AIPM Project Manager Magazine - No.2-2024

AIPM Board of Directors

Ms. Connie Beck - Chair

Ms Sam Pedram

Mr Peter Tow

Dr. Binod Aryal

AIPM Chief Executive Officer

Dr Darius Danesh

AIPM Editorial Committee

Dr. Ripon K. Chakrabortty - Convenor

Dr. Anthony Wood - Member

Mr Greg Hammond - Member

Dr Hang Thanh Bui - Designer

Email: editorial@aipm.com.au

Australian Institute of Project Management (AIPM)

Level 1, 18 National Circuit, Barton ACT 2600

T:

2 8288 8700

E: info@aipm.com.au

W: https://aipm.com.au/

LinkedIn: Australian Institute of Project Management AIPM

The views expressed by contributors to this magazine are solely their own and the AIPM accepts no responsibility or liability for these views

FromtheCEO

Dear AIPM Members, Partners and Friends,

Welcome to the revitalised edition of the AIPM magazine!

It is with great pleasure that I extend my warmest greetings to each and every one of you as we explore the latest developments within our realm of project management excellence As we delve into this refreshed edition, let us take a moment to acknowledge the significant strides we have made thus far this year

In the domain of project management, achievements are not merely goals met but milestones conquered through meticulous planning, collaboration, and relentless dedication This year has been no exception

From surpassing key milestones to fostering innovation in our methodologies, and refreshing our brand and logo, each triumph speaks volumes about the calibre of talent and commitment within our teams of staff and volunteers. Additionally, with a refreshed chapter committee membership and communities of practice we are poised to continue our journey of excellence with renewed vigour and determination.

As we commemorated International Women's Day, it is imperative to shine a spotlight on the invaluable contributions of the women in our project management community Their expertise, resilience, and leadership are integral to the success of our endeavours, and it is with deep appreciation that we recognise their unwavering dedication.

Looking forward, let us remain steadfast in our pursuit of excellence. Let us leverage our collective expertise to navigate challenges and capitalise on opportunities for growth and improvement. Together, we have the power to redefine the landscape of project management and set new benchmarks for success.

I am immensely proud to be a part of such an esteemed community of project management professionals Your passion, expertise, and commitment to excellence inspire me every day, and I am confident that together, we will continue to achieve remarkable feats

With sincere gratitude and optimism,

DR. DARIUS DANESH Chief Executive Officer AUSTRALIAN INSTITUTE OF PROJECT MANAGEMENT

FOSTERING INCLUSIVITY: THE IMPERATIVE OF GENDER-RESPONSIVE PROJECT MANAGEMENT

the dynamic landscape of project management, fostering inclusivity has become ore than just a moral imperative - it is a strategic necessity. Gender-responsive oject management practices stand at the forefront of this shift, recognising the verse talents and perspectives that women bring to the table.

Connie Beck / Chair of the Board/ Australian Institute of Project Management

r-Responsive Project Management

responsive project management ing and addressing the unique d contributions of both men and g he project lifecycle. It goes beyond simply having a diverse team; it requires adapting project management practices to ensure fairness, inclusivity, and equal opportunities.

Breaking Down Biases and Stereotypes

One key aspect of gender-responsive project management is the identification and dismantling of biases and stereotypes that may exist within project teams. This entails creating awareness about unconscious biases and implementing strategies to mitigate their impact By fostering a culture of inclusivity, teams can unlock the full potential of their diverse members

Enhancing Communication and Collaboration

Effective communication is the cornerstone of successful project management Gender-responsive practices emphasise the importance of open and transparent communication, creating an environment where everyone feels heard and valued Recognising and leveraging diverse communication styles contributes to stronger collaboration and innovation within teams.

Flexible Work Arrangements

Balancing work and personal life is a universal challenge, and gender-responsive project management acknowledges the unique challenges faced by women. Implementing flexible work arrangements, such as remote work options or flexible schedules, not only supports gender inclusivity but also contributes to improved work-life balance and employee satisfaction.

Promoting Career Development Opportunities

Gender-responsive project management involves actively promoting career development opportunities for all team members, irrespective of gender. This includes mentorship and sponsorship programs, skill-building initiatives, and a commitment to providing equal access to leadership roles. By investing in the professional growth of women, organisations strengthen their project management capabilities and enhance overall team performance.

Measuring Success Through Inclusivity Metrics

To truly embrace gender-responsive project management, organisations must establish metrics that measure inclusivity and diversity within project teams Tracking these metrics provides valuable insights into the effectiveness of initiatives and highlights areas for improvement, ensuring a continuous commitment to creating an inclusive work environment

Towards a More Inclusive Future

Gender-responsive project management is not just a checkbox on a diversity checklist; it is a transformative approach that propels projects and organisations towards greater success. By recognising the unique contributions of women, breaking down barriers, and fostering an inclusive culture, project managers can build teams that are not only diverse but also highly effective, innovative, and resilient in the face of complex challenges.

Embracing gender-responsive practices is not only the right thing to do; it is a strategic imperative for the future of project management

EMPOWERING SUCCESS: MENTORSHIP AND SPONSORSHIP PROGRAMS PROPELLING WOMEN IN PROJECT MANAGEMENT

In the quest for gender equality in the workplace, mentorship and sponsorship programs have emerged as powerful catalysts for empowering women in project management. As organisations worldwide strive to cultivate diverse and inclusive environments, these targeted initiatives play a pivotal role in nurturing talent, fostering leadership, and breaking down barriers.

By Sam Pedram / Director of the Board/ Australian Institute of Project Management

M Paths to Professional Growth

M as a compass guiding women through the complexities of project management. Seasoned mentors, often with extensive experience in the field, provide invaluable insights, advice, and a supportive ear For women navigating the intricacies of project management, having a mentor offers a roadmap to success, helping them navigate challenges, make informed decisions, and cultivate the skills needed to advance in their careers

Building Confidence and Breaking Glass Ceilings

Mentorship programs play a critical role in building the confidence of women in project management

By fostering a supportive relationship with a mentor, women gain the self-assurance to assert their ideas, pursue leadership roles, and overcome imposter syndrome. Breaking through glass ceilings becomes not just a possibility but an achievable reality with the guidance and encouragement of a mentor.

Sponsorship: Opening Doors to Leadership Opportunities

While mentors provide guidance, sponsors actively advocate for the career advancement of their protégés. Sponsorship programs for women in project management are a strategic investment in talent. Sponsors use their influence within organisations to create opportunities, endorse women for key roles, and ensure that their achievements are recognised Through sponsorship, women gain access to networks, projects, and visibility crucial for career progression

Addressing Gender Disparities in Leadership Roles

One of the primary goals of mentorship and

sponsorship programs is to address the persistent gender disparities in leadership roles within project management

By actively engaging in these programs, organisations signal a commitment to creating a level playing field The result is a pipeline of talented, empowered women ready to take on leadership positions, bringing diverse perspectives and enhancing the overall effectiveness of project teams.

Creating a Culture of Support and Inclusivity

Mentorship and sponsorship programs contribute to fostering a culture of support and inclusivity within organisations.As women in project management benefit from these programs, they become champions of diversity, inspiring the next generation of leaders. This ripple effect creates a workplace where talent is recognised, nurtured, and elevated based on merit, irrespective of gender.

A Blueprint for Equitable Success

Mentorship and sponsorship programs are not just initiatives; they are a blueprint for creating equitable success in project management By empowering women with guidance, opportunities, and advocacy, these programs contribute to dismantling barriers and fostering a more inclusive and dynamic project management landscape

Organisations that invest in mentorship and sponsorship are not only shaping the future of their female talent but also ensuring a more robust and innovative future for the entire field of project management.

21-23 October 2024

National Convention Centre Canberra

INTEGRATING TRAINING: YOUR KEY TO DIGITAL TRANSFORMATION SUCCESS

In the rapidly evolving landscape of digital transformation, the difference between success and failure often hinges on the human factor: effective training. Let's explore how a welltrained workforce can become the cornerstone of digital innovation and business growth.

By Ria O’DonnelManaging Director at Custom Learning Design

Redefining Strategy: The Role of Training in Digital Transformation

The data on digital transformation projects paint a disconcerting picture, with a high incidence of projects failing to reach their goals Reports indicate that between 50 to 75% of these projects fail to meet their planned objectives Furthermore, in over 40% of cases, employee adaptation to new systems is negative and costly post-go-live. Additionally, a similar proportion of executives view their company ' s digital transformation efforts as unsuccessful.

Given this context, one must question why such a high rate of failure has become an accepted outcome The persistence of high failure rates in digital transformation projects, accepted as the norm, raises critical concerns about the approach to change within organisations This enduring pattern suggests a gap in not just execution but in the strategic planning phase itself.

To disrupt this cycle, project managers must shift their mindset. Integrating change and training professionals into project teams from the beginning is necessary for aligning project objectives with the day-to-day operations and capabilities of the workforce A collaborative approach with these experts can bridge the divide between new systems and user readiness, ensuring that digital transformation initiatives are set up for success from the start.

This proactive engagement is crucial to set realistic objectives and ensure that the workforce is adequately prepared and supported through the transition, ultimately increasing the likelihood of project success

Training as the Keystone of Good Governance

Effective governance encompasses a comprehensive understanding that a proficiently trained workforce is central to achieving project objectives.

This recognition should be reflected early in the project schedule, marking end-user training as a cornerstone of project planning

Introducing a technical solution into a business's daily operations is a critical juncture where the true impact on personnel is felt. The transition from theory to practice is pivotal, and it's at this point that training becomes more than just a box to tick it becomes the essence of successful implementation.

Regrettably, training is often relegated to an afterthought, hastily addressed only when the absence of a competent user education plan becomes evident to a project team preoccupied with technical specifics While functional design documents and user stories are meticulously crafted around system requirements, they frequently overlook the essential human element: how will individuals engage with and execute these tasks?

Remember, software serves merely as a tool; it is the workforce that breathes life into it, ensuring it fulfills its intended purpose Without targeted and thoughtful training, even the most sophisticated tool can fall short of its potential

Embracing Innovation: Digital Twins and AI in Digital Transformations

Project managers with an eye toward innovation may find a ground-breaking avenue in adopting digital twin technology and AI for digital transformation training. Digital twins, essentially virtual replicas of physical systems, offer a safe environment for experimentation and learning without the risks associated with live system manipulation Such a technology is developed alongside the system and is very different to a traditional training or sandbox environment When merged with AI, these digital counterparts can predict outcomes, personalise learning experiences, and pinpoint knowledge gaps in real time.

For those willing to challenge the status quo, integrating such training solutions from the project's inception can revolutionise the success of digital transformation. AI-fuelled digital twins can facilitate a more immersive and interactive learning environment that can continue to be utilised by the company long after the transformation takes place

This approach also aligns with the growing trend of experiential learning, where engagement and interaction lead to better retention and understanding. Project managers who invest in these innovative training technologies demonstrate foresight and a commitment to excellence By embracing digital twins and AI, they can ensure their teams are not just prepared for the digital transformation but are ahead of the curve, fully equipped to leverage the digital system to its full potential right from the outset.

Maximising ROI Through End-User Training

The efficacy of a significant investment like an ERP system for example, is heavily contingent upon user productivity.

From the outset, it's essential to recognise that training is not merely an optional add-on; it is the very engine that drives productivity and user buy-in. Enabling users from day one with comprehensive training ensures that they are not just observers of change but active participants in the transformation process This empowerment is directly linked to the return on investment (ROI) as proficient users can leverage the system's full capabilities, leading to efficiency and innovation.

Historically, technology and infrastructure may have consumed the bulk of investment, but the tide is turning.

End-user enablement is now recognised as a critical determinant of project success By investing in end-users, organisations can maximise their ROI and ensure that the system's day-to-day users are fully equipped to use it to its fullest potential.

This user-focused investment strategy enhances project managers' capability to deliver successful projects. It's a ripple effect: well-trained users become proficient more quickly, reducing the need for post-implementation troubleshooting and allowing the organisation to reap the new system's benefits sooner

Conversations with vendors such as Oracle and Microsoft, and systems integrators like KPMG and Accenture, have validated this approach.

These technology and business consulting leaders understand that when training is prioritised, projects have a higher success rate, and the investments made in digital transformation yield better returns Their consensus underscores a broader industry recognition that empowering users through training is not just best practice – it's essential when it comes to digital transformation.

In closing, the transformative power of digital innovation is not solely in the technology itself but in the people who use it

A well-planned and executed training strategy can empower your team with knowledge, and your digital transformation journey will not only commence on solid ground but also reach new heights of operational excellence

and customer satisfaction Inevitably, this leaves your legacy as a high-quality project management professional long after the transformation takes place

Are you ready to turn the key that unlocks the full potential of your digital investments? Let us help you harness the full potential of your project. With our expert training strategies, your digital transformation will not just be a journey but a legacy of success.

OPPORTUNITIES AND CHALLENGES IN APPLYING AI TO PROJECT MANAGEMENT

AI is no longer restricted to centres of methodological excellence with extensive computing and data processing resources. In this article, the author discusses where AI is being and could, in the near future, be applied by project managers to solve complex problems. In doing so, project managers will need to upgrade their skills and work alongside new partners in the organization. An example is provided of how backpropagation neural networks, a near 40-year-old machine learning tool, can be used by project managers to better understand non-linear relationships between luxury asset, GDP and DJIA data sets. The case study uses rare whisky bottle returns as an independent variable.

By Malcolm Gloyer Malfaelgin Project Management Limited

Introduction

Following the publication of their pathbreaking paper introducing backpropagation (the basis of neural net training) in 1986[1], the authors had to wait until the market penetration of on-line retail, search and community reached near saturation before Artificial Intelligence’s (AI) contribution to modern society was fully recognised Now AI appears to be everywhere from advertising to robotics. AI is used in drug classification for pharmaceutical research, pattern recognition for FX trading and electronic sensory development, as well as DeepMind research where the AI software runs through hundreds of decisions until it learns to predict an effective solution.

AI supports developments in healthcare, life sciences, cryptocurrency “mining” and self-driving cars using a kind of machine learning called neural networks in which a computer creates a network to recognise patterns using the learning data set and then makes a decision based on this network Plant Jammer, for example, is an app that creates recipes based on available food ingredients and personal preferences by searching a database of recipes to find often-paired items. Using AI to find new combinations of flavours for cupcakes and cocktails allowed UK-based media agency Tiny Giant to differentiate itself from its peers.

[1] Rumelhart, Hilton & Williams “Learning representations by back-propagating errors” October 1986. Nature Vol 323, 533-536. https://www.iro.umontreal.ca/~pift6266/A06/refs/bac kprop old.pdf

Generative or conversational AI enables apps like ChatGPT to assist students with essay writing as well as potentially offer businesses customer service efficiencies and healthcare alternatives to talking therapy

Challenges

First is the challenge of AI inexperience among project managers, which can manifest as difficulty using AI to draw conclusions from large data sets. Working with data, as anyone in the field knows, is a fast-rising requirement among all levels of project managers. Comfort with an array of tools, techniques, visualizations and automation methods is becoming a staple for project roles, soon to be joined by AI.

Luckily, it need not take years of study to begin using AI in project management application As we show in the example later, basic project management concepts can quickly be applied and extended by using AI methods The lack of AI understanding can also be addressed through experimentation, attending training courses or by pairing project management and data science teams. Enabling project management to talk the same language as AI practitioners is often the first step to breaking new ground.

Opportunities

AI not only improves on traditional techniques but can simplify the process. Just think of AI-based code generation tools used to skip over the cumbersome code writing step in heavily quantitative models

Simplification enables new development opportunities including developing AI neural networks using the labyrinth of models available in Python into valuation apps that allow investors with a demonstrated dependence on economic metrics to consider non-linear valuations of such assets that are less dependent on historical timeseries data than linear models

Other development candidates include the use of AI neural networks to enhance valuation of complex derivatives like housebuilder options on land for future development. Similarly Modern Portfolio Theory could be enhanced by including a nonlinear version of Markovitz’s Efficient Frontiers. Machine learning can be applied to the calculations of Value-at-Risk (VaR) and Potential Future Exposure (PFE) to include neural networks for directional prediction in market risk and credit risk, thereby optimising investments banks’ use of capital By doing so, the financial markets may avoid future market disruption resulting from risk assessments that depend solely on linear models.

Linear catastrophe modelling by insurance and re-insurance companies would also be enhanced by including non-linear neural networks to determine more accurate project management and pricing strategies while better ensuring that individual companies are resilient enough to withstand major disasters affecting their insured assets

Classification neural networks can pre-select suitable hedging trade candidates to offset regulated metrics like EMIR’s Gross Notional Value or IFRS 9

Conclusion

AI is not yet prevalent in the daily lives of project managers but it soon will be. The scope of applications is vast and, once AI understanding is developed, simpler than traditional techniques. For project managers looking to shorten the learning curve, there are available resources and often practitioners at your firm to learn from

Appendix

Applying AI in Portfolio Diversification Example

We provide a typical example of portfolio diversification by demonstrating the relationship between luxury asset returns and traditional financial asset returns. Analysis uses monthly changes from 2013 to 2020 in the Whiskystats Whisky Index (WWI).

[1] US Gross Domestic Product (GDP)[2] and the Dow Jones Industrial Average (DJIA)[3]:

A key investment feature of rare whisky bottle returns appears to be their independence from financial market returns The annualised standard deviation of monthly changes in the WWI from 2013 to 2020 is 10% compared to 6% for US Gross Domestic Product (GDP) and 15% for the Dow Jones Industrial Average (DJIA). While monthly changes in GDP and DJIA have a correlation of 0.54 (quarterly changes have a correlation of 0 39), the correlation of monthly changes in WWI and GDP is 0 04 (quarterly changes have a correlation of -0 13), the correlation of monthly changes in WWI and DJIA is 0 12 (quarterly changes have a correlation of -0.14), and the correlation of monthly changes in WWI and one month lagged DJIA is 0.04 (quarterly changes also have a correlation of 0.04).

[1] https://www.whiskystats.com/ [2]

https://fred stlouisfed org/series/GDP

[3] https://www wsj com/marketdata/quotes/index/DJIA

Figure 1: Monthly Percentage Changes in WWI, GDP and DJIA

Having trained the model using most of the data and then applied this model to the test sets, the result is a low R2 score of 0.12. We conclude that there is little relationship between monthly changes in GDP, DJIA and WWI using either a linear correlation measure or a non-linear machine learning algorithm neural network, therefore no evidence against the hypothesis that luxury asset returns are independent of GDP and DJIA returns

Similar results were obtained using ranked monthly changes to indices and by varying time lags

Python Application of Non-Linear ML Analysis

The development of Python has enabled abridged backpropagation neural networks to conclude that nonlinear machine leaning analysis is consistent with the above correlation analysis when data are separated into a model training set and a model test set in an attempt to predict WWI returns using GDP and DJIA returns.

The coefficient R2 indicates dependence between variables with a score of 1.0 and independence if 0 (it can be negative too, because the model can make the comparison arbitrarily worse).

Figure 2: Monthly GDP vs DJIA Change

Figure 3: Monthly WWI vs DJIA Change

Figure 4: Python Regression Code

Figure 5: Python Regression Results

CHOOSING THE RIGHT APPROACH TO SCALE YOUR ORGANISATION WITH AGILE

We focus a lot of our energy on how an organisation can go Agile and how we, as an enabler, can help your organisation to apply the appropriate support and governance layers to Project Management, regardless of the existing methodologies used. However, once we’re starting to implement Agile, we need to stop to think about the approach we use to scale it across our organisation and how it grows and is maintained thereafter.

By Fatimah Abbouchi Founder & CEO, Agile Management Office

Choosing an Agile approach

Where there are hundreds of organisations moving to an Agile way of working, there are many approaches that have been developed to enable establishing and scaling Agile. Some of these approaches include (but are not limited to):

Scaled Agile Framework for enterprise (SAFe®)

SAFe® works by applying specific parts of the Agile methodology for managing their portfolios of projects, teams, budgets and priorities; moving onto the next part as one is applied It runs across an entire, or subset of the organisation It allows for embedding of business SMEs into value streams as necessary/appropriate; providing clarity of how teams interface together.

“Scaling Agile” across the enterprise as a Transformation Program

Approaching scaled Agile as a Transformation Program by extending participation beyond digital/software development teams to other functional and operational areas. It works by adopting and creating approaches to suit the organisation’s context and needs – for example ANZ vs Spotify vs Telstra It typically aims to improve flexibility and ability of the organisation to rapidly adapt and steer itself in a new direction; minimising handovers and bureaucracy in an attempt to empower people

“Wagile” Mixed Waterfall and Agile

This one’s a little more interesting and requires a little more overhead The mixed approach allows Project Managers within the PMO to adopt a Waterfall or Agile approach to suit personal preference and/or team experience.

It means that some teams in a portfolio may run Agile, where others may run Waterfall or even a cross between both (i.e. ‘Wagile’). It uses ‘lighter’ techniques such as ‘Scrum of Scrums’ to manage inter dependencies. Using this approach isn’t always a conscious decision.

SAFe® remains the predominant framework being applied, especially in Australia Before choosing an approach that suits you it is important to consider some of the below before you scale Agile across your organisation Once you have a clear understanding of your environment, only then can you begin to select an approach that suits you:

Scaling Agile requires a degree of Agile maturity, a reasonable amount of experience using Agile at the team level, Understanding of the implications of the pros & cons for the specific context (i.e. industry, organisation, culture, size, etc.) – some are listed below, Governance planning should include a structured approach to governance.

Agile should be piloted using well-selected projects/programs, with clear feedback processes built in, as well as planned evaluation/review gates – this will create a more evolutionary / tailored approach rather than ‘big bang’, and Lighter weight options also exist to start scaling a number of agile teams- such as using specific elements like Scrum of Scrums

Key requirements for successful adoption of an Agile framework

Several considerations are required when looking to adopt a more Agile way of working, these will be similar across organisations and are key to successful change.

Top-down commitment, understanding and clear communication of overall objectives

A degree of Agile maturity- and a reasonable amount of experience using Agile at the team level. However, not everyone needs to be an “agile expert”- it can be successful with a critical mass of “change agents” (agile practitioners with deeper knowledge) alongside an appetite to invest in training across a broad audience

PMO & executive level need specific training to understand the change in approach and what will be required of them

Broader teams need a more general knowledge of Agile fundamentals In addition to upfront training investment, this also needs ongoing commitment of resources to the quarterly planning and other activities

Several quarters of Planning Increments can be required to embed new processes, and build momentum and commitment

Assigning dedicated business SMEs to sit within value streams is a further investment that also improves the likelihood of successful delivery of business objectives and value

The roll-out approach and change strategy need to be well planned and integrated with other transformation initiatives, to avoid confusion and maintain levels of staff engagement and to avoid change fatigue

Common challenges in scaling Agile

Lack of experience with agile methods

Management concerns about lack of upfront planning

Lack of management support

Culture or company philosophy at odds with core agile values

General organisation resistance to change

Not enough/effective collaboration

Not enough training

Inconsistent agile processes and practices

Time & software quality issues where agile and non-agile worlds collide/interface

Governance approaches

There are many challenges you’ll face when going Agile. If not accompanied by culture change, remnants of old control structures can be retained as an overlay across agile teams/squads, creating dissonance and competing priorities. Training and commitment where not achieved upfront, can also delay adoption, leading to confusion and slower delivery of benefits

Agile management is about working smarter, not harder! It’s about generating more value from less work That way the client gets value sooner Good governance structures, such as ‘The AMO Way®’, developed by the Agile Management Office, have been specifically developed to be methodology and industry agnostic, and to support this sentiment, it will:

Provide an overall coherent aim for the whole system

Coordinate the significant changes needed and address gaps to existing delivery governance structures and gating processes

Determine the next steps post Agile implementation and ensure alignment to operational readiness

Implements and adopts just the ‘right amount’ of processes and governance

Avoids delivering a one-size fits approach (although elements are modular in nature and can be customised)

It can often be difficult to go all-in with Agile, which may be due to vendors or internal culture that requires time to shift mindset, but applying a good governance structure with clear roles, responsibilities and accountabilities that integrate across the organisation can help with this Provides flexibility, continuity and consistency for organisaitons greater than ever before

Potential next steps

So, where to from here? Here are a few steps that may help you move to a more Agile way of working:

Formally assess and agree on the current and desired level of agile maturity across the organisation,

Further, develop agile maturity at the team leveloffer additional training etc- as a precursor to scaling (as and where needed), Commence a scan of the organisation for appropriate pilot initiatives, Commence development of a change management strategy that encompasses all other transformation activities to identify the most appropriate time to implement and/or pilot approach and; Ensure you have a clear governance approach, like the one The AMO Way® developed by Agile Management Office, which helps to ensure a consistent, well-coordinated and collaborative approach to governance that provides a frictionless, proactive, scalable model focusing on results and getting it right the first time!

The

HOW CAN THE PROJECT MANAGEMENT OFFICE AND PROJECT MANAGERS PREPARE FOR ARTIFICIAL INTELLIGENCE?

Artificial intelligence is everywhere. Whenever someone posts something about AI on social media there are often many comments decrying it and claiming that it either isn’t going to work, or is taking our jobs or our souls. The fact is that AI is here. AI is the next wave of change. AI is the next in the series of major transformation that started with the move to the cloud, then and currently digital transformation, and now an AI transformation.

By Ryan Darby - Sensei Productivity Pty. Ltd.

If you work in a PMO you need to be thinking about how the PMO can prepare for the impact Artificial Intelligence (AI) will have on the operational and governance work in the PMO, the projects the PMO governs, and the staff themselves In this article, we will provide you with a step by step framework to consider as part of your preparation in the PMO To prepare the PMO we need to think of five key areas If you consider each of these areas, you will take a more comprehensive approach.

Strategy: What are your goals for the PMO? How does AI help towards this? Are you chasing a fad, or can you use it for improvement in the PMO. This depends on why your PMO exists. A governance PMO needs AI such as detecting anomalies in projects, compared to a support PMO who may need a language-based service to help people find processes And you may need a mixture of these Much like your PMO, it is not one-size-fits-all. At Sensei Project Solutions the PMO is beginning to use a machine learning approach to highlight which projects need further governance attention.

Data: everything is about data AI works with an algorithm that takes in your data, learns something, and builds that learning into a model That model is what provides the answers to your questions or the predictions you ask for. Your preparation has to start with data, as the AI grows from the data. Is your data good? Complete? Clean? Do you have historical data to learn from? Is your data good for reporting now? If not, what will you feed your AI?

People: besides the data, it’s about the people Many people in the PMO are manually manipulating data, manually analysing data or looking for issues and providing support How will their jobs change? Will the AI itself be a new persona and will it be accepted? The introduction of AI is often done in conjunction with change management and your people and culture team.

Context: Will the output of the AI be accepted? Does AI actually feel right for your organisation and managers? Is now the time? Are you mature enough? If management doesn’t trust your current reports, forget about getting them to trust your AI. A key principle of AI is that it can be trusted and is transparent.

Future: how will you keep the AI alive? Will the right data keep coming? Will you continue to have a need for AI as part of a process? Will you redesign the AI as your PMO processes change?

With these in mind, here is a 7step process to help you understand what to do:

Step 1: Decide on your desired outcomes

Why do you want AI? AI isn’t some sort of magic problem solver. You need solid use cases or you will spend a lot of time developing ideas that don’t make sense.

To develop your use cases, ask yourself this:

1

Where can you artificially introduce experience?

Consider what you need to do but can’t. Work out how AI can help you achieve the goals of your PMO. Think of it like hiring a person. What would they do? What goal is not possible right now but is expected of you that they could help with?

AI is the tool which to support your process, people and strategy So, treat it as a process project with a large change management component.

Step 3: Redesign the process to use AI

2

Where can you introduce intelligence? This goes beyond automation Where in the process do you need time and space to think? Where is a process requiring you to make decisions that could be automated or improved based on wider input on a scale that AI can handle?

Inconsistently using AI as a bolt on tool will not lead to sustainable benefits. It needs to be introduced into the process. This comes back to your use cases. Examples of processes are using AI to create estimates when business cases are created; to help you justify the business cases; to find lessons before you begin etc But these are steps in a process You need to redesign the process to use the input and output of AI This may lead to a change in the roles that use the process.

Step 4: Get your data ready

It is important to consider:

Is the data central and ready reporting? If you don’t have a successful PPM solution with data you can trust then you are not ready to add AI to it

Do you have all the status report data?

Is the data readable by someone with no assumed knowledge?

Is the data full of shorthand, codes, and abbreviations?

3. Consider where there are errors, and how AI and automation could help

Where there are inefficiencies. Consider where there is a high cost in time, stress and effort.

4. These questions lead you to your use cases But you don’t have to implement them all, only those that support your PMO goals and eliminate anything “cool” that doesn’t

Step 2: Develop your plan AI is popping up everywhere, especially when people start using things like ChatGPT. This may not be the most thought out approach for you. Implementing AI into your PMO is either going to be a dedicated project, or part of a wider PPM solution rollout. These need to be planned as a project. But don’t treat this as an IT project

If you don’t have data, or that data isn’t clean and representative, then your AI will fail or be biased. Make sure there are sensible relationships in the data. The data needs to represent each process and you need to ensure you have permission to use the data Consider how it was captured and how it will keep being captured If it is once off data, your AI isn’t going to last Consider how legacy data may have poor controls around it and be in legacy systems. This makes it worth less as an input and possibly harder to get to. Most of your effort is going to go into preparing your data. Organisations are introducing a data supply chain officer to ensure they have sufficient information for the AI.

Does every project have the same volume and type of data? If not some will influence the AI more.

A positive side effect of this is that your PPM solution reporting is going to be so much better

Step 5: Build the solution This is where it gets more technical There are specific processes you use to implement AI, and whoever is helping you with it will lead you through this. What you build depends on the tool you select and how it is integrated into your PPM solution. It isn’t all ChatGPT. That is one specific approach for a specific set of problems.

Let your requirements drive the selection of and configuration of the tool Working backwards from an interesting tool is a classic IT mistake.

Step 6: Train it

The tool is generic. You can’t just jump in and go live. You need to train the tool and let it learn from your data. This forms its experience, and then it can provide a useful service using new data in the future Don’t underestimate the effort required

You need to have a set of good data that you can use to train it For example, if you want it to predict a project going red, give it examples of that You also need a set of data that you can use to test it.

As you prepare you are going to find you need to re-prepare data until you get a level of accuracy you are happy with. You will adjust the AI model and the data a few times. The AI is training you at this point to understand it and give it the data it needs

Step 7: Prepare the people

People need to know how to work with AI, and you need to ensure that the introduction of AI does not alienate your people

Think of the persona The AI doesn’t have to be a piece of software, it can be thought of as a persona doing a job and this may help integrate it into the process.

Manage the change and educate your employees about the changes AI will bring but do it so that you foster a positive mindset around AI Show them examples and how it will help them succeed

Reskill and upskill your staff

Teach them about AI and how to use it

Help your staff understand the importance of data, and how data is the asset that together with the AI provides the value. Therefore, they need to keep data secure and not give it to AI tools outside of your control.

Think about how performance metrics may change and how you measure humans versus AI

Once you have your solution in place, keep focussing on the data and the people Ultimately your people are who generate the data, and adopt the outcomes. Project managers specifically have a few areas they can work on to prepare. There are three main reasons why you as a project manager mayl be impacted:

Your personal journey may be impacted as tools appear that use AI, and you are not used to it For example, are you using Microsoft Copilot and Open AI to its full potential?

You may be managing a project that implements AI or implements a solution that includes an AI component, which is like an iceberg with a lot more below the surface. AI isn’t just a bolt on tool, it changes the nature of how tools work and how we transform our organisations.

You may find your normal project has teams using AI to complete their tasks and you can’t understand what they are doing

There are some practical things you can do right now to give yourself a boost:

Take advantage of what is there. Depending on where you are Microsoft Window Copilot or Microsoft 365 Copilot has already come to you, or is about to. This is a personal AI wizard in Windows and Office that works on language/generative AI and can help you describe what you want, and it will do it For example, in Excel you could ask it to build you a report of costs you got from finance, by opex and capex and by month, and work out the variances

Learn how to use ChatGPT, and specifically generative AI. But do more than treat it as a search engine. You can take a course in generative AI for project managers put on by the PMI.

Understand the principles of responsible AI, as they will keep your project on its moral compass Microsoft, Google and others all have published principles They are all similar, and will help you guide your project team if they are building AI into whatever you are delivering. You can see Microsoft’s version here.

AI is coming to us as project managers, but it is still early and we can prepare. Hopefully the above will give you some simple ways to get ready.

Barriers and opportunities in implementing mathematical modeling-based optimisation tools to optimize a project’s performance

optimisation in projects is crucial for success, involving the efficient allocation of resources to meet project goals while considering uncertainties and risks. Mathematical modeling techniques are utilized to optimize projects by defining objectives and constraints and solving models using advanced optimisation solvers. Industry-academia collaboration is essential to close the gap between theoretical research and practical project management, but barriers such as differences in priorities, communication challenges, and concerns over intellectual property hinder effective cooperation. Possible solutions include recognizing the limitations of project scheduling software, promoting collaborative efforts between PMs and academic researchers, and government support through policy implementation and financial assistance. Efforts to facilitate collaboration and knowledge-sharing between academia and industry are essential for leveraging the full potential of mathematical optimisation in project management.

By Dr Humyun Fuad Rahman, Lecturer, Newcastle University, UK & Dr Ripon K. Chakrabortty, Lecturer, UNSW Canberra at ADFA.

What do we mean by optimisation in projects?

optimisation is an essential process to ensure any project’s success and modern project managers focus on the effective allocation of different resources (e g , time, money, manpower, machinery) to optimize the goal(s) of the project, such as time, cost, or quality, considering uncertainties or risks in real-life Project managers (PMs) learn from existing projects and apply qualitative information, such as intuitions and experiences to optimize similar projects in the future. Apart from qualitative information, PMs use data-driven tools such as the critical path method (CPM), program evaluation review technique (PERT), and Gantt charts within the framework of data-driven tools such as Microsoft Project management for planning and scheduling a project (Wei et al , 2024) With advanced computing technologies, data storage and analysis become more convenient than ever, and PMs can utilize data for decision-making.

But the question is, do PMs utilize the full potential of data in optimizing a project’s performance? Before finding the possible answer to this question, let's first understand it through mathematical model-based project optimisation approaches

Opportunities for optimizing projects based on mathematical modeling approaches

Mathematical modeling techniques are popular optimisation techniques that replicate real-world decisionmaking problems through a set of objective function(s) (single or multiple objectives), a set of constraints, and a set of assumptions. For optimizing projects, the objectives are the goals that the PMs want to optimize in a project, as discussed in the previous section The constraints for optimizing projects could be renewable resources, e g , maximum labor hours, and machine capacity, and nonrenewable resources, e.g., budget for a project.

Once the mathematical model is formed, the mathematical model is solved by advanced optimisation solver(s) to find a global or best solution (or a set of solutions) that satisfies all constraints within a reasonable computational time

The quantitative data, such as process time and resource requirement for each activity, costs, etc Apart from a deterministic scenario, the mathematical model can also adopt the uncertainties in real life, such as uncertainties in activity duration or project execution time, and optimisation solvers provide realized solutions for the PMs. Finally, the generated solutions can assist the PMs in planning and scheduling projects along with their experiences.

The mathematical model-based optimizing planning and scheduling projects is a predictiveprescriptive approach, as a datadriven mathematical optimisation process helps PM for scheduling activities for a project under prescriptive analytics and modeling and solving project optimisation with uncertainties can be considered as predictiveprescriptive analytics, i.e., scheduling activities in a project (how we can it make happen) and uncertainties in a project (what will happen)

Based on the above classification of analytics, traditional project management tools, e g , CPM are prescriptive tools for decisionmaking and it is available as an add-in for Microsoft Project software. Even though CPM is a popular tool for computing the optimal duration of a project (makespan) with an unconstrained project, however, it cannot provide an optimal makespan for a resource-constrained project (Rahman et al , (2020)) Hence, identifying the best makespan for a resource-constrained project scheduling problem is an active research area and over the last few decades lot of optimisation approaches (solvers) have been proposed by the researchers

[1]Source:

https://fdotwww.blob.core.window s.net/sitefinity/docs/defaultsource /content/statistics/symposium/20 14/bigdataindustry.pdf, p. 11.

Likewise, researchers are actively studying different project optimisation problems under the framework of the mathematical modeling approach. As an outcome of this research, a good number of studies have also been published in different academic journals and conferences. However, the question is if the PMs can take advantage of those research outcomes In a simple word, in most cases, the answer is NO as the barrier exists between PMs who deal with real-world projects and academics who focus on research (model and methodology development).

Barriers between academia and industry

To close the knowledge gap between theory and practice in project management, cooperation between business and academics is crucial. Without the collaboration, academic research may focus on more theoretical research and PMs will only rely on planning software

Of course, software is an essential decision-making tool for PMs, however, the software’s capabilities are often limited considering the complexities of projects. Despite the potential of industry-academia collaboration, several obstacles frequently prevent this cooperation. The difference in priorities and deadlines is one major barrier For instance, the industry places more emphasis on immediate outcomes with a solution that may not necessarily novel contribution to the scientific literature, and revenue generation by achieving short-term goals, however, academia often concentrates on long-term research goals, focus on providing novel solutions to the problem, and scholarly publications. Effective communication and cooperation between the two parties may also set a barrier.

In one hand, if the academics focus on explaining complex mathematical models and solutions to PMs then effective communication might be hampered as PMs may be unfamiliar or less interested in complicated theories and technical jargon On the other hand, if PMs fail to explain the problem characteristics or business case to the academic researchers, and it creates a potential issue in formulating a proper mathematical model for the problem Concerns over intellectual property (IP) can also foster mistrust between the PMs and academic communities. Because PMs worry about exposing their business idea through research publications, and academics could be reluctant to publish their research findings. Finally, barriers in organizational policies from both the project management firms and the university set barriers to smooth collaboration and knowledge-sharing opportunities.

Reference

Possible solutions

Project scheduling software cannot capture all features to solve all projects and therefore, both PMs and academic researchers need to collaborate on a case-by-case basis Both parties should realize the opportunities of implementing the mathematical optimisation framework and work collaboratively to remove or reduce the barriers to industry-academia collaboration. Finally, as potential stakeholders of projects, both local and federal governments can facilitate this type of collaboration by setting up common policies for both academia and industry and providing financial support to both parties to engage in such collaborations.

Rahman, H. F., Chakrabortty, R. K., & Ryan, M. J. (2020). Memetic algorithm for solving resource constrained project scheduling problems. Automation in Construction, 111, 103052.

Wei, Y., Lei, Z., & Altaf, M. S. (2024). Simulation-based comparison of push-and pull-based planning in panelized construction. Automation in Construction, 158, 105228.

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Project Benefits Management

In this article, Dr Richard Hughes from Edith Cowan University reflects on a topic familiar to many AIPM members: project benefits management. While the topic has been explored before, Richard summarises some recent developments that practitioners should know about regarding project benefits management.

By Dr Richard Hughes Lecturer, School of Business and Law, Edith Cowan University

What are Project Benefits?

Many project managers are introduced to the idea of project benefits when they first encounter a strange box in a business case template asking for a description of the project’s benefits Other project managers discover benefits when they study for project management qualifications For example, as a child of PRINCE2, I learned that benefits were measurable improvements arising from some outcome that stakeholders deem to be advantageous. I may still have written any old waffle in the strange box in our business case templates. In our courses, we teach the PRINCE2 approach and the Project Management Institute’s (PMI) Guide to the Project Management Body of Knowledge (PMBOK®) approach. PMBOK® seems odd as recent editions don’t even define benefits in their glossary. This is not surprising as PMBOK® generally applies once a project has been green-lighted, and the business case template precedes the green light by months In reality the PMI, like PRINCE2, does have guidelines about project benefits which connect the funding organisation’s justification for the project to the predicted end result In addition, for some years, PMBOK® has associated business value with net quantifiable benefits which arise from doing a project The latest version of PMBOK® is much clearer about the strategic reasons for embarking on a project than older editions as it makes the connection between projects and business value much clearer. It’s also much shorter and better written. Thus, project managers who know about and work with project benefits probably understand that benefits should be central to a project's value proposition because, when correctly managed, benefits can bring financial gains, improved efficiency, enhanced customer satisfaction, and even create broad societal impacts Such core explanations of benefits are also present in guidance issued by State and Territory finance and treasury teams to managers of public projects

Who Needs to Know About Benefits?

Not all project managers define and manage project benefits daily In my conversations with industry colleagues, I find that many project managers working on the contractor side of projects focus less on benefits than colleagues working for the organisation which funds the project In many ways this is not surprising A key role for contractors is to make the scope of the project a realty for the organisation which funds the project. Therefore, the organisation which funds the project has bought the scope of their project from one or more contractors. It is therefore more likely that project managers who work for the organisation which funds the project are the ones who will have completed the box in the business case template describing the project’s benefits. The best benefits align with the funding organisation’s strategic objectives. A project’s benefits are therefore of little concern to the strategic objectives of a contracting organisation So, project managers working for the funding organisation should be adopting good project benefits management practices Such practices are about providing a clear rationale for undertaking the project while also giving the project board and stakeholders a tangible understanding of the project's purpose and the contribution that the project will make to the funding organisation’s strategic vision and plan. Clearly articulated project benefits in a wellconstructed business case are vital for securing funding and support for the project from initiation, planning and into its execution. Most contemporary guidance also stresses the importance of assessing project benefits post-implementation to enable the organisation to evaluate the project's success in a meaningful way, and to determine if and how the project did contribute to the organisation’s strategic vision and plan

What Has Changed Recently?

The issues I have described are not unique to Australia My peers in the research community have been actively looking at the problem with project benefits management for many years, and a recent batch of research papers has reignited interest here in academia Since many AIPM members are time poor and probably don’t read academic journals, I’ve summarised my top 6 take-aways for project managers from the current research into project benefits management, and have explained what’s changed.

1. Project benefits are broadening out

Project benefits management is starting to become more holistically integrated into both the project and the funding organisation. As such, narrowly associating project success with delivering a project within time, budget, and scope, and quality constraints is viewed simply as an indication of efficiency The value concepts seen in PRINCE2 and the current version of PMBOK® are being developed

For example, in 2021, in a Project Management Journal article, academic colleagues in Canada found that project benefits were indeed linked to a perceived gain by stakeholder but also that the complexity and variability in what constitutes a gain varied wildly. While economic benefits were the most prevalent, the research showed that project benefits also included improvements in operational efficiency, social impacts, and enhancements to the external and internal environment of the organisation.

What does this mean for practitioners? If you are part of an organisation which funds projects, take some time to review your approach to your business cases and the benefits described in them Do they go beyond how the project performs with respect to time, budget, and scope, and quality constraints? Do your benefits routinely look at the measurable value your successful project could bring to your organisation and its stakeholders?

2. Brace for impact

Benefits are now being viewed as part of a chain that creates specific positive outcomes which culminates in the impact that a project makes. In some amazing research in Australia, a benefit is viewed as a desirable outcome generated from a project or program. In line with current practices, benefits can be financial or non-financial, can be anticipated or unanticipated, but they still represent positive changes or advantages that accrue from the project to its stakeholders

However, the impact of the project is gaining increasing importance Impact refers to the effect on project stakeholders directly attributable to the value created from a project or program and distributed to its stakeholders. Impact can be positive or negative and may occur at any time from immediately after project completion to well into the future. It encapsulates the broader effects of a project beyond immediate benefits, potentially affecting wider societal, environmental, or economic fields.

3. Funder enlightening

According to recent research, the funding organisation should be recognised as the standard bearer for a project’s benefits, value, and its impact, and not the project manager. The project managers is too busy and is only with the project until it ends. The reality is that the funding organisation owns the money for which the funder is responsible. The funder should be the funding organisation’s representative on the project. There should be a flow of value, from the funder’s perspective, which streams benefits, or disbenefits, to stakeholders. These benefits should arise from the delivery of benefits and into the realisation of outcomes. Under such an approach, the funder is the one to determine the success of the project after the project has been executed In a narrower sense, the funder should also be able to gauge the performance of the management of the project using efficiency measures linked, for example, to the project’s time, budget, and scope, and quality constraints Therefore, a mature and enlightened funding organisation will have a clear understanding of the project’s intended or anticipated benefits, value, and its impact as part of the business case, and definitely at the end of the planning phase.

4. Bridging the gap

It has also been recognised that one of a project’s most fundamental features, its temporary nature, presents one of the biggest problems. In my upcoming research, and as discussed by other colleagues, there’s a gap that needs bridging between the project and the funding organisation To address the problem of defining and tracking genuine benefits, value, and project impacts two influential researchers have proposed that projects and funding organisations appoint a project owner They argue that the project owner should be accountable by the funder for securing the project’s target outcomes and, as such, the project owner should continually act on behalf of the funder both throughout the project and beyond. Beyond means having insight into business operations until the benefits, value, and impact have been determined. As such, a genuine benefits management and review process would be needed to underpin this new approach. Logically, it also means that the project owner should be the real client for the project manager, and it makes sense to appoint the project owner from within the funding organisation

5. A confusion of tongues

It is highly likely that when you read what I’ve written so far, you may get slightly confused with the terminology I have used I have deliberately used some terms in a specific way. For example, I have referred to benefits, value, impact, funder, project owner, client, strategic vision, outputs, outcomes, realisation, efficiency and performance in a deliberate way. Researchers are arguing that academics and practitioners should try and use the same terms in the same context. That way we will all understand each other whenever we refer to project benefits management. I appreciate we won’t get there immediately, but if we can do our part in the AIPM, then maybe we’ll improve how we understand and describe a mature project benefits management approach to others, and we can create a common lexicon for Australian projects

6. Better business cases

Finally, a mature project benefits management approach does not exist in a vacuum. It should be part of our regular activities in a project. We should understand it regardless of whether sit on the funder’s side or the contractor’s side. Business cases need to be better. Business cases should make the benefits, value, and impact explicit. More importantly, business cases should be used as a living document to guide project decisions in much the same way as risk plans and stakeholder registers My plea to AIPM members is to banish the fear of the strange box in the business case template, to share best practice about project business cases, and to ensure that what we create through our Australian projects is world class

References

Aubry, M., Boukri, S. E., & Sergi, V. (2021). Opening the black box of benefits management in the context of projects. Project Management Journal, 52(5), 434452. https://doi.org/10.1177/875697282110206

Zwikael, O., & Huemann, M. (2023). Project benefits management: making an impact on organizations and society through projects and programs. International Journal of Project Management, 41(8). https://doi.org/10.1016/j.ijproman.2023.102538

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