You Ask, I Answer: The Difference Between SEO and Content Marketing?

Christopher Penn: In today’s episode a soft asks, Do you look at content marketing differently than a regular content marketing? Should all content be SEO oriented content? When would you make the distinction? That’s a good question. Today in today’s modern SEO environment with the way search engines use artificial intelligence and machine learning, there really isn’t a difference anymore. All content that you create should be about your key topics, it should be relevant, it should be timely, should be diverse in content type. It should have expertise, it should be from an authority in the space or be authoritative in nature, and it should be trustworthy. And those are the major factors that go into content marketing anyway. And it goes To SEO anyway, a few years ago, yes, there was a difference. As the old, very, very old joke of this one goes, an SEO expert walks into a pub bar Tavern and so on and so forth. And you ended up with a very stilted language that was trying to check the box on specific keywords of things. But again, as artificial intelligence has taken over the way search engines do their analysis. That no longer applies. Now search engines are looking for things that we would write naturally to each other. Right. So if we were talking about, you know, a bar, yes, you might use the word tavern, but you’re probably not going to use all the other words and you’re going to talk about your food, your drinks, your service, the atmosphere, specials, entertainment, all the things you would expect to see on a website about a bar. That’s today’s modern SEO, it is about What do the leading pages in a category like yours? have about the topic of your choice? What sort of words or even phrases? What topics Do you talk about? And how will and how thoroughly Do you discuss them that proves that you have that expertise, that you are an authority that your content is trustworthy in nature. Now, where you’re going to run into some trouble is that there are a lot, a lot of SEO practitioners out there who are still working on the very outdated knowledge of really anyone who has not brushed up on their, on their skills on what the tools do, and how search engines work if they’ve not been keeping current in the last six months. They’re really, really behind. When you hear people talking about oh yeah, modify the h1 tag and Put these words in bold anyway, right? Yeah, it’s about a decade old. And some of those practices don’t cause harm some of them do. But almost all of them will not help anymore compared to the, the modern basics of topic centered content creation, and the timeless, get more people to link to your page and talk about you and send you traffic. Those two things really are the bedrock of modern SEO. Now, in terms of other differences, if you are doing SEO focused, content marketing, you will spend more time probably on the research about the topics and the technology used to identify what word should be in your content then you would with just straight piece of content marketing, we just go in and just create something for the sake of creating it. Knowing what topics you should be creating, not knowing what should be in the page, knowing What similar pages that rank well how on them knowing what the category overall has that does take time. And it requires a lot of research because I was digging around yesterday one of the industry leading SEO tools and even there it’s it’s struggling struggling to keep up you can see that it’s not I typed in this keyword for a very, very broad topic and yeah, it should have come up with other things that didn’t attacted as an example, I typed in the coffeehouse and coffee shop. Now when I typed that in what words should be on the page? What words would you expect to be in that content will sure the coffee right? But this thing kept coming up with the coffee shop coffee shop near me coffee house near me, but at no point didn’t say espresso or lattes. or music available or seating area. And again, those are things that you would expect in a coffee shop business. But this SEO tool, it one of the industry leading ones, one of the biggest names out there, just couldn’t do it, it couldn’t find those related terms that were semantically related, but not exactly what the customer typed into the tool. So that’s where you’re going to run into the most difficulty is, when you’re working with these tools, they are not keeping up the way they should be. And you will have to invest more time investigating on your own, maybe even building some of your own software just just to get to the point of getting those keywords. Certainly interviewing your own subject matter experts to get the natural language that people use to describe something in your industry is is the easiest way to get some of those words. So Good question. Really, there’s a lot to unpack. And there’s a lot to do that you will have to do on your own because again, the tools are not there just yet, compared to the way that of course, search engines with multi billion dollar r&d development budgets can can do so. As always, please leave your comments in the comments box below. Subscribe to the YouTube channel and the newsletter, I’ll talk to you soon. Take care what helps solving your company’s data analytics and digital marketing problems. This is dot AI today and let us know how we can help you

You Ask, I Answer: Most Exciting 2020 Marketing Trend?

Christopher Penn: In today’s episode, Laura asks, What is the industry trend you’re most excited about in 2020? Well, the continued growth Unknown: and Christopher Penn: the practicality of what’s happening in artificial intelligence and machine learning and in marketing data science force. There’s three specific trends that I think are so exciting and have been exciting for quite some time. Number one, continued growth in natural language processing. 2018 was sort of a watershed year for a lot of natural language processing in 2019 brought some of the largest models available that we can use to process text, to understand it better to be able to in some cases, generated and that is stuff that I expect to see continue in 2020 not just at the cutting edge of research but at the ability for the average machine learning practitioner to access it and deploy it. So by that i mean it’s it’s relatively friendly, relatively easy to use it is not the incredibly challenging you know, super high tech stuff although that that stuff is exciting too. But for the average well versed practitioner in data science and machine learning to be able to pick up something in NLP and use it. Number two, there are some really amazing things happening in the world of audio with wavenet a few years back and now Mel net bunch of other technologies that are making it easier for machines to understand audio, particularly the spoken word and Then able to replicate it and synthesize it. And this is some amazing stuff again, there’s if you look at the milnet demo, you can listen to a machine synthetically generate voices, synthetic degenerate music. I think music composition is has come a long way and continues to accelerate. It’s not going to replace humans anytime soon. But it’s getting there. And it I would say it’s at the point now where AI can generate, like elevator music, right? stuff if you put it on in the elevator like no one would be offended. But certainly it’s not going to win any Grammys. But as with all things in, in machine learning, the technology improves, the models get better. And whereas two years ago, it was it sounded like something like your three year old would You’re hammering pots and pans with spoons. Now it sounds like elevated music. So expect in time for it to become a credible alternative for people who want access to music composition and don’t have the skills themselves can’t play an instrument but can direct or conducting an orchestra of machines. Number three, and this is a bit more esoteric, but there will be more and better pre trained models in 2020. We saw within the natural language processing field open AI is GPT to released its mega model, the 1.5 billion hyper parameter model in 2019. And that was a really big deal for that company because that was the model they were so concerned about falling into the wrong hands. And they said they didn’t see any evidence of its its use and partly because it’s such a beast try and get to us. But one of the major Overall meta trends and AI in the last couple of years has been, instead of generate your own models, pick up a pre trained model and then fine tune and expect to see more of that happening in music, image recognition, video, natural language processing, good old fashioned statistical computing all these things. We’re seeing more and better models for better just you pick it up and you work with it. Now, there are some challenges with that in the chat. One of the big challenges, of course, is that you are relying on a pre trained model and you you don’t know for sure how that model was trained. You after take on faith to some degree, the model was trained well, so that’s going to be a consideration. Which brings me to three trends I’m concerned about number one is interpreter ability and explain ability, the ability for machine learning and AI practitioners to explain what is the models are doing, which is really important and something that The industry is struggling with and will struggle with for some time. Partly because interpretability is so much more expensive than explain ability, but is the gold standard for being able to say, watch and see what the machine is doing any step of the process. Number two, the things I’m worried about is, is bias. And this goes hand in hand with interpretability. If we don’t know what the machine is doing, we don’t know if it’s doing something wrong. There have been many, many, many examples of machines making decisions that on the surface seem okay. But when you get inside the model, or when you see the post hoc results, you’re like, That’s not right. So bias is a major concern. And it’s one that the industry is making strides on. But the industry as a whole is not going fast enough. Not just fast enough to allay some of the fears that people have. And, and set aside the misconception that’s important. And number three, and by far the one I’m most concerned about in 2020, because it is an election year is the misuse or malicious use of artificial intelligence technology for things like deepfakes is the most popular cited example not the one that I think is probably the most prevalent. I honestly think that bots with minimal natural language processing capabilities are much bigger problem because there’s so much easier to scale deepfakes don’t scale well. Right. deepfakes require a lot of computational power. And yes, you can rent it for pennies on the dollar from like a Google Cloud or something. But to do so, then also makes you not anonymous, right? Because once you sign into something like when a big text clouds every single thing you do is tracked and can be identified but long for So in a lot of cases, if you’re doing something malicious, you need to be doing it in the dark away from the prying eyes of every major tech company ever. So things like, you know, mass armies of Twitter bots and Facebook bots and things are a much more practical application. And very easy, very cheap. And they have the ability thanks to the hyper partisan world that we live in, to really manipulate people. And it’s not the machines fault that humans are gullible and that humans like to have confirmation bias out the wazoo. But it’s the machines enable hostile actors to do more faster and better. And, at least in the context of the United States of America, our defenses have been largely dismantled the last couple years with the abolition of the cyber security Council and things like that. So we are in a case where AI can be maliciously used. And that’s very concerning to me as a practitioner because, again, we want people to trust this technology. If the technology is being used for malicious means really hard to build trust around it. So that’s sort of the opposite of is exciting trend. That’s the the most worrying trend but that’s what we have to look forward to in 2020. For marketing data science for machine learning for artificial intelligence, would love your thoughts, leave them in the comments box below. Subscribe to the YouTube channel on the newsletter, I’ll talk to you soon take care what help solving your company’s data analytics and digital marketing problems. This is Trust today and let us know how we can help you

Intelligent desk that promotes office worker health, wellness and productivity

When you think of an intelligent machine,
what comes to mind? Does it look like this? Or this? What
about this? If intelligence could be embedded in
everyday objects, what kinds of problems could we solve? Take the workplace. As
much of the economy shifts to office work, poor environmental quality is having
a direct effect on worker health. In the long term, these conditions can increase
the risk of obesity, musculoskeletal disorders, and even cancer.
Unfortunately, workers habits are deeply ingrained. So just giving them the option
to stand isn’t enough. But what if instead of making you sluggish and sick
your desk helped you feel and perform your best? What if it was tailored just
for you? That’s the goal of this intelligent workstation designed
collaboratively by engineers in Arup and researchers at USC. We’re starting with
building a smart desk and looking forward to the future of what would be a
intelligent workspace. Provide some sort of feedback to the user and sense what
the user is doing. How can we build intelligence to these desks so that they
can support more comfortable and more productive and healthier environments?
Together, the team is using the latest advances in sensing and machine learning
to transform the way we work. The project represents a new way of interacting with
machines, bringing together both cognition and embodiment. The mind and
behavior of both desk and user symbiotically co-evolve. The desk mind
works with the human mind and enables the desk motor functions to change the
human’s motor functions. So how does it work? There is a sort of
data hub that can be really different depending on which computer you use. And then
there is a sort of sensing and the communication hub which at the core of
it at the center of it is a Raspberry Pi and using Bluetooth and the Wi-Fi is
able to then connect to the building or app infrastructure. So we’re
constantly monitoring the user; physiological responses of a person as
well as the ambient environment around the user. The sensors that we typically
have are environmental sensors measuring for instance, temperature, humidity, air
pressure and but also a distance sensor so we use a distance sensor to check the
height of the desk but also we have a higher resolution cameras looking at
posture using machine learning to understand whether a posture is correct
or not. And then I would say the limit really is imagination and what kind of
availability of sensing technologies there is. To help users improve their behavior
without becoming intrusive or annoying, the desk has to understand when and how
to engage. Getting the balance right means building a relationship and the
key to that relationship is trust. We want to understand what people prefer in
terms of level of autonomy and how the desk could accommodate this. How do we
provide feedback that isn’t obstructive but at the same time isn’t something
that somebody’s going to automatically start ignoring? The goal of collecting
all this data is to get away from the one-size-fits-all approach. Systems are
designed for the standard user but if you don’t fit in that standard user
bucket, you’re either too hot or too cold and not comfortable in your space. By
observing how we respond to different environmental cues, the desk can
gradually learn our needs and preferences. Ultimately a network of
intelligent workspaces, desks that you can take the AI with you when you go to
a different office and it knows your preferences. It can continue to help you
be healthy. How can we utilize this to change
to change habits to move towards that healthy lifestyle and to improve
productivity and performance? It starts with a desk but eventually this kind of
intelligence could pervade our lives. The desk is a kind of a spring board to allow
and sort of implement technology that is really embedded in a way that is more
subtle. Creating life for something that is so regular an office desk. I think
that would be a tremendous achievement.

Automated trucks: Blue-collar disaster or economic win? | Andrew Yang

The big misconception about the impact of
technology in the workforce is thinking that it’s around the corner. Instead it’s been with us for years. If you look at the last 20 years or so, we’ve
automated away 4 million manufacturing jobs in Michigan, Ohio, Pennsylvania, Wisconsin,
Missouri, Iowa, all the swing states that Donald Trump needed to win in 2016 and did
win. Then my friends in Silicon Valley and my friends
who work in technology know that what we did to the manufacturing workers we are now going
to do to the retail workers, the call center workers, the fast food workers, the truck
drivers, and then even bookkeepers, accountants, insurance agents, lawyers, and on and on through
the economy. So what happened to the manufacturing worker
is a very clear sign of what’s going to happen to these other workers moving forward. And I talked a little bit about retail workers,
the most common occupation in the economy. Thirty percent of Main Street stores and malls
are going to close in the next five years because Amazon is soaking up $20 billion of
commerce every year. And many of these workers are making $11 to
$12 an hour and don’t have a huge savings cushion to be able to make meaningful adjustments. Being a truck driver is the most common job
in 29 states. There are 3 and 1/2 million truck drivers
in this country, average age 49, 94% male, average education high school or one year
of college. They’re making about $46,000 a year right
now. It’s one of the higher paying blue collar
jobs in this country. And on the west coast, you have my friends
in Silicon Valley who are trying to automate truck driving. And the reason they’re doing that is because
of the money — $168 billion in financial incentives for automating away truck drivers. And that’s not just labor savings. That’s also equipment utilization because
a truck never needs to stop whereas human-driven trucks have to stop every 14 hours; fuel efficiency
because trucks can convoy together in lower wind resistance and so robot trucks would
be able get places with less fuel, fewer accidents because truck drivers right now kill about
4,000 other motorists a year in accidents and that number would come down if you had
automated freight. So there’s a very, very powerful set of incentives
to try and automate truck driving as an occupation. Again, though, you have these 3 and 1/2 million
truckers, and only 13% of them are unionized. So there’s not going to be a grand negotiation. So imagine being a trucker who’s taken out
$50,000, $60,000 loan to lease your truck and it’s your livelihood and your means of
support, and then all of a sudden, you have to compete with a robot truck that doesn’t
need to sleep. And that is what is around the corner for
hundreds of thousands of truckers in this country in the next five to 10 years when
robot trucks start to hit our highways. And Amazon is testing out robot trucks as
we speak, right now in the Midwest.

AI Impact on Jobs & the Skills of the Future

Hi everyone, and welcome back. So we’ve been hearing more and more talk
about A.I. and how A.I. and automation will impact jobs in the future. From Trevor Noah to the Financial Times everyone
is talking about it, so we wanted to add our opinion into the mix. So how is A.I. for business going to affect
the future of work? And more specifically, can we identify those
jobs that are at more risk of being taking over by A.I. and automation? There’s a very wide dissonance on this. We started analyzing a bunch of different
reports that have been shared online like McKinsey reports, OECD studies, and, my personal
favorite, an Oxford study that said 47 percent of U.S.
jobs are at risk of automation over the next few years. Meanwhile, we see that the general population
and workers think differently. A recent study, conducted by Marist College,
actually identifies that 97% percent of workers believe that most jobs will be automated,
but not their own. This suggests that the general public needs
to be educated on which jobs are susceptible to this risk, which are not and businesses need to be aware of the forthcoming
skills gap. Of course, not all jobs are equal. The Oxford study that we cited a moment ago
actually highlights this. They examined 700 participants and found that
the generalist occupations that require creative knowledge or innovation are at least risk. The same is true for occupations in education,
healthcare, media and arts jobs. On the flip side, jobs like telemarketers,
junior lawyers, accountants are at most risk. In short, there is a simple rule of thumb:
if your job is in some way predictable or routine, the risk of automation is much higher. If a job doesn’t require innovation or creativity
than the return on investment for companies is higher on machines than real time employees. Machines are faster, can’t be distracted and
can work 24/7. This is actually good for creative marketers,
because A.I. and automation can serve to augment their jobs, rather than substituting them. Last month, McKinsey and the World Economic
forum published a white paper about the impact of emerging technologies on the creative economy. They stated that artificial intelligence is
changing creative content from beginning to end. By 2030, A.I. will be able to write high school
essays, code in Python, compose top 40th chart songs and make creative videos. But all these advancements also comes with
risks and costs. Take a look at this report by the Global Commission
on The future of work. In the absence of effective transition policies,
many people will have to accept lower-skilled and lower-paying jobs. High-skilled workers are taking less cognitively
demanding jobs, displacing less educated workers, And this is already happening! Also, technological dividends are being unevenly
distributed among firms. A very limited amount of companies tend to
dominate when it comes to “big data”. Just think about Google and Facebook. Today, they alone are responsible for 70%
of the referral marketing traffic and receive more than 50% of total, global advertising
budget. So the question is: can businesses, workers,
and social institutions go in the same direction? If companies and public policy leaders can
understand the evolving landscape they can help the workforce anticipate the
upcoming challenges. Technology and the demographic changes are
leading to a smaller workforce, compared to the previous generation, and the workforces has to pursue many careers
during their time of work. We need to provide workers with an environment
where they can continuously upskill and grow. Governments will have to re-evaluate the educational
system we will have to continuously learn and grow and companies will have to redesign their
structure and their culture around technologies Just like during the industrial revolution,
we are heading into a new age. In the great transformation that we are about
to see by 2020, it is estimated that 20% to 25% of the labour force will be displaced
within 10–20 years. However, this is also an opportunity for us
to get ahead. We have to find ways to attract and retain
highly skilled workers and allow them the time to upskill themselves, even during work
hours. We think that a good way to start is to develop a learning community so you can benefit from each other. And also to use technology to supplement your
goal tracking and your effort, instead of as a distraction. In short: what are you doing to bridge the
dissonance. Have you made a map of how A.I. and automation
will affect your industry and your company? If this is an economic imperative, how do
you feel about committing yourself to a lifelong approach to knowledge? Now, what is your opinion? Please, let us know your thoughts in the comments
below and stay sharp!

Cloud Conversations: Oracle’s Work with Emerging Tech

Big topic, artificial intelligence, everyone’s
talking about it. Oracle adaptive intelligence, how is this
being manifested in the cloud platform today? Yeah, we’re embedding AI
into every part of our cloud so we can democratize the access
and value from AI so it’s inside our integration cloud service, it’s
inside our analytics cloud service it’s inside our applications, our SaaS applications
so we can give you next best offer, how to do discounting based on what the
market data is saying, etc. So a very interesting discussion on AI,
and now there is an other big part to AI that you definitely want
to talk about. Well, its the autonomous database.
Of course! You know, people are spending so
much time patching database, provisioning databases, operating databases.
We want to take all of that away. With the autonomous database, we’ve created
a database that is self-driving, self-patching, self-scaling, self-healing.
Yes, self healing–exactly! So, the goal is, for example,
for a warehouse you don’t even have to worry about
setting indexes. You don’t even have to worry about
query optimization. Over the last two decades, we’ve learned
how to optimize the platform and we’ve learned how to optimize
the infrastructure upon which it is running So that you can just get
the value of the database.

What is Cognitive Technology? A Look at Real Business Applications

According to a recent analysis from LinkedIn,
2019’s employers are looking for a combination of both hard and soft skills.
Cloud computing and artificial intelligence are topping the list of desired attributes.
So is there a type of technology that combines both of these desired attributes?
The answer is: Yes! These are the so-called cognitive technologies. Hi! I’m Judit, A.I. Trainer and
Coach at Growth Tribe. Offered usually by the AI-first companies within their cloud platforms and by pioneering
startups, these cognitive technologies mimic human abilities such as vision, text analysis
and speech. More importantly, these applications make
use of top performing algorithms trained on loads of data containing images, videos and text. Basically companies like Google, Amazon and
Microsoft have been acquiring this data from society’s behaviour for years, and now, they
are selling this intelligence back to society as machine learning as a service (MLaaS).
They became part of the secret tool kit of the best analytics translators and those decision
makers responsible for matching a business problem with a feasible technology. Let me give you an example: SkinVision is an app that detects skin cancer
melanoma, which is based on computer vision supported by Amazon Web Services.
It has trained a proprietary algorithm to label melanoma pictures that is now being
used by more than 1 million active users. Users can take pictures with their smartphones
and scan their body frequently, under a subscription model. Considering that 1 in 5 citizens develop some kind of skin cancer, the service is now frequently
offered for free by insurers to their clients. So instead of developing an app to book a
visit to a dermatologist, it actually brings the specialist view directly to you in the
form of computer vision. A typical example of artificial narrow intelligence.
But you still need to know how to evaluate these models according to the cost of their
mistakes. It is still the job a human to judge these
mistakes in an analyst-in-the-loop approach. Just like when you buy a car, you need to
know how to compare different models and make diagnostics of engine failures. But you don’t
need to know how to build the entire car yourself. Another very interesting example comes from
DataSine’s Pomegranate image scoring. Their machine learning models have been trained
on images and text examples to understand how different content appeals to humans.
It offers suggestions on words and phrases to replace, as well as colours, themes
and images, depending on the personality of your users. We’ve seen some really nice examples from participants in our Growth & AI traineeship. Marwan from the Mobile Company, recently tested Pomegranate by comparing the suggestions of
the image scoring model with real click through rates of different ad campaigns.
Using this kind of image recognition, a skilled marketer will not need to run too many experiments
in order to find images and copywriting that actually generate a higher conversion rate. Another application of computer vision was tested by Merle from Son of a Tailor.
She used Google’s Auto ML Vision to build a prototype classification model for new t-shirts
based on their attributes, for example crew neck or v-neck.
After you set up the environment on GCP, there are no coding skills required, but the person
running the experiment can observe the performance metrics like precision and recall.
Or even observe how the model would perform on new t-shirts once deployed in an application. Microsoft Azure, similarly, has a set of cognitive services that can be used to detect specific
content in text and images. Another one of our trainees, Niels, from Eyecons,
tested the Content Moderator and review API to detect profanity and negative sentiment
in content. This content could be posted, for example,
by sport fans in forums and comments to articles. Then, a human-in-the-loop could react promptly
with the best communication strategy. Hope you enjoyed these examples of cognitive
technologies and are keen to know more about them, as well as the current top
professional skills: cloud computing and artificial intelligence. Don’t forget to like, subscribe, leave your comments below and see you next

The Project Economy: What it means for the world, business, and you | European CEO

European CEO: I’m with Sunil Prashara, President
and CEO of the Project Management Institute. Sunil, as part of Project Management Institute’s
renewal, you’ve launched a new focus on what you’re calling the Project Economy; what do
you mean by Project Economy, and what does it mean for organisations moving forward? Sunil Prashara: Yeah, so, project economy
for us – there’s two definitions. There’s one at the individual and corporate
level, and there’s one at a, sort of, global level. And I think it’s a cause-and-effect. You know, there’s a lot of big things that
are happening around the world, which are resulting in the countries in those parts
of the world changing. For example, in the Middle East: if oil demand
is declining, and alternative energy source demand is increasing. If you’re an oil producer, you might be thinking,
‘Well okay, I’m probably good for the next 50 years, maybe even 100 years. But I do need another story. So how do I go out and build another story?’ And they’re pushing very hard in alternative
energy sources, healthcare, tourism. Changing what each of those companies wants
to be, to maintain their GDP, and maintain their relevance in the marketplace. That causes in that company a change. A project. So the world is becoming projectified, because
of these tectonic shifts. You could say the same thing in Africa; I’ll
give you another example. 2050, the population of Africa is expected
to be 2.4 billion people. It’s 1.4 billion today. They have to build 65,000 homes a day, every day, for the next 30 years to accommodate those people. How many hospitals?
How many schools? How many roads?
Who’s going to do that? How’s that going to happen? And which countries are going to be relevant
when that happens? That is a huge amount of project work. So if I’m an organisation, and my government
is saying that in order for me to be relevant, I need to change the kind of services that
I provide: that’s a transformation, that’s a change. We believe that organisations are going to
have to really rethink the way work is done in their businesses. So we’re seeing the world at the business
level becoming projectified. And we call that the project economy, where
you have teams of people moving between functional areas, without the boundaries of finance,
HR, legal etc. And the people you need in the organisation
should all be able to be well-versed in project management disciplines, and the ability to
execute. Because they’re going to be called upon to
do that, moving from project to project to project. European CEO: Now, what skills do project
managers – and if everything is being projectified, professionals in general – need in order
to thrive in the project economy? Sunil Prashara: Well you definitely need to
have a very strong understanding of technology. Technology is helping project managers and
other professionals to optimise the way they do their work. So leveraging technology, leveraging big data
to give you insights as to what your next step and decision should be. We call it the technology quotient; not necessarily
being a software developer, but having an appreciation for how technology can help you
is very, very important. When you look at specific skills, you still
can’t run away from scheduling, planning, iterative processes for development, governance,
risk management. You know, you also have to be able to pick
the right methodology for how you execute on a programme, on a project. The third skillset is empathy. And here’s something that technology really
can’t get its arms around. Professor Tabrizi from Stanford University
calls these the power skills. They used to be called the soft skills: empathy,
cultural awareness, ability to be able to say ‘sorry.’ The ability to be able to be human. A few years ago, these were soft skills, and
the hard skills were, can you create a Gantt chart? Can you do an Excel spreadsheet? Can you manage a month end close? Concrete things which today are automated,
systemised, codified. And those softer skills are now being called
the Power Skills. They’re the things that make things happen. So these are some of the skills that we advocate
as PMI. You have to have an appreciation, an understanding
of the hard skills. You can’t get past home base without that. But what makes you stand out is when you have
the softer skills, or the power skills, to be able to make things happen. Sunil, thank you very much. Thanks for watching. Click now for more from Sunil, on how technology
is changing project management and strategy implementation. Learn more at, and please subscribe
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