Six group behaviours found in all successful project teams

Image by salestinus sustyo h

There are many reasons IT projects go off the rails or fail, but one of the most common is the behaviour of the people working on them.

Over the past decade spent rescuing troubled projects, we’ve identified six different group behavioural patterns that are common in successful, failed and reformed projects.

Understanding these behaviours and finding ways to recognise and expose them has been transformative in our line of work. But as large, high-profile IT projects continue to run into difficulties, we believe others might also find value in our methodology.

We refer to the behaviours as peak performance attributes and we consider them to be the six essential characteristics of a peak performing project team. We further divide the six attributes into two key areas, which we call business solution clarity and capability to execute. However, all six are considered predictive of project success.

Within business solution clarity, the three key peak performance attributes we look for in a project team are clarity of purpose, balance and alliance.

Clarity of purpose is as it sounds. Is everybody clear about the purpose of the project? And not only are they saying the right thing but are they acting in a unified way? This is a very important measure when a project involves multiple internal groups as well as third-party resources from big suppliers.

Balance is another important attribute in the project ecosystem – that is, among the project team, suppliers, business stakeholders, executives and the customer. All have to work in unison to achieve the project’s outcomes, and so balance is the ability of the ecosystem to trade off often-conflicting performance metrics of budget, schedule and solution quality or business outcome.

It’s very common in these projects to get different answers from different people, based on their own weighting of these trade-offs. A CFO may weight meeting budget higher than time or solution outcomes, whereas the business stakeholders may have a far greater focus on the solution quality rather then time or money.The reality is that to be successful, the project ecosystem has got to be able to efficiently trade those different factors off as a whole.

Alliance is characterised by a shared commitment and shared risks to operate as a unified temporary organisation for the business outcome. What happens when a project becomes stressed: are key suppliers as worried about the project risk profile as much as their own commercial risk profile? As soon as subgroups put their own risk agenda before the program’s risk agenda, the ecosystem starts to pull apart. Successful projects are unified and all pull in the same direction.

The other three characteristics – drive, certainty, and effectiveness – are all indicators of the project ecosystem’s capability to execute.

Drive is a measure of the group’s forward momentum on the project and their ability to quickly make and hold key decisions and overcome any issues that may crop up; certainty is about the group’s ability to effectively manage project risks; and effectiveness is a measure of the group’s ability to employ structured project management techniques to deliver project outcomes.

When we come across troubled projects, underperformance on any of these six characteristics is typically not a recent phenomenon that has caused the project to become derailed. Rather, low performance on these key attributes can be traced back to project inception.

Often, the project is set up and structured poorly – there may not be adequate role clarity, or the disciplines for managing the project are poor. It then becomes a chicken and egg problem: the poor structure causes poor behaviour, and poor behaviour then prevents the structure from being fixed.

Over the years we have codified recognition of the six group behaviours and used machine learning both to more quickly recognise variations in successful patterns of behaviour and to predict where they might lead if left unchecked. Each time we measure peak performance attributes of the group, our tool learns from them and improves its ability to predict whether a project is going to be successful or fail.

We have recently launched a new tool TeamAmp, that is part of our AI Assurance Suite that embodies our methodology and offers projects the ability to measure, monitor and manage these key performance attributes right from project commencement.

Time and again, we have found the human overseers of projects need help getting to grips with people problems. People are a very complex factor to measure and understand, and computers are well suited to this kind of behavioural pattern recognition.

In our more than ten years making projects right, the absence of specific project management methodologies and measures for people is clear. There are plenty of approaches to defining and measuring a schedule, defining and measuring a budget, designing, building and testing code. But what we found was it was the way people behaved and aligned, what they were focused on and how they managed risks jointly that really mattered to a project’s success or failure.

by Michael Devlin

How to apply machine learning to assist digital transformation projects

Large digital programs usually tend to focus on the technology side of the solutions, particularly IT transformation programs.

Image by Sourajit Sengupta 

The focus is predominantly on implementations of new platforms to support the business while forgetting other important aspects, such as people, organisational structure, business processes and platforms, what is going to change and how are they going to change in response.

Attention should be given to the role platforms continue to play in new solutions. Some of the focus needs to be on the domain of the solution rather than just on the tech. For example, the business architecture (a top-level view of how a business will operate), and how new systems will operate in conjunction with existing infrastructure.

System testing for quality assurance is often an afterthought. There tends not to be sufficient focus in the early stages on how a solution will be deployed on the business IT environment.

Consideration must also be given to the organisational change in the wake of a new deployment. This should be structured and planned from the outset so that change is properly managed and becomes relatively seamless.

Only by focusing less on the technology aspect and more on the business side can enterprises successfully implement any organisational rules changes, new business processes and strategies to manage the business transformation.

If the business is not set up to manage the transformation and staff are not prepared, the whole program can be jeopardised.

Other factors to consider are more akin to standard project management practices, such as dealing with risks at multiple levels – from technology to the risk of marketing changing due to adversity in the business environment. Given the average timeframe of programs, it is more likely than not that such issues will arise at some point so it pays to be prepared.

Structuring and planning for program effectiveness

During large transformation programs, operatives are spread far and wide so incidents tend to looked at in isolation, rather than examining the root causes. There needs to be more focus on a smaller number of factors that are crucial to the project.

It is very important to have a mechanism to collect information and data that provides a view of project success based on characteristics. With a small amount of high-quality data, you can build a model to interpret new data sets and correlate with existing datasets to draw reliable conclusions into a project’s success or failure, as well as gaining a better understanding of the characteristics of a project – what have we learned and what insights and recommendations should we glean from it?

So what are all the considerations and characteristics of projects? By collecting and assessing high-quality data, through machine learning, the system acquires knowledge and becomes better equipped to provide recommendations on how to achieve success.

This means that humans don’t need to do too much. Organisations do need to invest effort in understanding outputs though and be ready to be able to accept and understand data to take recommended actions.

Use machine learning to focus on practical, measurable outcomes. It is not simply about change management but rather making sure you are using tools and approaches in a way that concentrates on what is important to achieve successful business outcomes.

Article by Michael Devlin published on IT Brief Australia

More Aussie firms are hiring chief transformation officers

In a 45-minute window of the half-year results period just past, two large companies made the same significant strategic move, independently of one another.

At 8.00am, ANZ Banking Group created a digital transformation executive role. At 8.45am, BHP announced the appointment of its own “chief transformation officer”.

They are not alone. LinkedIn shows there are now 124 chief transformation officers in Australia. They can be found across government departments and private enterprise, and their growth reflects a trend that is also being seen in other markets internationally.

The appointment of chief transformation officers at high-profile Australian companies is significant for several reasons.

First and foremost, it demonstrates that transformation is its own skill and domain of expertise.

Transformations are complex and a discipline in their own right. One mistake we consistently see in under-performing transformations is where executives don’t appreciate that someone who is good at day-to-day business operations might not necessarily be as adept at managing a transformation program. There is still a view that these skills are interchangeable. The reality is they are not.

It’s rare that an executive is going to be able to look to someone internally – potentially more junior than them – who really only has business-as-usual (BAU) experience, and expect them to pull off a successful transformation.

Therefore, the ideal person to a run a transformation is someone that has experienced both sides: the business-as-usual operations, as well as having past transformation experience. If the latter is a stumbling block, the skills of a BAU-focused transformation leader can be augmented with targeted advice from a transformation specialist like Certus3.

Second, the arrival of the chief transformation officer is further proof transformation needs a seat at the executive table.

The most successful transformation programs are driven from the top down. This isn’t just about executive sponsorship, though having a strong sponsor clearly helps. For example, ANZ’s chief executive Shayne Elliott has consistently pressed the case for bank-wide adoption of ‘Scaled Agile’ under the institution’s ‘New Ways of Working’ transformation. Yet, the bank still feels a need to have a separate transformation executive.

This is because while executive sponsorship provides a mandate for change, the execution and day-to-day work associated with transformation delivery is best left to someone with a particular set of skills.

As McKinsey notes, “chief transformation officers should be independent (certainly not associated with the decisions of the past), have experience of similar turbulent corporate environments in their earlier careers, and enjoy support from the board, the CEO, and top management. They should be fully integrated into the executive team (not sidelined to a separate transformation unit). Ideally, they should behave like an extension of the CEO or even the board and as such be able to hold the top managers accountable.”

Third, executive representation, when combined with the support of risk professionals and specific tools, can help reduce uncertainty and ensure risks inherent in digital transformation programs are properly understood and managed.

By their very nature, digital transformation programs deliver changes to processes, technology and culture. This can have a significant impact on the business risk profile of an organisation.

Risks that aren’t adequately managed and resolved during program execution will often manifest in operational uncertainty after the program has gone live.

Those risks can be as simple as keeping everyone in the organisation informed and focused on the end goal. A recent survey found that while 67 percent of managers were aware of their digital transformation efforts, that awareness level dropped to just 27 percent of non-managers – evidence that there is a need to better engage staff.

A well-supported transformation executive is likely to be best-placed to run that engagement and to create processes and ways of presenting information to the business in a way that keeps the program on track.

Dashboards are often a great way of keeping everyone informed about a digital transformation program. A dashboard can provide an overall portfolio-wide view of risk and uncertainty across a transformation in real time. It also ensures that important information actually reaches the people who need to see it.

We’ve also found that regular independent assurance using risk-centric predictive analytics like AI Assurance is an effective way of ensuring that risks in a transformation program are progressively managed, and that uncertainty is reduced and understood by the executive committee.

Successful transformation is about getting programs on track and keeping them there. The elevation of transformation from an executive concern to an ex

Measuring people performance with ML


Eek! A machine is going to tell me I’m not good enough?…

We hear a lot about artificial intelligence and machine learning as if they are future concepts with no practical applications. But in the project management and business transformation space there are real-world applications of AI and ML showing very positive results.

One of the challenges of assessing the effectiveness of a transformation project is measuring the effectiveness of people. ML can be applied to measuring and isolating the people characteristics of these temporary organisations that make up a project to determine if people are performing well or not.

When something goes wrong in a big, complex transformation project it can be difficult to pinpoint the exact cause and effect, but it’s imperative to determine the root cause. It is quite a complex environment to even measure these problems and that is one reason why traditional project management methodologies and tools haven’t attacked the problem.

“Implementation of targeted ML-generated advice enabled a complete project turn-around in a very short period of time.”

We are using ML for this, as part of our AI assurance approach, and it is proving very useful. ML enables us to isolate and measure the people performance characteristics of a transformation project, that are predictive of success. With ML we can very accurately determine the probability of success, to derive improvement actions.

Why machine learning? The old approach to solving the problem is to gather data, see what occurred most often and draw conclusions as to why the project failed. The problem with that method is in such complex systems trying to draw cause-and-effect it is difficult. You would need an enormous data set that is much larger than most organisations can effectively gather and process.

ML can make quite accurate predictions on smaller, but higher-quality, data sets. There is a a need to train the machine learning system and we have done this via the characteristics we have isolated and measured in our challenged project recovery work. Over time we trained the ML to learn from each project what the characteristics of success look like.

It has taken eight years to train, but now our ML system learns on its own and is very accurate at predicting success or failure and advising how to improve your odds.

The practical outcomes? We have worked on financial services projects and one client ran our machine learning system a number of times across a recovery project. It gathered environmental data about the project and ran it back though the algorithm which correlated the characteristics of the target project with past projects. The implementation of the targeted advice generated by the ML system enabled a complete project turn-around in a very short period of time. We could then provide very specific advice on how to reform or improve the project to improve the probability of successful delivery.

Transformation success or failure almost always links back to people. This is a fairly well accepted proposition – everyone knows people are a problem and how they behave impacts projects significantly. AI is the only effective way to accurately measure something as complex as behaviour in a group context. The silver bullet is finding a connection back to people being the biggest risk.

For multiple parties to come together and be successful with a transformation project there needs to be good risk management. For example, a banking project will bring together a number of big suppliers and all those parties would be managing their own program risks and outcomes.

One example of what we can do with ML is precisely measure how much these groups are sharing common risks. You can’t expect groups to abandon their own interests, but business leaders must have visibility to the degree of risk alignment in order to manage a successful outcome.

Project and transformation success is a perennial challenge and sometimes improvement is as simple as shining a light on a problem, measuring the gap and raising the conversation at senior levels. For the first time ML is enabling this conversation.

AI and ML are great examples of emerging technologies helping to improve an age-old challenge like transformation project success. I encourage you to look at how your organisation is challenged by projects and processes, and consider how a data-based solution like machine learning can be applied for better outcomes.

Contributing opinion by Michael Devlin originally published on iStart on 12 September 2018

How Agile will expose your decision-making effectiveness

Article by Certus3 managing partners Simo Popovac and Michael Devlin.

A key goal for many organisations that go down the Agile path is to move faster.

The language of Agile supports this, packaging work into sprints and delivering new features and products on a regular cadence. So does the live experience: velocity is listed as the third-highest measure of success for individual Agile projects in the latest State of Agile report.

How fast you can go depends on a range of factors.

First, as Atlassian notes, velocity varies between teams. Each team estimates the amount of work it can complete in an iteration differently, and therefore works to a different pace. However, one would expect that pace to increase over time “as the team optimises relationships and the work process”, Atlassian says. There is a direct relationship between Team Performance and velocity.

Second, the higher the velocity, the better an organisation has to be at decision-making but making the right decisions.

In our experience, this is an area where many organisations still find they need some help. Artificial intelligence shows tremendous promise in this field because it is able to monitor across a vast array of complex scenarios thrown up in Agile projects and surface timely information and insights that help the business leaders overseeing these projects to adapt on-the-fly, make the right decisions at speed and keep to time.

Bad decisions still abound

Decision-making in Agile organisations is hard.

A survey by McKinsey in April found only 48 per cent of respondents agreed that “their organisations make decisions quickly”. Decisions taken at speed were not necessarily good; “just 37 per cent of respondents say their organisations’ decisions are both high in quality and velocity,” McKinsey found.

As if to highlight that, an earlier study, also by McKinsey, found 72 per cent of senior executives “thought bad strategic decisions either were about as frequent as good ones or were the prevailing norm in their organisation”.

That earlier study recognised the role that Agile organisational models could play in getting decision making “into the right hands” and being able to react to or anticipate shifts in the business environment faster.

Yet, adopting Agile by itself is not a guarantee that decision-making speed and processes will improve.

“In the digital age, good decision making entails taking more shots on goal and shortening iteration cycles. However, few decision makers are rewarded for such an approach,” a March survey says.

The success of a decision is still measured on the outcome it produces. How you arrive at that decision can be augmented and innovated on, and there is clearly room for that to occur.

A Swedish study on data-driven decision-making presented at the International Conference on Agile Software Development in late May shows the enormous promise of AI in this space.

While 79 per cent of respondents said data was “highly valued in today’s decision-making, a majority of the respondents agreed or strongly agreed that data should play an important role (71 per cent) and be highly valued (87 per cent) when making decisions in the future.

Bringing in artificial intelligence

In an Agile environment, governance is required to understand the metrics that indicate success in overall project terms and what actions need to be taken and when to get there. In that respect, information is power – the power to be successful.

Senior executives responsible for governing and assuring the success of Agile-driven transformation projects are rethinking how they get access to the right information at speed to help make good decisions.

Artificial intelligence (AI) is emerging as a key enabler. AI can assist people to access information that was previously inaccessible in a timeframe and format that enables sound and timely decision-making.

By making use of machine learning algorithms and expert systems, organisations can gather data from across a project and model it in new ways.

AI-based systems can also protect against internal bias and other factors which might weigh on the direction of decisions and results. Within Agile, you depend heavily on teams to accurately estimate how much work they can get through, and on people to provide assurance that things look correct. This is very prone to being influenced by organisational culture, politics and biases.

What is clear to date is that without AI, organisations and executives are far more limited in being able to measure and use the information for fast and accurate decision-making. Data-driven decision-making is the key to unlocking Agile success.

Why CTOs need to stop being overly-apologetic about Agile

Article by Certus3 managing partner Michael Devlin and Certus3 managing partner Simo Popovac.

Australia has more than its share of companies on organisation-wide Agile transformation journeys.

No one goes into these initiatives underestimating the complexity involved, but few companies emerge from the journey independently – without needing to call in reinforcements.

That’s because Agile at scale has its share of purists, models, misconceptions, and ambiguity. All are capable of sending an organisation-wide Agile transformation off the rails.

Many organisations are now dealing with the consequences of making the transition to Agile. How do I know if I’m doing Agile right? How can I develop maturity as the transformation progresses? These are just some of the questions we see Australian organisations asking themselves.

Some questioning of process and practice is healthy and is important to the iterative development of new ways of working inside of an organisation.

All too often, however, Agile projects in Australia are closely modelled on what has worked elsewhere. For many, it means adopting the so-called Spotify model for Agile organisational design – a way of arranging workers into cross-functional teams.

It’s often not the case that one size fits all. No one Agile playbook works.

Therefore, success is really about having the confidence to implement a flavour of Agile specific to your own organisational constraints and needs. It’s also about recognising and pushing back against other “needs” that might get bundled up in Agile transformations, but which do not necessarily fit with your own.

There’s no need for Agile purity

Agile ways of working do not exist only in a pure form.

Author Allan Kelly argues that Agility is a spectrum, with strict Waterfall at one end and Pure Agile at the other. The extremities are “sparsely populated”, Kelly says. Most companies land on the spectrum somewhere between these two extremes.

Oftentimes, companies are apologetic that the approach they’re taking is not Pure Agile. Arguably, however, those that strike the right balance are simply demonstrating a greater degree of maturity in their approach to Agile.

Agile maturity is often underestimated – or perhaps companies are overly critical of their own maturity, particularly where the end goal is defined in Pure Agile terms.

An annual ‘state of Agile’ survey last year found 84 per cent of organisations identify as being “at or below a ‘still maturing’ level when it comes to Agile.

Some organisations may be hard markers of their own Agile progress and performance. Positives for organisational and innovation culture are still possible without an end goal of Pure Agile.

But the high percentage also shows that not every company is as mature as you might think. Though it may seem competitors or early adopters have it together, many are still afflicted by the same core doubts about whether or not they are doing Agile right.

Of course, running in a hybrid fashion, potentially with different projects and parts of the organisation at different levels of Agile maturity simultaneously, comes with its own set of challenges. These include the extent to which it is possible to maintain a level of consistency, control and planning between these teams or programs of work. That may or may not be simpler than bringing an entire organisation up to the same bar of Agile maturity at once.

The decision is best determined by individual company circumstances.

Deconstructing org structure

Another maturity misconception is that people and programs of work naturally function better and faster in an Agile model.

Being performant is overtly promoted in Agile.

Agile relies on capable skilled individuals working in reasonably autonomous teams, often colocated with one another. Individuals and teams in Agile environments are seen as being well-connected, well-formed, and high-performing, with a greater ability to deal with change and to tolerate and respond to fluctuating levels of risk.

But high-performing teams exist outside of Pure Agile organisations as well. The same or similar results can be achieved in companies with a waterfall or hybrid organisational structures.

A related misconception is that self-governance reduces the need for external assurance. That is, a high-performing team does not need to track or measure its performance independently because of its inherent performance characteristics.

This notion is challenged, we would argue, by the spectrum of possible paths to agility and the lack of a one-size-fits-all approach to achieving success. Independent assurance is just that – a confirmation that things are, indeed, still on track.

Why organisation-wide Agile is still a work-in-progress

As Australian companies start to report the outcomes of organisation-wide Agile transformation journeys, their key learnings are now being debated.

Article by Certus3 managing partner Michael Devlin and Certus3 managing partner Simo Popovac.

Clearly, there are tales of caution and mistakes to avoid, but the question is how many of these are cross-applicable to others on similar journeys, versus how many apply only to the company involved and their specific set of circumstances?

Over the coming weeks, I plan to present an Australian state of organisation-wide Agile: what’s worked, what hasn’t and what we can take from user experiences so far.

To begin, it’s important to baseline the discussion.

The Agile approach owes its origins to software development. It was conceived by 17 developers at a US ski resort in February 2001, with the results of that meeting embodied in a manifesto.

One of the 17, Martin Fowler, wrote in 2006 that the manifesto “really captured the core of the ideas” from the initial meeting, though even at that stage he worried about the intent being misconstrued. “I’ve seen the terms incremental and iterative abused into all sorts of strange project shapes. I hope the manifesto will make clear what is and isn’t Agile.”

While what is and isn’t Agile is clear from a software development perspective, Agile has since transcended the world of software and is now considered an organisational change methodology.

The four core values and 12 supporting principles defined in the Agile manifesto have proven to be highly effective in the delivery of any business solution, not just for software development, and therefore have lent themselves to be used in organisation-wide transformations.

One of the leading adaptions of the agile manifesto now being used organisation-wide is the Scaled Agile Framework of SAFe.

Australian adopters of SAFe include Australia Post, Westpac and Telstra. Others, such as ANZ Banking Group, are deploying their own customised version of ‘scaled agile’ (which they are calling ‘New Ways of Working’).

Therein lies the complexity of organisation-wide Agile. There is no prescriptive way to approach it. Much depends on the company, its culture and its receptiveness to change – and this impacts the cross-applicability of lessons learned.

Further, Pure Agile may no longer be the specific end goal. Some argue that Agility is a spectrum and that few if any, organisations achieve full Agility. Most are instead destined to sit somewhere along that spectrum, perhaps combining Agile and non-Agile elements in the way they organisationally structure and function.

So, organisation-wide Agile is still very much a work-in-progress, complete with ambiguities and misconceptions of what it might involve, and no real way of knowing whether you’ve got things right (except in cases where it is clear that things have gone wrong). We’re changing this through the introduction of predictive measures of Agile success.

What is Agile?

In order to set a baseline for this discussion, it’s worth examining briefly the four core values of the Agile manifesto.

Agile is intentionally the opposite of the traditional waterfall approach to the development of business solutions where much time is spent upfront scoping requirements before work can begin on actually designing, building and maintaining a business solution (or in the case of software development, a piece of code).

One of the shortfalls of the waterfall approach is that by the time a working product is delivered produced, requirements have changed and therefore the outcome may be of limited utility. Agile changes this by reducing – but not limiting – the number of upfront requirements and design work, allowing a business solution to emerge over time to meet changing business requirements.

The core values of Agile are as follows:

Individuals and Interactions over Processes and Tools. This is easy to understand because the individuals that make up diverse teams understand business needs and are best positioned to drive delivery of a business solution.

Working Software (Solution) over Comprehensive Documentation.

This does not mean that documentation is abandoned altogether; rather it is limited to the minimum that is required to produce a business solution that delivers value to the customer in an incremental manner.

Customer Collaboration over Contract (Requirements) Negotiation.

The collaboration with the customer starts from the beginning and continues to the very end. The customer remains an integral part of the team throughout business solution delivery.

Responding to Change over Following a Plan.

The Agile approach recognises and embraces change, rather than fighting it. The focus is always on improvements that changes bring to the project and on added value to the business solution.

Over the past five years, a number of large Australian organisations have embarked on Scaled Agile journeys, with mixed results.

Hills Deploys Machine Learning to Support On Time Delivery of Digital Transformation

Hills Limited (ASX:HIL) is a value added distributor of technologies that ‘connect, entertain and secure people’s lives’ with turnover approaching $280Mill. It has built up a strong presence in the security, audio-visual, communications and health markets.

The Challenge

Like many large companies, Hills saw an opportunity to digitally transform its operations and become more customer-centric in order to drive new growth opportunities.

In September 2017, Hills announced plans to “develop an e-commerce platform that will provide the customers of Hills with 24×7 real-time inventory and self-service capabilities, including customer statements, invoices, pricing, online payments, and delivery information.”

“Hills believes the e-commerce platform will allow staff to be more engaged with customers and vendors, and create a stronger platform to promote vendor products”.

However, as with other organisations that choose to tackle digital transformation, Hills knew it would encounter challenges along its journey that would threaten the success of the project and had not attempted a project of this complexity for many years.

The Solution

As a result and following a board-lead initiative, Hills decided to deploy an innovative assurance approach using an artificial intelligence (AI) service by Certus3.  It selected TeamAmp, an innovative, lean ICT project assurance system developed by Certus3, to keep the project on track right through to its successful delivery and provide predictive measures of success which could be reviewed at any moment in time thereby avoiding potential pitfalls such as late delivery or budget blowouts.

Certus3 is Australia’s leading provider of independent specialist ICT assurance services. Its AI enabled tools and services have become critical to running healthy, complex IT transformation projects, providing quick, accurate, bias-free, systematic measurements and actionable diagnosis of challenges before they can adversely impact the delivery schedule or outcomes.

TeamAmp is the first use of machine learning to enable executives to continuously monitor and improve the people (behavioural) characteristics of a project that are predictive of success or failure. Certus3 have also created an expert system that supports TeamAmp by enabling clients to precisely measure the risk profile of their project and define a specific improvement or reform plan.

The Benefits

Hills used the TeamAmp system at key milestones during the e-commerce implementation program to drive improvements in team performance and ensure people remained aligned to the common goal.   It was also able to provide executives with unique insights into any areas of concern that enabled them to proactively guide and lead the project team to success. This also gave comfort to the Board given that this was a multi-million dollar project.

On a practical level, rather than using external consultants to run health check services, the project team was periodically sent a carefully designed, multiple choice digital survey that took less than 10 minutes to complete, ensuring it did not disrupt the team delivering the new platform.  All through the transformation program, the internal team at Hills were in control and were able to gather data and produce results through the online platform with no human intervention.

As a result, the new e-commerce platform went live on schedule in February 2018.

Chief Financial Officer, Chris Jacka, who sponsored the e-commerce program, says TeamAmp helped guide the program to success.

“It is a key pulse point in our project, delivering to Hills valuable insights to discuss with the project teams and refine project plans accordingly, which was more than just anecdotal feedback”

“There is no other way you can get this kind of analysis from the 40+ people involved in a program regularly and provided a clear value for money solution for our project team ” Jacka says.

The TeamAmp system also gave confidence to the Board and executives that the project would deliver the stated outcomes.

Non-Executive Director, Philip Bullock AO, strongly supported the use of TeamAmp for this project, based upon his industry experience built up over 20 years at IBM and the benefits he had witnessed elsewhere.

“Almost 40-50% of all technology based projects are late or over budget1. As a Board, we needed to ensure that the governance processes gave us the best chance of success. Certus3, via their TeamAmp, which has been developed over 8 years, gave us a level of comfort that we could deliver a critical project for the future success of Hills” Bullock commented.

Hills CEO and Managing Director, David Lenz, says TeamAmp “gave us a very clear insight into areas of concern” during the e-commerce platform delivery, “which resulted in us addressing those areas immediately. This was very beneficial for Hills and allowed us to move forward with the project.

“Through the use of a specialist assurance company we have been able to get a deeper understanding of the issues affecting people in a transparent way,” Lenz says.

An Eye on the Future

Hills is now embarking on yet another transformational initiative to upgrade its enterprise resource planning (ERP) to better position the company to support growth. Based on the success of stage one of the digital transformation, it is planned that TeamAmp will be used to provide assurance around the delivery of the ERP work.

For more information, visit or

  1. PMI’s Global Pulse of the Profession, 2017, “Success Rates Rise”, p5

Certus3 Launches New Australian-Developed AI-Enabled Assurance Solutions To Support Digital Business Transformation Success

Certus3’s new AI Assurance Suite supports traditional, agile and hybrid project management governance approaches and is an alternative to expensive consultant-lead health checks or assurance.

Certus3, an Australian services company which partners with organisations to assure large-scale and complex transformation programs, has launched its next generation of Artificial Intelligence (AI) enabled solutions. TeamAmp and SolutionAmp give companies the data they need to confidently manage digital transformations to success.

Traditionally, most organisations have used standard project management and governance processes to assess the ongoing status of their projects with some supporting this with internal or external reviews or assurance of the projects. In both scenarios, the organisations use measures and approaches that have not materially evolved in the last 20 years and are unreliable and highly variable in terms of quality and outcome.

At the same time, organisations are heavily dependent on the people involved in the reviews or assurance and are very prone to being influenced by organisational culture, politics and biases. They also look at lagging indicators, including schedule and budget as measures of progress and success and usually fail to look at leading predictive indicators of project success. By the time project issues surface in schedule and budget overruns, the leading indicators would have identified problems affecting performance for many months if not years.

“Our new AI-enabled solutions address all the shortcomings of traditional approaches and support the increased the use of assurance by significantly lowering the cost of assurance and its intrusion into project execution, says Michael Devlin, Managing Partner, Certus3. “By being able to run assurance reviews more frequently and focusing on leading indicators of projects success through machine learning automation, the success rate of projects will increase.”

Certus3’s new AI Assurance Suite supports traditional, agile and hybrid project management governance approaches and is an alternative to expensive consultant-lead health checks or assurance. It changes how project assurance is delivered and does this at a lower cost while placing control in the client’s hands. At the same time, it improves the effectiveness of assurance by measuring the predictive characteristics of success enabling organisations to take improvement action early.

Certus3’s new artificial intelligence based suite includes TeamAmp, a world first cognitive platform that enables companies to measure, monitor and manage people and how they are performing together. Its machine learning technology benchmarks a team’s behaviour to optimise their performance and then supports sustaining that high performance for the life of the transformation.

TeamAmp uses the data gathered from a company’s project ecosystem and calculates a project’s success according to six key attributes of high performance and then provides an overall Team performance Score. These attributes include Clarity of Purpose, Balance, Alliance, Drive, Certainty and Effectiveness. These attribute scores determine the heath of a project, the probability of success and what actions need to be taken to improve performance and chance of success.

In addition, SolutionAmp, part of Certus3’s new AI Assurance Suite, is a diagnostic expert system that delivers a rapid project risk profile and powerful action plans.

For example, a construction company implementing a compliance platform as part of a health and safety program must have a program with a clearly defined set of functions. At the same time, it needs to ensure safety compliance, monitoring and adherence to safety protocols that prevent injuries and save lives. At each stage of the project lifecycle the business leaders must assess that the solution being built will meet the stated objectives and provide the return on investment that is required.

SolutionAmp can guide the assessment of project milestones and control points to determine if the right level of certainty has been achieved and, if not, what needs to be done. It provides business leaders with a precise measure of actual versus target certainty progress, what the gaps are and how best to close them.

Devlin says “Our aim is to enable Australian business to start with the end game in mind, ask the right questions and confront the uncertainties of the future so that transformation success can be achieved. Our AI solutions now provide organisations with the predictive measures of root cause success factors so they can be actioned and improved before they impact project schedule, cost or solution quality.”

For further information, please visit:

About Certus3

Certus3 is an Australian assurance services company which works with clients undergoing IT transformation programs to diagnose issues quickly and accurately and subsequently provide clear insight into recommended approaches for improvement.

Certus3 believes that every business today is a digital business. As a result, its assurance solutions combine people, insight, and technology, enabling Certus3 customers to realise the full potential of their ongoing transformation programs.

Certus3 achieves client success through its locally developed machine learning and expert system software solutions. Current clients include Asciano, Hills Industries, Myer, News Corp, Masters, NAB, Perpetual, Telstra, Toll Holdings and Woolworths.


Are people the biggest risk to transformation success?

Australian companies are undergoing a hive of digital transformation activity with some projects more successful than others. Is enough being done to review the impact of people on the success of a project? Let’s take a look at how the “people risk” of a transformation can be identified and mitigated.

Start with the building blocks

Large transformation projects are seen as any other temporary organisation, with functional specialisations, partnerships and goals. The huge difference between an organisational unit and a transformation project is the temporary organisation is formed in a hurry – sometimes in as little as three to four months – and they are put together with not too much science.

There are expectations the teams will be a high performing unit without any real nurturing or caring. Compare that with any other large organisational unit and the disparity is clear.

A CFO building a high-performance function will start by developing a strategy, designing the organisational structure and carefully selecting staff and partners. Once that is put in place it goes through a process of one to two years to get to a level of operational excellence.

Too often the characteristics of big transformation projects lack this level of awareness assembling a person-driven function. This is the highest risk part because not all organisations form transformation programs with this care and many contract this risk away by engaging with a large IT services firm.

Temporary organisations are just as strategic to a business as permanent ones and having the right people in the right role, performing at a high level is a foundation for success.

Know your differences

As with any project, a transformation organisation has different skills and cultures that have to work together to deliver an outcome.

There are differences between people, suppliers, and even sub-cultures within a transformation program, and, due to their temporary nature, don’t really have the opportunity to go through a cultural change management exercise.

Your project environment, therefore, has to compensate for these cultural differences. You have to create a temporary project culture that is strong enough to balance the individual cultures. For example, there are particular technologies, such as collaboration tools, which can help deal with that.

Why is this a big risk? The problem is nobody is talking about it, let alone measuring it. This leads to a lack of awareness of what needs to be done to mitigate the people risk associated with cultural differences. One supplier might have a completely different way of delivering the same work package compared with in-house staff. These differences must be identified, measured and managed, not swept under the carpet of the transformation program.

Consider this scenario. An insurance company will find a big heavyweight IT services player and then try to transfer the internal scope of the transformation to the supplier. In most cases, the new supplier doesn’t align or change itself to meet the in-house people engine.

None of the focus is on forming a function of people that will operate in a high-performance manner – this is crucial when you need to do a significant amount of delivery in a finite time.

By shifting the delivery to a third-party, you need to be prepared to identify and assess the differences from the outset and manage them for success.

A path to better people-driven outcomes

Is it all misalignment and the invariable doom and gloom? The good news is a lot can be done to reduce people-related risk to ensure people continue to be your most important asset. Time and time again, our clients demonstrate that once identified and understood, these risks can be corrected using standard project management practices – sometimes in a matter of weeks.

A lack of role clarity, structure and lack of accountability are all found in dysfunctional transformation organisations. When we look at a failed or poor-performing project we often find the root cause back to these problems and we can use those as a predictor of whether the project will be successful.

Most of today’s project management collateral and tools deal with communication and operational challenges, but not at the same breadth and depth as what is possible by analysing the people-related risk factors.

Our advice to clients is to think about the people stuff upfront; set yourself up for success; measure the performance, and incrementally improve.

The old cliché “people are your best asset” unfortunately gets lost in many transformation projects. People are your best asset, but they can also be a risk if they are not managed properly from the outset.

Contributing opinion piece by Michael Devlin originally published in IT Brief Australia on 24 September 2018