’s knowledge to update the formal process documents.

To avoid this pitfall, it’s always recommended to walk through the process with the SME. Shadow the person to observe how the process is executed in practice, so that your RPA analyst can fill in any gaps identified in the documentation.

Test vs. Live (Production) Environments

More often than people want to admit, bots can act differently in the live environments than they did during testing. There are several reasons for this but mainly it’s due to the application versions in the test environment being slightly older to that in the live environment.

Even if you have the most updated version in test, the bot may still act unexpectedly for unknown reasons, hence why a two-week, live test period is advised (or longer if the process is a high-risk, high-impact process such as one involving finances). During the test period the developer and support team can watch the bot closely and immediately fix any bugs.

We’ll look again at testing the process in both the test and live (or production) environments later on, but ensuring the test platforms have the most up-to-date versions to match the live environment as close as possible can minimize risks.

#rpasuccess #digitaltransformation #rpaimplementation #artificialintelligence

You can learn more about implementing RPA (Robotic Process Automation) and intelligent automation at On-demand Consulting

You can read about these tools used in the AEIO YOU method in their new book Business @ the Speed of Bots, How to implement RPA that scales

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Types of common departmental processes that you can automate right now:

Many companies, in fact, there are whole industries still hesitant to invest in process automation with RPA and AI. Perhaps your traps in a slow behemoth organization that hasn’t quite worked out to fit automation into the overall strategy and very risk-averse.

The reality is, these companies are more at risk not to be involved,

as they may get left behind, by competitors who are operating faster, more accurately, enhance their customer experience scale to serve more clients whilst reducing costs and boosting staff morale by removing monotonous. repetitive tasks.

Or perhaps you’re in a smaller company who has only caught wind of the new technology from articles like this or by observing a competitor. Yes, your ‘early adopter’ competitors are getting an edge by leveraging intelligent automation, but take a look below at some example processes that you could actually be automating right now.

Common use cases

  1. Automated receptionist for welcoming visitors to enterprise campuses
  2. Customer onboarding
  3. Daily briefings based on calendar and assigned tasks
  4. Data migration and entry
  5. Extracting data from PDFs, scanned documents, and other formats
  6. Generating mass emails
  7. Issuing refunds
  8. Periodic report preparation and dissemination
  9. Procure-to-pay
  10. Product categorization
  11. Pulling data from multiple websites to identify best deals on auction websites
  12. Quote-to-cash
  13. Transferring business cards to Salesforce
  14. Updating inventory records
  15. Updating vendor records

Banking, Finance & Insurance

  1. Appeals processing
  2. Claims processing
  3. Daily P&L preparation
  4. Financial planning
  5. Know Your Customer (KYC)
  6. Loan processing
  7. Logistics – Trade Finance
  8. Reconciliation
  9. Responding to partner queries
  10. Trade execution

Customer Service & Sales

  1. Automating multi-step complex tasks that require little decision-making
  2. Creating and delivering invoices
  3. Obtaining detailed billing data
  4. Loading a detailed customer profile
  5. Resolving simple but common customer issues
  6. Updating user preferences and other user information


  1. Absence management
  2. Candidate sourcing
  3. Employee data management
  4. Employment history verification
  5. Expense management
  6. Hiring & onboarding & headcount reduction
  7. HR virtual assistants
  8. Payroll automation

Tech & Support

  1. Fault remediation
  2. Opening up internal tools to customers or employees
  3. Regular diagnostics
  4. Regular testing
  5. Software installations

To find out about some use cases in the legal industry/department visit us at next week {Event postponed to december}

You can learn more about implementing RPA (Robotic Process Automation) and intelligent automation at On-demand Consulting

You can read about these tips and tools used in their AEIO YOU method in their new book Business @ the Speed of Bots, How to implement RPA that scales


Are you an Early adopter?

This adoption curve (a.k.a. the Rogers’ bell curve) shows the general distribution of when people and businesses start to adopt a new technology. Change and the unknown is scary to humans, and so when something is brand new, be that a new technology or a new way of doing something, understandably very few people or businesses want to be the first to trial it.

The Innovators are the brave first few. They are probably those in the inner circle, or close enough to the new technology to appreciate it. They may be more educated in the new concept that then rest of the people. Generally, they are pioneering-risk takers with a passion for exploration

The Early Adopters are courageous and daring with a great eye for spotting the beginning of a winning trend. They are keen to stay on the cutting edge so they know when a new approach is starting to gain traction and they can capitalize on the potential upswing.

The Early Majority is pretty much everyone else who can really benefit from the new idea, the rest of the target market. They still can get an advantage over the remaining 50% of the population who have yet to discover it

The Late Majority are slow behemoth organizations that only move because their institutional investors are peering intently over their shoulder. By this time, they feel more at risk not to be involved, as they may get left behind, or may already be behind. Or perhaps they are smaller companies with not enough resources to research market changes and may have only caught wind of the new technology from observing a competitor.

The Laggards are frankly the are the old-fashioned, stuck-in-their-ways companies, who refuse to move, they believe their old way will prevail. Think Blockbuster.

If you’ve never even heard the phrase RPA or Robotic Process Automation, you’re way behind many of your competitors.

It’s a sobering thought – especially if you’re in manufacturing, banking, insurance, healthcare or utilities (these were RPA’s earliest adopters). You’re even further behind if your company has lots of manually intensive processes, with a business model that is no longer scalable. Tell-tale signs are high attrition rates, highly frustrated employees, poor customer service, or large backlogs of back-office work which may be why you’re hemorrhaging clients and money.

However, if your company has started investing in RPA technology to automate your processes, this is great news! Your company can see the massive potential of getting a competitive advantage. As of this publication (2019) you’re probably at the latter end of the Early Majority phase. If you’re also leveraging Artificial intelligence (such as Intelligent Character Recognition, Machine Learning or Intelligent Chatbots), then you’re certainly in the Early Adopter camp, as your competitors will probably give it another 6–18 months to make this step.

Now it’s time to scale your automation and create a Centre of Excellence team

You can learn more about implementing RPA (Robotic Process Automation) and intelligent automation at The virtual consultant

You can read about Lean IA’s trademarked AEIO YOU method in their new book Business @ the Speed of Bots, How to implement RPA that scales


New to RPA? Avoid These Top 5 RPA Mistakes

All across the country and from our network in other countries too, we’re observing businesses and teams new to RPA and automation making these same mistakes. Let’s dive straight in to see how these can be easily avoided

1)    Lack of senior or business leader buy-in

Getting senior buy-in is the only way to get a successful RPA programme moving. During stakeholder engagements it’s vital to get top-down enthusiasm and demonstrate how RPA can enable their senior management teams to be more efficient and cost effective.

If senior management are fully on-board, they can remove blockers, deploy the right personnel to assist the RPA team to attend workshops, identify opportunities, and they can invest their limited funds in more RPA initiatives. If senior leaders believe in the technology, they will be more likely to redirect funds away from ‘tried-and-tested’ methods, such as recruitment or outsourcing, and start investing in this new and seemingly untested (in their area at least) technology.

2)    Choosing the wrong process

Time and time again I’ve heard of companies or consulted with companies who were in the midst of trying to automate a complex, low-volume, highly volatile process as one of their first processes. Sometimes the process even requires several forms of AI.

This is a clear sign of “shiny object syndrome” (SOS), the propensity to chase something because it’s new and shiny and everyone else is doing it.

Or perhaps a case of “The one who shouts loudest” gets their process automated. At this point the RPA sponsor or senior needs to step in. New things can have great value, but there must be a logical approach to utilitising such a powerful tool.

Choosing the wrong process to start with is guaranteed to stall an automation programme. Stakeholders will lose interest, the RPA team will get disheartened, and the RPA sponsor will be wondering whether she will ever get a return on the investment and may consider pulling the plug on the whole thing. Such a toxic perception of this tool will swiftly destroy what you are trying to achieve. If you suspect you have started with the wrong process, this realization may mark the last chance you have to hire an experienced RPA analyst or success manager to turn things around and get the process back on track.

If you have just hired an expert and have yet to choose your process, it would be wise to listen to the expert to avoid choosing badly.

3)    Lack of time/commitment from SME

Though buy-in from senior leadership gets things moving, the second hurdle is getting the managers and the team themselves excited and keen to get involved. This is potentially the hardest sell, as many may be totally content with the old way of doing things. Some may not feel comfortable with change in general, and the rest may quietly fear that this new technology could change their job or replace them.

Once team managers and SMEs are on board, the right expectations need to be set with them so they understand how much time the RPA team requires from them in order to design the right solution, to the necessary detail. If expectations are not clear upfront, staff and managers may grow frustrated with intermittent disruptions to business as usual. It’s important that they understand how the discovery and implementation process works, and the RPA team needs to clearly layout their method, and explain that it’s not a one-and-done event, but a staged process.

4)    Poor stakeholder education and communications

Doing RPA in a dark corner of the office leads to staff thinking the worst. Openness and visibility is best; take them on the journey with you. At a minimum, an easy way to stay visible is to create a newsletter and an online portal with information on the technology, the process and a discussion forum for questions and concerns.

In addition to the visibility provided by open-door policy and regular lunch-and-learns or periodic workshops, the Centre of Excellence should stay in communication with staff. Popping up and disrupting teams for a few days, then disappearing without results can be very irritating. Stakeholders in a few organizations I’ve worked with have expressed their frustrations about previous RPA teams who attempted to implement but were shut down or moved on to a different team without any explanation. Understandably when I came to launch the RPA properly I was initially meet by reservations and reluctance. Even if your RPA team needs to close down an initiative, its courtesy to inform the team as to why, so that the relationship ends on good terms and they are welcoming the next time you circle back.

5)    Not streamlining process first

There are two schools of thought in RPA. Roll out automation fast leaving the processes as it is because it will immediately impact the bottom line. Or optimize a process first with lean thinking so that the process is redesigned to be streamlined and is designed for a robot instead of a human. At Lean IA, you can surely guess which one we prefer. Garbage in, garbage out.

Optimising a process can provide a much faster payback period as costs may be lower because the process may be designed to be simpler. Imagine a process where the human goes back and forth to pull data from one application into another. This workflow is long winded as the human has limited memory, whereas the process could be redesigned for the robot to take all the information from the whole page, then paste it into the other page in one go, thus eliminating the old back-and-forth way.

You can learn more about implementing RPA (Robotic Process Automation) and intelligent automation at The Lean Intelligent Automation consultant

You can read about Lean IA’s trademarked AEIO YOU method in their new book Business @ the Speed of Bots, Succeeding In The New Age Of Digital Transformation


How to quickly identify your RPA team’s strengths and weaknesses

When you’re in the mists of ‘doing automation’ it is sometimes hard to find the time to take a speak back and understand what is and isn’t working in your RPA delivery. Make projects have stalled, staff aren’t as engaged anymore, or key stakeholders are looking elsewhere to invest their budget to save time and money, like in outsourcing.

I can’t now remember which business psychology book I had read this, however an important point it made was that you can’t perform whilst you’re in a learning phase and you can’t learn when you’re in a performing stage. This is why in our AEIO YOU methodology, reflection is a keep step in RPA delivery.

You can’t perform whilst you’re in a learning phase, and you can’t learn when you’re in performing stage

I’ve seen many teams that have processes and frameworks set up and keep running and hitting a brick wall, repeating this action each time, hoping the wall may eventually break (though you may run out of money due to minimal results before that happens).

So, here are a handful of questions to ask your self, your business and your team at each stage of the AEIO YOU method to help you identify where you may have potential areas of improvement in your RPA team and implementation process, which may encourage finding a new method that fits your situation

A: Aware & Align

Are YOU Aware?

It’s vitally important to win hearts and minds by being visible and taking key stakeholders along with you through the transformation journey.

  1. Can you and your RPA team clearly articulate in a sentence or two what RPA and intelligent automation is?
  2. Do senior managers in Operations, IT and HR understand how RPA and intelligent automation can impact and benefit their teams and departments?
  3. Do directors and C-levels understand how RPA and intelligent automation can fit into the organisation’s strategy? And do they have congruent realistic expectations?
  4. Do team leaders and staff members understand how RPA and intelligent automation can enhance their performance, productivity and job satisfaction?
  5. Do your RPA and Intelligent Automation Centre of Excellence and Operational Excellence teams understand the various myths and challenges as mentioned, and are they aware of how to avoid common pitfalls?

Otherwise can you combine several RPA hubs or centralise resources, tools, and knowledge so you can learn and improve faster?

  1. Are all RPA opportunities identified, assessed, and measured in the same way?
  2. Do all developers and support engineers work to the same coding standards?
  3. Do you have a standard tool kit, including checkpoint criteria and templates for each stage of automation lifecycle?

E: Educate & Empower 

Are they educated?

Test whether your Centre of Excellence (CoE) team has educated your entire staff:

  1. Can key stakeholders and staff involved in RPA projects clearly explain what RPA is, and what the benefits are at a business, department, and individual level?
  2. Have you connected with HR to educate them on the emotional impact of digital transformation with regards to new career paths and incentive structures?
  3. Has everyone in the teams you’ve targeted for automation attended at least one lunch & learn session or workshop?
  4. Do teams understand what the eight types of waste are?
  5. Do team managers understand what metrics must/should be collected to assess a process’s performance and suitability for RPA?
  6. Does your CoE have its own intranet portal with information, shareable material, and a forum for requests and questions?

Do they feel empowered?

Verify that your CoE is providing the tools, templates, guidance, and training need to accelerate your digital transformation strategy:

  1. Has each team interested in RPA been given an automation catalogue template and guidance?
  2. Is the CoE having regular communication with teams who are pursuing RPA to ensure they are using the right tools, and using them in the right way?
  3. Has the CoE received any positive feedback or interest from staff keen to learn more about the technology or take on more responsibility to assist in identifying and assessing opportunities?
  4. Has HR communicated that the CoE may provide new roles in the near future?

 I: Inspect & Ideate

Inspect: Have you identified and prioritised opportunities?

Review these questions below to confirm that your team has prioritized your automation candidates in a logical way:

  1. Have you measured your processes by effort (AHT x volume) and potential saving?
  2. Do you know which handful of processes make up 80% of all your team’s effort?
  3. Have you reviewed your top processes in detail to understand their complexity?
  4. Have you plotted your top processes by Effort, Benefit, and Ease of Implementation on the Complexity quadrant?
  5. Have you provided the leadership team with a visualization of the makeup the department’s processes so that they can strategically prioritise which processes to automate first?

Ideate: Have your solutions involved all the right people?

It’s important to include those stakeholders that may cause or be affected by the existing problem and may be impacted by the solution when implemented:

  1. Have you involved key stakeholders from the team(s) that are upstream from your process, who feed information into your target team?
  2. Have you involved key stakeholders from the team(s) that are downstream from the process, who receive information from your team?
  3. Do all the root causes identified by the workshop group relate to all the problems being experienced in the target team?
  4. Have you first considered lean process re-engineering?
  5. Did you have a technical expert present in your solution design workshop?
  6. Did you have full participation with everyone’s opinions being heard and respected?

O: Optimise 

Are your processes lean?

Before moving to the automating stage, look back and see how much waste you’ve removed from the process and see if you now have a lean process to automate.

Remember that the purpose of Lean Six Sigma is to meet the customer’s expectations as fast as possible, and in RPA and intelligent automation, Lean Six Sigma can greatly increase the ROI of your initiatives. See how well you can answer these questions:

  1. Have you identified all eight wastes in your target process and team?
  2. Have you mapped out your target team or process in a SIPOC?
  3. Have you identified non-value-added areas using a VSM?
  4. Is your CoE’s physical and digital work environment organized using the 5 S method and ready to scale?
  5. Have you run a root cause workshop and used the 5 whys to discover what the potential root causes are for the inefficiencies in your target team?
  6. How much waste have you eliminated in the proposed future state model, even before you automate?

Y: Yield 

Are you getting value?

The big question clients, stakeholders, and senior leadership will ask is, are they getting value from this digital workforce? Look at these questions to make sure your team can answer this:

  1. Was performance of the processes measured accurately before they were automated?
  2. Do you have visibility of your bots’ performance, via logs and a dashboard?
  3. Do your internal/external clients have visibility on how their bots are performing?
  4. Are you regularly tracking the bots’ and CoE’s performance metrics that answer the right questions?
  5. Have you found ways to quantify qualitative data? (cost of errors/re-work, using Net Promoter Scores etc)
  6. Have your robot performance indicators helped teams discover potential ways to improve on the most common systems and business exceptions?

O: Organise & Oversee 

Are you in control?

Go through these questions to confirm whether your Support team have all their bots under control and the team has a scalable structure.

  1. Do you have someone carrying out each role shown in the Support CoE structure (even if some of your team are wearing several hats)?
  2. Do you have a transition manager who is a separate person to the developer and support engineer?
  3. Do both the Development and Support teams use the same agreed coding standards, best practices, criteria, and knowledge base?
  4. Do you regularly provide process owners with bot health and performance reports and discuss seasonal or future volume increases?
  5. Do you have a prioritization mechanism and triage process for handling requests and defects?
  6. Are you continuously looking to optimise your CoE through the same lens as when inspecting a BAU team? (automate repetitive tasks, using self-service portal for clients and process owners).

U: Uncover, Upgrade & Upskill 

Have you identified ways to enhance your bots to gain more benefits?

Keep circling back to the beginning of the AEIO YOU lifecycle to stay aware of new emerging technology and education.

  1. Is your team aware of the plethora of different types of AI capabilities on the market? (e.g. chatbots, ICR/OCR)
  2. Have you meet with different AI vendors and watched demos of what their products are capable of doing for your business?
  3. Have you run any POCs with vendors to demonstrate first-hand how much value their ‘bots’ can add to your CoE team?
  4. How serious (or nervous) is your business at implementing AI into their business processes?

Want to get a clearer understanding of your RPA/CoE teams stregnths and weaknesses, take our straightforward multiple choice test and receive a free report that scores each sections and breaks down your results with useful actions you can use today to improve


You can learn more about implementing RPA (Robotic Process Automation) and intelligent automation at Lean IA Consulting

You can read about these tips and tools used in their AEIO YOU method in their new book Business @ the Speed of Bots, How to implement RPA that scales