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.


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


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


The 36 steps the AEIO YOU method

The underlying approach of the AEIO YOU method is to ensure you’re applying Robotic Process Automation (RPA) and intelligent automation to lean (aka streamlined and optimized) business processes. Using lean thinking you can achieve a much higher ROI from your new technology than if you were to have just automated bad processes.

Remember the old adage, ‘GIGO’ (Garbage in, garbage out)

Follow these steps to bring your RPA Centre of Excellence (CoE) team to a maturity level to proudly call it an Automation Factory. Then start again from step 1, but this time with a newer technology

Here are our 36 steps of the AEIO YOU method:


1. Understand the Technology

2. Know the myths, Challenges and the Benefits

2b. Know the common mistakes and pitfalls

3. Understand the market

4. Choose the right solution provider

5. Be an evangelist

6. Run a Pilot (POV rather than POC)

7. Start building your Centre of Excellence


8. Bring in the Experts

9. Involve staff so they welcome the change

10. Keep hold of your most valuable assets

11. Upskill to build capabilities inhouse


12. Zoom out. Create an Enterprise Automation Roadmap

13. Zoom in: Define and Measure the problem

14. Filter on what’s suitable

15. Focus on the top 20% (80:20 rule)

16. Build a complexity map: Cost vs Benefits vs Financial savings

17. Prioritise: Go for some quick wins first

18. Zoom in further: Business Analysis 101 (Data and requirements gathering)

19. Root cause analysis


20. Solution design

21. Lean thinking

22. Business Case: detailed cost-benefits analysis

23. Define the process

24. Design the solution

25. Build the solution using best practices

26. Prepare the data for testing

27. UAT

28. Hand over to support team

29. Launch

30. Reflect

31. Repeat and scale


32. Realise the benefits


33. Maintain the benefits

34. Manage the changes


35. Circle back for continuous improvement

36. Discover newer technologies

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