Project Management

Voices on Project Management

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Voices on Project Management offers insights, tips, advice and personal stories from project managers in different regions and industries. The goal is to get you thinking, and spark a discussion. So, if you read something that you agree with--or even disagree with--leave a comment.

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Cameron McGaughy
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The 4 P’s of Successful Modern PMs

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AI Disruption to Transform Project Success Rates

By Peter Tarhanidis, Ph.D.

One of the impacts artificial intelligence has had is prompting a reconstitution of project management. Here I look to leading industry experts to explore the benefits to project management systems due to matured AI software; and the maturity of the project manager as a data- and fact-driven champion of business outcomes and innovation. This combination of advanced project systems performance and leadership competence will significantly transform project success rates.

As a background to the current state of project management, HBR states that $48 trillion is invested annually in projects. The Standish Group notes that only 35% of projects are successful, and 65% of projects waste resources and have unrealized benefits.

Additionally, Proofhub attributes project failure to firms that lack project management delivery systems; they are prone to miss targets and overspend. It noted that 67% of projects fail because project management is undervalued; 44% of all managers do not believe in the importance of project management software; and 46% of firms place a high priority on project management. Also noted: Utilizing a good software program reduces failure by 10%, and scope creep by 17%.

More specifically, a PMI Learning Library article noted some reasons for project failure:

  1. Unclear goals and objectives
  2. Lack of resource planning
  3. Poor communication across the organization
  4. Inadequate stakeholder management
  5. Poorly defined project scope
  6. Inaccurate cost and time estimates
  7. Inadequate risk management
  8. Inexperienced project managers
  9. Unrealistic expectations

Maturing Systems
An HBR article suggests that poor project success rates are due to a low level of available mature systems. Many firms continue to rely on spreadsheets, slides and other applications that haven’t matured current practices. While the current tools are adequate in measuring project performance, they do not allow for the development of intelligent automation and collaboration across the portfolio of projects. The opportunity to apply AI to project management could improve the success ratio by a quantifiable 25%, or trillions of dollars of newly realized benefits for firms and society.

Gartner Inc. analysts predict that by 2030, AI software—driven by conversational AI, machine learning and robotic process automation for gathering data, reporting and tracking—will eliminate 80% of all project management office tasks. Gartner identifies project management disruption in six aspects:

  1. Better selection and prioritization
  2. Support for the project management office
  3. Improved, faster project definition, planning and reporting
  4. Virtual project assistants
  5. Advanced testing systems and software
  6. A new role for the project manager

PwC envisions AI-enabled project management software will improve a project leader’s decision-making process across the following five key areas crucial to success:

  1. Business insights improvements by filtering better data for relevant knowledge
  2. Risk management assessing scenarios that offer mitigation strategies
  3. Human capital in allocating resources more appropriately to meet the business priorities
  4. Integrating various technologies and specialists to improve project outcomes
  5. Active assistance by enhancing administrative tasks and stakeholder progress communications

PwC posits the advancements in project management software are an opportunity for firms and leaders that are most ready to take advantage of this disruption and reap the rewards.

PM Competence
PMI’s Project Manager Competency Development (PMCD) Framework provides an assessment and development of a project manager’s competence. It is based on the premise that competencies have a direct effect on performance. A project manager’s competence can be categorized in terms of project management knowledge, project management performance and their accomplishments, and personal competency in performing the project activities and personality characteristics. This combination is the stated success criteria for a competent project manager.

AI’s capability to assess disparate sources of big data to obtain actionable insights arms project managers with improved decision-making competence throughout the project lifecycle. However, a challenge noted by PwC’s recent analysis of OECD data (covering 200,000 jobs in 29 countries) warns that AI’s job displacement effect will automate 30% of jobs involving administrative manual tasks by the mid-2030s. This indicates a clear need to upskill project manager competence in order to thrive in the future.

In order to succeed, a firm’s culture of adaptability and lifelong learning is a cornerstone for shifting today’s project management roles into the future. They will need to expand competence in soft skills, business and management skills, technical and digital skills—all working in concert with each other.

IAPM states project managers will face fundamental changes over the next 10 years with job descriptions and roles. It suggests AI will make logical analysis and decisions, allowing the PM to focus their main area of responsibility on creativity, resolving conflicts, and innovation.

Lastly, with any transformation or disruption, one must consider the actions and obstacles—whether financial, management support, or workforce ability—to embrace and enact change. Here are some key considerations to reflect on:

  1. Does your firm value project management?
  2. Is your firm a quick adopter of intelligence-based project software?
  3. Will your firm invest in your competence development?

Post your thoughts in the comments!

References

  1. PMI: Project Management Competency Development Framework—Second Edition
  2. PMI: Why do projects really fail?
  3. HBR: How AI Will Transform Project Management
  4. Gartner Says 80 Percent of Today’s Project Management Tasks Will Be Eliminated by 2030 as Artificial Intelligence Takes Over
  5. IPAM: Will project managers soon be replaced by AI?
  6. PWC: A Virtual Partnership? How Artificial Intelligence will disrupt Project Management and change the role of Project Managers
  7. Proofhub: Top 10 Reasons Why Projects Fail (And How to Solve Them)
Posted by Peter Tarhanidis on: August 22, 2023 10:57 AM | Permalink | Comments (14)

Supercharging an Organization’s Performance to Achieve its Mission

By Peter Tarhanidis, Ph.D.

There is a dramatic increase in the strategies corporations implement to meet the needs of their stakeholders. Driving value from all parts of an organization and its functions may seem like repetitive exercises—and even feel more like a medieval gauntlet with only a few successful programs. HBR (2021) wrote that by 2027, about 88 million people will be working in project management—with economic activity reaching $20 trillion USD. Also noted: Only 35% of projects are successful, leaving immense waste of resources.

There are many reasons projects fail. HBR (2021) states of the 70% of failed projects, and after exhaustive root-cause analysis across all industries, one can identify common themes such as undervaluing project management skills and methods, and poor performance. Yet organizations that apply project management methods recognized their performance had a 2.5 more times chance to be successful, and organizations can waste 28 times less resources. As such, when applied, the implementation of PM methods works.

Yet in a world filled with a variety of project taxonomies, many organizational boards are now contemplating the need to implement environmental, social and corporate governance (ESG) and corporate social responsibility (CSR) programs. Forbes states the benefits of ESG and CSR initiatives include:

  1. Advancing organizational culture, empowering staff to do social good, and welcoming diversity.
  2. Encouraging partners and investors who are interested in long-run strategy to manage risks and opportunities by emphasizing the organization’s ethics.
  3. Raising an organization’s staff confidence and productivity, creating a workplace that achieves the business mission.

Therefore, to ensure success for ESG and CSR programs, an organization’s top leaders need to prioritize and align across all the organization’s businesses. Leaders can use the balanced scorecard to achieve this alignment, and can extend its use across the entire project portfolio.

This theory was developed by Kaplan and Norton, which state the balanced scorecard method converts the organization’s strategy into performance objectives, measures, targets and initiatives. Linking the concept of cause and effect, the balanced scorecard covers four perspectives:

  1. Customer: How do customers see us?
  2. Internal: What must we excel at?
  3. Innovation and learning: Can we continue to improve and create value?
  4. Financial: How do we look to shareholders?

Marr (N.B.) reported over 50% of companies have used this approach in the United States, the United Kingdom, Northern Europe and Japan. One clear benefit has been to align the organization’s structure to achieve its strategic goals.

In conclusion, applying project management methods and aligning an organization’s performance through the balanced scorecard can unlock ESG and CSR benefits that can supercharge a company’s efforts to achieve its mission.

References

  1. HBR: The Project Economy Has Arrived
  2. HBR: The Balanced Scorecard—Measures that Drive Performance
  3. Project Management Statistics: Trends and Common Mistakes in 2023
  4. Forbes: Three Reasons Why CSR And ESG Matter to Businesses
  5. Balanced Scorecard: How Many Companies Use This To
Posted by Peter Tarhanidis on: June 14, 2023 04:12 PM | Permalink | Comments (3)

AI To Disrupt Project Management

By Peter Tarhanidis, PhD

Technology has demonstrated tremendous benefits and efficiencies (many of them unstated) over time. The technology lifecyle enhancements that started with our initial computers, software programs and the internet of the past have given way to the modern-day cloud, Big Data and artificial intelligence.

Throughout this maturing landscape, technology has affected all industries—especially how we collaborate. According to Peng (2021), here are some key impacts to consider:

  • Digital transformations spending will exceed an estimated $2.39 trillion by 2024.
  • Collaborative tools and technologies increased operational efficiency by 131%.
  • Technology will displace an estimated 85 million jobs globally by 2025.
  • AI augmentation will increase global worker productivity hours to an estimated 6.2 billion hours.

Project management has benefitted from the overall technology lifecycle, either by implementing aspects of it or by being a user of its collaboration outputs. Yet project managers are at the doorstep of being part of the next wave of AI disruption.

What a PM organization must consider is the methods and concepts used in managing past programs and become proactive in shifting to an AI-enabled PM organization. There is no doubt that the role of PMs and our methodology will be augmented with AI-enabled assistance.

PwC identified five areas of AI disruption and decision making in project management:

  1. Business insights: Filter data to gain actionable perceptions
  2. Risk management: Develop the ability to run multiple risk scenarios and outcomes
  3. Human capital: Optimize teams and leverage staff skills or new areas of training
  4. Action-taker: Provide analysis and optimization of schedules and staffing needs
  5. Active assistant: Augment the collection process of information to generate progress reports

To prepare for these changes, project managers should:

  • Invest in data sciences and digital skill sets
  • Create a culture that adopts digital disruption
  • Enable the use of digital tools and approaches to limit manual efforts and drive value-added work.

In order for these changes to emerge, there are a few considerations that may hold one back from the changes—such as organizational readiness, employee skills assessments, and the state of technical tools.

PwC outlines a change approach to assist in the transition that relies on updating project management strategy, leveraging technology investments, integrating digital and AI, and a comprehensive communication plan to generate awareness through adoption by the future project management workforce.

What other approaches have you used—or should be considered—to manage AI disruption in project management?

Reference:

  1. https://www.pwc.com/m1/en/publications/documents/virtual-partnership-artificial-ntelligence-disrupt-project-management-change-role-project-managers-final.pdf
  2. https://writersblocklive.com/blog/technology-in-the-workplace-statistics/
Posted by Peter Tarhanidis on: January 07, 2022 10:00 AM | Permalink | Comments (11)

Plan for the Velocity of Change to Keep Increasing!

Plan for the velocity of change to keep increasing

By Peter Tarhanidis, Ph.D., M.B.A.

Today, developments in emerging technology, business processes and digital experiences are accelerating larger transformation initiatives. Moore’s Law means that we have access to exponentially better computing capabilities. Growth is further fueled by technologies such as supercomputers, artificial intelligence, natural language processing, Internet of Things (IoT) and more across industries.

Emerging Tech
The global IT industry is valued at $5.3 trillion in 2020 and is poised to grow 6.2 percent by 2021, according to tech market research firm IDC. Emerging technology like augmented reality and robotics will make up an increasing share of that growth.

Business Process Maturity
Organizations are improving the maturity of their business processes. They’re doing this by automating tasks, eliminating them, improving performance or finding the lowest-cost way to perform a task. Organizations are connecting with experts to collaborate across a wider network of colleagues. This enables strategies to be integrated across the value chain to quickly drive business outcomes.

According to market research group IMARC, automation and the IoT are driving growth in business process management (BPM); the BPM market is expected to grow at a 10 percent compound annual growth rate between 2020 and 2025.

Customer Experience
In addition, having a formidable customer experience strategy can make the difference between customers choosing your brand or your competitors in 2020. That’s according to Core dna, a digital experience platform vendor.

Customer experience is redefining business processes and digitizing the consumption model to increase brand equity. Gartner reports that among marketing leaders who are responsible for customer experience, 81 percent say their companies will largely compete on customer experience in two years. However, only 22 percent have developed experiences that exceed customer expectations.

Economic Forces
Lastly, the potential for cash flow growth remains high in 2020, despite economic risks, according to the U.S. Corporate Credit Outlook 2020. This will likely lead to capital investments and a fair portion of companies funding transformational projects.

The Way Forward
While transformations have evolved, they encapsulate the way we think and operate. Old methods may seem encumbering and administratively difficult, creating bureaucracy and delays in decision making. The challenge is the velocity of change, which is very disruptive to organizations.

I’ve developed a few guidelines to help navigate this change:

  • Work with an agile mindset.
  • Fail often and fast to ultimately filter out winning initiatives.
  • Define the cultural attributes that propel staff and colleagues to succeed on their endeavors.

Change is now inherent and pervasive in the annual planning process for organizations. Given that, I like to ask: What is the plan to prepare staff and colleagues to compete in this hyper-transformation age?

What observations have you made to keep up with this new era’s velocity of change?

Posted by Peter Tarhanidis on: February 13, 2020 04:31 PM | Permalink | Comments (4)

Machine Learning Isn’t Magic

By Christian Bisson, PMP

Machine learning is one of today’s hottest tech topics.

It’s essentially a type of artificial intelligence (AI) in which you give your software the ability to “learn” based on data. For example, you probably notice how YouTube, Netflix, Amazon and many other companies suggests videos or products you should check out. These suggestions are based on your previous online actions, or those of other people deemed “similar” to you.

For some time now I’ve been working on projects that involve this technology. We often have clients who want machine learning even though they do not know if it’s even relevant to them. Since “everyone is doing it,” they want to do it too.

Calibrating a project sponsor’s expectations is often a good idea. While the automated services generated through machine learning may seem magical, getting to that point involves challenges—and a lot of work.

1. It needs quality data.

The machine will learn using the data it has being given—that data is the crucial starting point. The data that’s available is what drives how the machine will evolve and what added value machine learning can bring to your project/product. For example, if you are trying to teach the machine to recognize vehicles on images it scans, and all you can teach it with are images of small cars, you are not set up for success. You need a better variety of images.

The machine’s ability to learn is directly tied to the quality of the data it encounters.

2. It needs lots of data.

Once you have quality data, you need it in high quantities. If you can only provide the machine with the website behaviors of, say, hundreds of users per month, don’t expect it to have enough information to be able to recommend the best products based on user trends. Its sample will be too little to be able to be accurate.

3. It needs to be tested continually.

Once you have the necessary data, the journey is not over. The machine may learn on its own, but it’s learning based on how it was built and with the data it’s being fed. There is always room for improvement.

4. It’s costly.

As amazing as machine learning is, it is not cheap. So keep an eye on your project’s budget. Machine learning experts can command high salaries, and there is a lot of effort involved with researching the best approach—creating the models, training them, testing them, etc. Make sure the ROI is worth it.

Have you had a chance to work on a project involving machine learning? What challenges have you faced?

Posted by Christian Bisson on: July 14, 2018 08:59 AM | Permalink | Comments (12)
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