1) Capturing and sharing lessons learned from past projects is challenging with traditional methods. Lessons are often lost once projects end and teams disperse to new work. 2) An AI/ML system could automatically capture and codify lessons from project data to provide knowledge continuity across projects. This helps prevent the same problems from reoccurring. 3) Providing easy access to insights from past similar projects could help project managers address challenges more effectively than relying only on their own experience.

1. Seven project management experts on how people
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2. 2
INTRODUCTION
Bringing People and Data Closer Together
Research shows fewer than 10 percent of complex projects finish on time and on budget. This finding
suggests that standard approaches to project management do not work well, yet the art of managing projects
is as old as civilization itself. Why do projects and business processes so often fail to deliver as planned? Does
it have to be that way?
With the generous support of Sharktower, we set out to learn more about the challenges project managers
face and how they overcome them. We asked seven seasoned experts the following questions:
• Which challenges make it difficult to keep a complex project on track?
• How do you spot potential problems before they occur?
• How do you quickly obtain and apply insights from project interventions?
• How do you balance resource allocation with business value and desired outcomes?
Spoiler alert: The answers depend on what you know and how you apply what you know. In today’s data-driven
world, artificial intelligence is becoming the key to bringing data, insight, and decision-makers together in ways
that strengthen outcomes.
The experts have much to say about how technology changes the way information is applied in complex
projects. I see a lot of wisdom in these pages about how people and advanced technology empower each
other. I hope you agree.
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Editor

3. 3
People who deliver projects deliver the future. But no matter how capable, experienced or resilient
those people are, they are limited by the processes and data available to them. For over 20 years, I
was one of those people, battling to manage and control complex, multi-million dollar projects with
nothing more than spreadsheets and post-it note boards. And that’s why I co-founded Sharktower.
While Marketing and Sales teams have had access to automated intelligent systems for decades,
project management has been left behind, still relying on out-of-date control methods, wasteful
manual reporting and siloed data. The very function that’s expected to deliver the future has found
itself stuck in the past.
AI and machine learning are the game-changers. Powerful software like Sharktower blends the
collaboration and flexibility of leading work management tools with embedded delivery intelligence
that gives decision-makers better oversight of change across their entire business.
Project management software will never fully replace great project professionals. But having
access to the right, usable data and tools enables project managers to become true value
managers. People who can drive change, not just deliver it.
When intelligent people can embrace intelligent systems, it creates businesses that are more agile,
more scalable, and ultimately more successful.
FOREWORD
Like a shark tower at the beach is
used to spot danger on the horizon,
Sharktower’s AI-driven software
identifies issues and risks in project
delivery.
This intelligent project management
software gives businesses clarity,
transparency and control in one
place, and means work can be
planned, managed and tracked
visually in real-time, and viewed
across the whole business.
Under the surface, Sharktower’s
machine learning models analyse
data to highlight potential problems
before they happen, showing project
health, slippage and team sentiment
in a truly objective way.
Change is complex. How you deliver
it doesn’t have to be.
Craig Mackay,
CEO and Co-founder of Sharktower

4. DAVID PORTER
Endeavour Programme
Managing Director
pg. 6
KEN CREGAN‌
Unum
Head of Innovation
pg. 20
CLAUDIO TRUZZI
Université libre de Bruxelles
Technology Transfer
Professional - Digital Innovation
pg. 14
PETER TAYLOR
The Lazy Project Manager Ltd
Speaker, Trainer, Consultant
and Coach
pg. 23
MEET OUR EXPERTS
CHRISTINA GRITZAPI
Eurobank
Head of Group Web Sites, PMO
& Creative Hub
pg. 9
LINDSAY SCOTT
PMO LEARNING LIMITED
Director
pg. 11
SUSIE PALMER
University of Northampton Stu-
dents’ Union
Chief Executive Officer
pg. 17

5. Project management tools
for the future you're delivering
You can’t deliver the future with old-school spreadsheets and passive programs.
For a personalised demo of future-focused project management,
go to www.sharktower.com/demo

6. 6
“Transparency is a rare commodity in a
complex project.”
AI Can Spot Problems Earlier and Drive Better Decisions
In project management, we spend a lot of time and money forecasting
what we think is going to happen. Predicting a future outcome and spotting
problems are fundamental to the process because those insights drive
decisions about whether interventions are needed.
Traditionally, however, we do not do a good job of project forecasting.
One study looked at 3,000 projects and found that the on-time, on-budget
completion rate was much less than 10 per cent*. That finding suggests that
whatever we are doing now to make predictions and subsequent decisions
doesn’t work well, and doesn’t seem to be very reliable.
A deeper analysis of project data shows that if your project is going well,
which means that it is coming in on time and on budget, you know that by the
time you are a third of the way through the project. If your project is not going
well, you won’t know that until two-thirds of the way through the project. One
reason for the delay in recognising that a project is in trouble is that humans
are optimistically biased. If project managers find that their project isn’t going
well, they are inclined to underestimate the problem. They also try to fix it
without bothering senior management. After all, that is what project managers
are hired to do. Also, particularly in complex projects where time and cost
David is the founder of Endeavour
Programme and the driving force behind
the business and its global strategy.
He is an experienced leader in project
development and delivery across a
wide range of industries. David is a
strong advocate for “Data Driven Project
Management” which brings together the
best of project management practice
and technologies such as Artificial
Intelligence.
David Porter, Endeavour Programme,
Managing Director
*Flyvbjerg & Budzier (2019), https://www.sbs.ox.ac.uk/oxford-answers/why-megaprojects-systemically-fail-and-what-can-be-done-about-it

7. 7
overruns occur frequently, project managers often hoard and manipulate data to
present the most favourable perspective on the project performance. Transparency
is a rare commodity in a complex project.
Artificial intelligence (AI) and machine learning (ML) offer to make a fundamental
difference in how projects run. AI does not suffer from optimism bias and it can
derive meaning from complex data faster and at a far greater scale than humans
working with traditional project management software tools. As a result, AI can
deliver answers more accurately, faster, and earlier than current methods. In
practice, we are seeing AI tools turn out actionable information 35 per cent sooner
than traditional systems. Putting that finding in the context of a twelve-month
project, you have insights four months earlier than you would normally have. If you
know four months earlier that a problem is coming, you can do something about it
that may save money and ensure a better outcome.
Any process that has the visibility and analytical power to transparently identify
future outcomes as well as the ability to escalate through layers of management
becomes a decision driver. AI can do more than generate early warnings, however.
It also helps managers make the right intervention decisions. One approach is
to run multiple what-if scenarios. Project teams already do this when a serious
problem arises. Managers pull people off the project to work out what they could
“AI does not suffer from optimism bias; it makes
meaning of data faster and at a far greater scale
than humans working with traditional project
management software tools.”

8. 8
do to rectify an impending problem. The time and resources needed to perform
this task limits the possibilities they can consider, as does the limit of the
collected wisdom of the professionals in the room. An AI solution can quickly
work through hundreds of scenarios to reveal the top few for deeper analysis.
Another way AI helps decision-makers is by analysing project data and outcomes
from many previous projects, and then quickly linking lessons learned from those
projects to the problem at hand.
The effectiveness of project management AI depends on the types and quantity
of data such a tool consumes. All relevant project data is fair game, including
project reports, communications, documents, and incidental data that could
affect the project, such as weather or news events. The more data AI systems
have, the better they become at prediction and forecasting. They continuously
improve, which has big implications for the project management world, where
project management wisdom is often lost whenever a key manager retires or
moves on.
There are many views on how, how much, and when AI will start to have major
impacts on the way projects are managed. Despite this, no one knows the answer
to these questions. It is inevitable however, that AI will be a driver of major
change. Research from Germany indicates that one third of all work in the OECD
is conducted in a project format, and research from Oxford University indicates
that over 50 per cent of projects will exceed their budgets. That’s 15 trillion dollars
per year of work of which well over half will experience cost overruns. It is an
industry that needs disruption.
David Porter, Endeavour Programme,
Managing Director
1
2
Any process that has the
visibility and analytical power
to transparently identify future
outcomes and the ability to
escalate through layers of
management becomes a
decision driver.
The effectiveness of project
management AI depends on
the types and quantity of data
the AI system consumes. The
more data AI systems have,
the better they become. They
continuously improve.
Key Points

9. 9
“The ability to identify problems and address them as
quickly as possible depends on the team’s knowledge
and experience.”
Close Cooperation amongst Team Members Is Key
Complex projects typically have many people involved. For instance, when we
do a website project, we have our own team of subject matter experts, which
includes web managers, designers, copywriters, and analytics experts. At
least one but often more business units will be involved, along with marketing,
the information technology department, and any vendor assisting with
implementation. We also include our internal security team, which makes sure
that the deliverable is safe and ready to go public.
With so many people involved, challenges are inevitable. One challenge is
scope management. Stakeholders have their vision of what the scope should
be, and that vision translates into project requirements. Often, however, when
the project is built and goes back to stakeholders for review, they decide to
make changes. Therefore, it is essential that project managers stay close to
stakeholders, keep them informed, and show them the results of each project
phase.
Another challenge is resource and budget constraints, which require planning
and adjusting priorities to accommodate the organisation’s strategy. Teams
must work closely together on the different phases of the project. Managers
must notify everyone of problems as early as possible so that they can help
the project meet its goals. Close cooperation amongst team members is key.
Christina Gritzapi is a senior business
and IT executive in the Banking
Sector. With 20+ years of experience
in managing large & strategic IT
projects, she currently serves as
the Head of Group Web Sites at
ERB Eurobank. She holds an MSc
in Information Systems and a BSc
in Computer Science. Over the last
three years, she has participated
actively in the organization’s digital
transformation journey.
Christina Gritzapi, Eurobank,
Head of Group Web Sites, PMO &
Creative Hub

10. 10
When project managers spot a risk, they should announce the finding immediately
so that the teams can address it with minimum impact on the project. Many
project managers make the mistake of not revealing problems or risks because
they fear others will think they are bad project managers. If you speak loudly about
a problem, then it will be solved. If you hide it, then it could lead to the project’s
failure.
People must also learn from problems and mistakes and incorporate that learning
into project workflows. The project manager and subject matter experts are the
knowledge base for project work. The ability to identify problems and address
them as quickly as possible depends on the team’s knowledge and experience.
Team members must be able to tap into available knowledge resources, whether
within the team or through consultants.
This is where artificial intelligence and machine learning are useful in project
management. People make mistakes; they cannot catch everything. If a machine
learning–based tool can analyse project data and identify patterns, that tool will
be able to spot risks. These technologies can take on some of the routine project
tasks that people perform, helping the project management processes evolve, but
projects still need key people in key positions.
Christina Gritzapi, Eurobank,
Head of Group Web Sites, PMO & Creative Hub
In a complex project with
many stakeholders, managing
project scope can be a
challenge. It is essential to
stay close to stakeholders,
keep them informed, and
show them the results of each
project phase.
Machine learning–based
tools can take on some of
the routine project tasks that
people perform, but projects
will always need key people in
key positions.
1
2
Key Points

11. 11
“Useful lessons learned are easily lost. For many project
managers, the lessons they learn come from their own
experiences rather than a database of past projects.”
Provide Knowledge Continuity across Projects
The Project Management community periodically discusses challenge areas in
project management. Invariably, two challenges crop] up: poor risk management
and subpar project governance. Why are we not realising benefits and values
from the two things we as project managers are delivering? Many project
management offices (PMOs) would answer that question by talking about how
we learn from projects or how we fail to pass on the knowledge we gain.
The traditional approach is to wait until the end of the project, and then conduct
a detailed post mortem that digs into what went badly and what could have
been done better. As project management and governance have become more
agile, we have shifted to conducting retrospectives along the way. What has not
changed, however, is that when a certain issue or challenge has been identified
and we have learned a lesson from it, there is no easy way to spread that
knowledge across the organisation to prevent that problem happening again.
The traditional lessons-learned process has people sitting in a room talking
about the issues and how they could make changes. Then, the project is done,
and it’s back to business as usual. The project team disperses to join new
Lindsay Scott is Director of PMO
Learning, a training company that
focuses on great courses for PMO
practitioners helping them to learn and
grow in their roles. She also runs PMO
Flashmob, the learning and networking
group for PMO and the annual PMO
Conference. She loves PMO!
Lindsay Scott, PMO LEARNING
LIMITED, Director

12. 12
projects. As a project manager, you have finished with that project, you have done
a post mortem, and you have come up with some good ideas, but to whom do you
give that knowledge? Individual projects tend to be siloed. The PMO is supposed to
straddle all projects, but many businesses do not have a PMO.
A traditional approach to capturing lessons learned is to frame that information in
terms of project process and include it in a project report that would eventually go
into a project database. Then, ideally, when project managers start new projects,
they can search the database for elements that may be similar to what they
think they will face going forward. This ridiculously archaic process requires a
project manager to manually wade through many reports. The reality is that it is a
burdensome process that makes historical analysis difficult. Useful lessons learned
are easily lost. For many project managers, the lessons they learn come from their
own experiences rather than a database of past projects.
Enter artificial intelligence and machine learning, and now you have a means of
automatically capturing and codifying lessons learned. The promise of such a
solution is that it becomes a way to provide knowledge continuity across projects.
An AI-based tool would do what a great PMO would do: review status reports across
all projects, compare them for trends, and look for problems. The machine learning
algorithm would do what a good PMO does naturally but in the context of all the
“Enter artificial intelligence and machine learning,
and now you have a means of automatically
capturing and codifying lessons learned.”

13. 13
historical data of past projects. It can quickly pull up just the parts of that historical
information that are relevant to the current situation. The result is a path to faster
decision-making because the tool has access to more information. It also makes
project modeling possible, giving an organisation a forward view of what taking
certain decisions may mean and facilitating better decision-making that affects
project budgets.
Lindsay Scott, PMO LEARNING LIMITED,
Director
The traditional approach to
capturing lessons learned
manually entering that data into
a project database that project
managers must manually. It
is an archaic process in which
many lessons learned are lost.
Project management tools with
AI capabilities should be able
to automatically capture project
information, provide relevant
insights from historical project
data, and provide decision
modeling capabilities.
1
2
Key Points

14. 14
“When faced with complex situations or large amounts
of data, people often filter information through biases
that prevent them from deriving the most business
value from that data.”
AI Systems Level the Playing Field for Decision-Makers
In today’s data-driven business process environment, achieving desired
outcomes depends on the ability to process data, derive insights from that
data, and take the right actions and decisions based on those insights. This
affects the way people manage processes, and it can significantly affect
project outcomes.
For example, projects often take longer than originally planned because people
underestimate the time it takes to do things. The reason they underestimate
time is because many interruptions occur in the course of a complex project
or business process. Some of these interruptions may be unexpected
developments in the project; others may be incidental. The fact is, however,
that people are not good at multitasking.
People also have limited ability to evaluate and act on business processes and
project data. When faced with complex situations or large amounts of data,
people often filter information through biases that prevent them from deriving
the most business value from that data. Common biases include:
• Confirmation bias. People subconsciously select the information that
confirms their beliefs and strategically ignore data that challenges their
beliefs.
Senior professional in technology
development, transfer and
implementation with 30+ years of
experience in all aspects of digital
innovation across multiple applications.
Earlier experience includes C-level
roles for high-impact, high potential
technology start-ups. At present,
Claudio is Head of icity.brussels and
SmartCampus at ULB University in
Brussels, Belgium. He holds MBA,
PhD and MSc degrees from Solvay
(B), Turin and Bologna (I) Universities,
respectively.
Claudio Truzzi, Université libre
de Bruxelles, Technology Transfer
Professional - Digital Innovation

15. 15
• Reality denial. People build an understanding of a situation, and then use
various techniques to rationalise away contradictory information.
• Availability bias. Not having time to research a situation in depth, people
tend to fall back on the most recent information they have.
• Experience bias. Experienced people confronting a new problem may see
similarities to other situations they have dealt with in the past. They apply
an old solution pattern similar to what they are seeing without taking the
time to determine whether new data suggests taking a different action or an
alternate approach.
All these dynamics are at play in decision rooms. The result is that too many
decisions are made based on perceptions and the latest information available to
the decision-maker.
Adding artificial intelligence (AI) and machine learning to process management
systems enables professionals to automate repetitive processes, follow up
automatically on uncompleted tasks in the process, and see the overall picture of
a process alongside performance metrics. These systems provide dashboards
not just to the managers but to everyone involved in the process so that they can
easily visualise what is happening and never lose track of important information.
These tools enable us to overcome our own limitations.
“These systems provide dashboards not just to the
managers but to everyone involved in the process so
that they can easily visualise what is happening and
never lose track of important information.”

16. 16
AI-based systems do not just make life easier for project managers and those
engaged in project work. It is true that they can reduce the number of meetings
and automate repetitive tasks, but for now, at least, someone still has to make
the critical decision. These systems provide a clear visualisation of key data for
everyone in the decision room. leveling the playing field so that everyone can
objectively assess the risks and rewards of a decision based on real data, not
personal perceptions or biases.
Claudio Truzzi, Université libre de Bruxelles,
Technology Transfer Professional - Digital Innovation
1
2
Many different human biases
are at play in decision rooms.
The result is that too many
decisions are made based
on perceptions and the last
information available to the
decision-maker.
AI-based systems provide
clear visualisations of key
data that level the playing
field for everyone involved in
project decisions. Decisions
can be based on real data,
not personal perceptions or
biases.
Key Points

17. 17
“A project manager’s ability to make good decisions
is only as good as what he or she knows. What a
project manager knows is only as good as the data
available.”
To Improve Project Outcomes, Bring People and Data
Closer Together
A significant challenge to effectively managing projects is how to overcome
the disconnect between expectation and reality that grows out of mismatches
between an organisation’s capability to deliver and its people.
People are at the heart of project delivery. Project delivery, in turn, depends on
those people having the right skills, capacity, information, freedom, and time
to fulfill project demand. Projects always start with the best intentions, with
everybody pulling in the same direction. Then, new ideas come up, hindsight
kicks in, and relationships begin to develop amongst people. Often, projects
begin to spiral out of control because people do not know how to draw insight
from relevant information. They do not know how to bring data close enough
to their decision-making process to benefit from it.
This ineffectual use of data has an impact on management’s ability to address
the problems that invariably arise throughout a project. Whether those
decisions are operational or concern resource allocation, many of them have
downstream ripple effects. The key to project managers making the right
I am trophy yielding change bod
with the ambition to affect how we
change as individuals and teams;
helping organisations be responsive
and respectful of the people inside
them. My change experience spans
construction, IT and strategy; giving
me both stories and skin in the game;
helping me to leverage varied histories,
diverse opportunities and unique
cultures to help us get real outcomes
that we be proud of.
Susie Palmer, University of
Northampton Students’ Union,
Chief Executive Officer

18. 18
decisions is knowing how to draw on their experience and ideas as well as the
information available to them so that they can intervene in timely and efficient
ways—ways that stick. A project manager’s ability to make good decisions is only
as good as what he or she knows. What a project manager knows is only as good
as the data available.
Good project managers have a lot of experience and a wealth of hindsight that
enable them to spot trends and see potential problems early. New technology is
beginning to provide data pooling that makes trusted data available to project
decision-makers. This technology brings to bear a wealth of hindsight from other
people’s experiences. It enables managers to project outcomes, raise queries
about risks early in the project cycle, look at likely sources of cash over-spends,
and plan for staffing issues. If you can start a project knowing where the risks and
problems are likely to arise, that would be phenomenal.
Good decision-making depends on a combination of data and evidence. Many
project decisions are based on what people infer or imply without finding evidence
for whether their assumptions are true. That is not because the people are wrong
or the data is wrong. It’s because the data and the people are not close enough
“If you can start a project knowing where the risks
and problems are likely to arise, that would be
phenomenal.”

19. 19
together. You can have brilliant people and brilliant data, but if nobody’s gaining
insight by bringing those two worlds together, then there is limited value in either.
Building reflection and hindsight into project decision-making results in a brilliant
boost to project performance. There is massive value in understanding the ripple
effects of a decision before you make that decision.
Project managers can draw from many tools to track project information and
support project operations—everything from simple spreadsheets to artificial
intelligence–driven tools that perform trend analysis, triangulate outcomes, and
tap historical project data. Which tools you choose depends on your understanding
of the best fit for your organisation. If you are interested in growth, development,
and reflection and you want to draw insight and generate improvements for future
delivery, you will need more robust tools than those designed to help you deliver a
single project instance. In addition, buying an advanced tool is not in itself the goal.
You must buy a tool, shape it, learn to use it, and then employ it to drive success.
Susie Palmer, University of Northampton Students’ Union,
Chief Executive Officer
The key to making good
decisions is knowing how to
draw on your experience, ideas,
and the information available to
you so that you can intervene in
timely, efficient ways—ways that
stick.
If you are interested in growth,
development, and reflection
and you want to improve
future deliveries, you will need
more robust tools than those
designed to help you deliver a
single project.
1
2
Key Points

20. 20
“Resourcing decisions force managers to balance risks
and benefits …. and the effects those decisions will
have on other processes and projects.”
Meet Continuous Change with Better Resourcing
Decisions
One of the biggest challenges business managers face is deciding where to
focus key projects, budgets, and time to best align with the business’s strategic
position in the market. These decisions are difficult because resources are
always limited and business realities are always changing.
Resourcing decisions force managers to balance risks and benefits. When
resources are limited, managers have to evaluate whether a particular need
can be addressed in the short term or will require a longer-term resource
commitment that could evolve into scope creep. They must also consider
the effects those decisions will have on other processes and projects. Will a
particular action solve multiple problems, or will it create new ones?
Answering these questions requires continuous discussion about risk
management, resource management, and scope management. It requires
looking forward and anticipating change, something that must be done with
the delivery teams and key stakeholders. Even then, however, the unexpected
happens. When they do, detailed impact analysis and planning across project
portfolios and across the business are needed.
In over 20 years as a consultant and
in industry, Ken has worked in multiple
countries and industries delivering large
digital transformation iniaitives from
strategy through to implementation.
Success is in part about how you
respond to new insight and feedback
to your initiatives and resource
appropriately.
Ken Cregan, Unum,
Head of Innovation

21. 21
New tools are emerging that monitor and evaluate many aspects of a project. Daily,
delivery teams engage with systems. As data accumulates, project managers
can begin to see trends in how time and money are being spent. The tools
provide visibility into the accuracy of planning. If a problem arises, managers can
immediately see how key resources are allocated and make decisions to address
the problem. These tools provide real-time insight into which projects or initiatives
are working well and have a good chance of success. They can also show which are
going to deliver the biggest return. These systems look across a project portfolio and
its total resourcing pool to see where resources are allocated, down to the individual
team members.
With additional artificial intelligence capabilities, these systems are just beginning
to be capable of intelligent automation. They can help leadership understand the
effectiveness of specific projects by comparing them against metrics for similar
projects. These comparisons may be based on any number of factors, such as
speed of delivery, quality of output, team sentiment, and return on investment. To
maximise their effectiveness, these tools require a database of historical project
data with which to make comparisons based on key metrics, and then create
forecasts based on current information. They also enable managers to do project
modeling based on complexities or the likelihood of problems arising. The systems
can also flag potential problems and provide alerts.
“[New tools] provide real-time insight into which
projects or initiatives are working well and have a
good chance of success. They can also show which
are going to deliver the biggest return.”

22. 22
It’s important to recognize that these systems do not replace people. Every project
still needs a person who understands the organisation, the stakeholders, the delivery
teams and their capabilities, and the business implications of each change. The
tools will help people identify and predict these changes, and then manage change
in a controlled way that is acceptable to the business and everyone involved.
Change is continuous, and the need for better insights to make better resourcing
decisions never goes away. Businesses that have good project governance
processes in place stand to benefit most from technologies that help them see why
changes are happening, evaluate options to respond to those changes, and see the
trade-offs. With that understanding of possible impacts, people can better plan,
adapt, and carry on.
Project resourcing decisions
are difficult because
resources are always limited
and business realities are
always changing.
With additional artificial
intelligence capabilities and
a database of historical
project data, these systems
can deliver a level of
intelligent automation that
enables project modeling
based on complexities or
the likelihood of problems
arising. These systems
can then alert managers to
potential problems based on
forecasting.
Ken Cregan, Unum,
Head of Innovation
1
2
Key Points

23. 23
“It is no longer just one person trying to understand
everything. Hundreds of people may be on a project
team, all with eyes and ears open and contributing to the
good of the project.”
Project Management Requires Real-Time Visibility
across the Team
Advanced technologies are changing every aspect of business operations,
and managing complex project portfolios is no exception. The old, top-
down, centralised governance approach to project management, where
communication is controlled and hierarchical, no longer works.
Complex projects with many related moving parts can make visibility by
one person almost impossible. Furthermore, it’s not practical to have people
continuously writing reports. Projects do not move at that pace anymore. Too
many people are involved. Projects require a regular flow of communication, not
include written reports that are out of date as soon as they are written.
The biggest challenge for project managers and project sponsors today is how
to provide visibility into what is happening on a project. The only way to provide
such visibility is through collaboration technology, because it is no longer just
one person trying to understand everything. Hundreds of people may be on a
project team, all with eyes and ears open and contributing to the good of the
project.
Speaker, Consultant, Trainer, Coach -
Peter is the author of the number 1
bestselling project management book
‘The Lazy Project Manager’, along with
books on Project Management, PMO
leadership, Executive Sponsorship,
Transformation Leadership, and
Speaking Skills.
He has delivered 400 lectures around
the world in over 25 countries and has
been described as ‘perhaps the most
entertaining and inspiring speaker in the
project management world today’.
Peter Taylor, The Lazy Project
Manager Ltd, Speaker, Trainer,
Consultant and Coach

24. 24
The key to staying on top of complex, fast-paced projects is enabling the team
to exercise its responsibility to deliver project outcomes, which depends on trust,
valued input from team members, and regular updates and communication.
For that, projects require collaboration tools that facilitate communications and
provide dashboards so that team members can see what is happening. Frequent,
regular observations; inputs; and pulse checks throughout the course of a project
are imperative. These elements are not the same as milestones, audits, or regular
reviews. Today’s project management requires continuous short, sharp, checks on
whether things are good or not good and whether changes are needed.
Those frequent checks not only keep team members and managers informed but
become the key to spotting problems early. Artificial intelligence (AI) and augmented
intelligence built into project management and collaboration tools are exciting
capabilities that can turn all those micro-checks into leading indicators of project
health. Dashboards are important, but they are lagging indicators. The future is in
tools that provide predictive analysis based on trending inputs from the team. Tools
are now available that track language patterns, which can indicate mood and feeling.
Growth is also occurring in project team performance management, which assesses
the alignment and interactions within project teams themselves.
“Imagine a changed world in which project
managers and project sponsors alike are rewarded
based on business benefits achieved rather than the
delivery of project outcomes.”

25. 25
These same tools help apply insights and make mid-course corrections more
easily. Predictive tools and alerts empower project team members to take
corrective actions rather than waiting on a project management review process
to make one big periodic change. The team now has the information it needs to
make incremental adjustments in real time. AI represents an evolution in project
management. Instead of the information just being there, the tools will be pushing
it out to the team and telling members why they should be looking at it. The tool
becomes a guidance system, pointing team members to the right information.
These capabilities are moving the focus of project management more towards
outcomes than processes. For instance, in addressing questions of resource
allocation, tools can help with impact analysis based on how different resource
allocations would affect project outcomes. As this trend continues, ideally, project
management will one day focus more on business benefit than process refinement.
Imagine a changed world in which project managers and project sponsors alike are
rewarded based on business benefits achieved rather than the delivery of project
outcomes.
Peter Taylor, The Lazy Project Manager Ltd,
Speaker, Trainer, Consultant and Coach
Predictive tools and alerts
empower project team
members to make incremental
corrective actions in real
time rather than waiting on a
project management review
process to make big periodic
changes.
AI represents an evolution
in project management in
which the tool does more
than simply consolidate
information into a dashboard.
The tool can now push alerts
and information out to the
team.
1
2
Key Points

26. Change
is complex.
Delivering
it doesn’t
have to be.
Project management is changing for
the better. For a demo of AI-driven
planning, predictive analytics and
real-time reporting, go to
www.sharktower.com/demo