he science of project planning has something of a tenuous reputation. How often do large oil and gas capital expenditure (CAPEX) projects really come in according to plan? Almost never. Even during this era of digital transformation, project schedule and cost overruns are still the normal course of business, not the exception.

Arguably, the reason for this is less about poor execution and more about how we still struggle to accurately forecast how long these complex CAPEX projects will actually take to complete.

According to a 2017 McKinsey report, the Middle East has one of the most significant project pipelines anywhere in the world, with a total of US$396B of future projects planned across the region — this means there is a lot of money to be made and opportunities to engage new methodologies and approaches.

New approaches using digital planning and risk assessment tools are poised to change oil and gas project economics forever, bringing with them the potential to deliver successful and on-time CAPEX projects while unlocking significant value.

For the first time in the industry, project planning can combine artificial intelligence (AI) and human intelligence to create true risk intelligence. In a nutshell, this is achieved by bringing together historical project data and human expertise. This way, planners and project teams can produce more accurate and fully risk-adjusted schedules for their projects.

If oil and gas companies in the Middle East can use these tools to adapt their scheduling practices to meet the needs of their unique regional environment, the productivity improvements could deliver up to 30% in cost savings – a whopping US$118B.

A wide variety of factors can contribute to project delays and CAPEX mismanagement, but the root cause has less to do with the likes of planning techniques not being fit for purpose and more to do with inaccurate data being fed into those plans.

The tide is finally turning toward more accurate project forecasting with the advent of AI and the simple realization that it takes the expertise of a specialized team to build a plan, rather than a single planner working in a silo.

Next-Generation Risk Analysis
To help address the challenge of developing a meaningful risk model, a more team-centric and collaborative means of capturing risk and uncertainty inputs has been developed, along with more easily consumable and actionable risk reports. Enter the human intelligence element, where a team’s collective expertise is pooled together on a single platform.

Let the Software Compile the Uncertainty Ranges for You
Rather than force team members down the “describe the range of outcomes as a distribution” approach, why not capture such expert opinion through a simple scorecard instead? Simply ask team members to either buy in or push back on the proposed durations.

This approach carries the massive benefit of making the expert opinion and knowledge capture process very fast and easy for contributors, while still retaining the underlying modelling methodology. This approach also ensures that the total consensus of the team is accounted for in the risk model rather than being “the voice of one.”

Relating back to the challenge of owner/EPC contractor alignment, this concept of consensus-based planning helps drive that necessary synergy tremendously, which in turn drives buy-in and, ultimately, the project’s chances of on-time completion.

Use AI to Help Establish Your Risk Register
In addition to more efficiently capturing duration ranges through the approach described above, the second step in the risk model building process is to capture and quantify risk events.

Traditionally, risk events have been tracked in what is known as a project risk register. The modelling challenge arises when linking those identified risks from the risk register into the schedule risk model. Without overstating, this process is treacherous at best, and one that causes huge challenges in project risk workshops.

So instead of identifying risks in isolation of the schedule and then trying to embed them back in, why not provide an environment where risks are identified and scored directly in context of the schedule itself?

Taking this a step further, by leveraging AI, team members can also take advantage of the computer making suggestions as to common risks and their historical impact on similar scopes of work.

AI-Driven Guidance on Risk Event Identification

Rather than team members having to brainstorm from a blank sheet of paper, they can take into account previously realized risks and opportunities from similar historical projects. As new risks are identified, they can be automatically added to the enterprise risk register, ready for subsequent consumption. This self-perpetuating risk management loop is an entirely new and more effective way for an oil and gas company to adopt a more mature outlook on risk.

Risk-Adjusted Forecasting Is Applicable to All Project Stakeholders
Historically, project risk analysis has been available to larger project organizations and, typically, embraced more by business owners than EPC contractors. The advent of next-generation, risk-adjusted forecasting software is opening up the benefits of risk insight to the broader market.
By combining the data mining power of AI and pooled human intelligence, risk modelling is making huge strides forward.

Contractor organizations can now benefit from determining applicable contingency, along with appropriate margins, when developing their commercial bids. In short, contractors can ensure they are more competitive by following this risk-adjusted forecasting approach. Likewise, owners now get more insight into the realism and achievability of contractor schedules and, thus, can react and remediate faster.

In all instances, the benefit of providing an easier means of capturing risk inputs, applying them to a proven approach, and then gaining deeper and more meaningful insight through next-generation risk reporting, is hard to argue against. 

The long overdue collaboration between human and artificial intelligence is finally becoming a reality. By enabling on-time project completion, this culmination of proven practices becomes a perfect union, and has the potential to unlock value across a project’s life cycle.  The end result is that more projects will see the light of day.

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The AI Hype Train has left the station. You know you’re in the middle of a hype cycle when products and companies start using a term regardless of whether or not their product incorporates any of that technology. This is where we currently are with regards to the market for AI products and services. While there is a lot of great, new innovation that’s pushing the industry forward towards more intelligent systems capable of many of the challenging areas that have previously not been able to be solved due to extreme complexity or the need for human labor, there are just as many companies who are using the term AI as more of a marketing ploy or a way to raise money.

Many firms are claiming to be AI-enabled when all they have done is put some thin capability provided by a third-party library or API that doesn’t really transform their existing product into something that is inherently different with that new intelligent technology. One of the biggest offenders of this AI-as-a-buzzword is the entire Robotic Process Automation (RPA) market. While automation is they key word in this phrase, this corner of the process automation market somehow is identified not with the traditional workflow automation and Business Process automation or management (BPM) space that has existed for decades, but rather the more widely hype and strongly invested in AI space. This whole category is currently attempting to rebrand itself as intelligent and AI-enabled because they’ve added OCR or some other add-on. While automation is valuable and provides a ROI in and of itself, there’s no reason to conflate automation activities with intelligent activities. Automation is not intelligence.

What is automation?

Automation is not a bad word. The primary movement of the industrial revolution was to take much of what we were doing at the time with manual labor and automate that work so that we could achieve significantly greater productivity, quality of life, and transform society as a result. Automation is the process of applying technology to some repeatable task or process so that the task or process can be accomplished with predictable repeatability, lower total cost of operation, increased safety, and provide better efficiency. This is what we demand of most of our technology, and technology has delivered that value. In fact, technology continues to deliver increasingly greater value to enterprises and individuals, squeezing more efficiency and capabilities and increasing productivity on a daily basis. So, automation is good. There’s nothing bad about it.

Mechanization and the unleashing of power from steam and electricity, the development of the assembly line and factorization of manufacturing, and the evolution of computing and the Internet have truly revolutionized the way we work, live, and exist. However, while these are fundamentally potent and transformative technologies, they are not intelligent technologies. We can’t walk up to a steam engine and ask it to recognize who we are or answer a random question or even learn from its experiences. A web server is just a web server no matter how many times it’s served the same content to the same sort of people. Automation has provided enormous and fundamental value to society. However it is different than the value we are seeking from intelligent systems, because automation is not intelligence. Today In: Innovation

Intelligence is much more than automation

Humans demand more from intelligent systems than simply repeating or simplifying a repetitive task that requires zero cognitive skills. From the beginnings of what researchers have been attempting to do with AI, we’ve been striving for systems that can understand and comprehend their surroundings, learn from their experiences, make judgements and decisions that are based on rational thinking, handle new situations and apply their learning from previous experience, and perhaps even address bigger questions of self-awareness, consciousness, and more. These are complex problems AI researchers are trying to solve, and fundamental questions of cognition including self-awareness and reasoning.

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If you read through the latest edition of GitHub’s State of the Octoverse—a comprehensive report on the code respository’s biggest trends—you might pick up on something interesting. Although it’s already a well-established programming language, Python is continuing to grow at the rate of an up-and-coming one, gaining 151 percent in usage since 2018.

Part of that continued rise is directly attributable to data science, GitHub added in its report. “Behind Python’s growth is a speedily-expanding community of data science professionals and hobbyists—and the tools and frameworks they use every day,” it stated. “These include the many core data science packages powered by Python that are both lowering the barriers to data science work and proving foundational to projects in academia and companies alike.”

Indeed, data science and machine learning repositories on GitHub have enjoyed extreme growth. Developers (and the companies they work for) clearly feel that analytics and machine learning are the keys to the future, and Python is playing a significant role in that. “Among the most popular (based on star counts) public repositories labeled with the topic, over half of them are built on numpy, and many of them depend on scipy, scikit-learn, and TensorFlow,” GitHub added. “We’ve also seen non-code contributions from the data science field, including academic papers.”

Learning Python

Its importance to data science, itself a rapidly burgeoning field, makes Python worth studying. But where to start? If you’re totally new to the language, a good beginning point is this handy documentation available via the Python Software Foundation. From there, check out Microsoft’s “Python for Beginners” video series, which features 44 videos (all of them super-short, between five and 13 minutes in length) that cover various aspects of coding. 

Once you become a little bit more adept, you can begin focusing on writing faster Python (via Functions, Lists, and more), debugging, and other more advanced skills. A variety of tutorials and books (some of which will cost a monthly fee) can help you with Python in the context of data analytics and other fields.

Whether you intend to use Python for backend web development, data analysis, machine learning, artificial intelligence (A.I.) applications, or something else entirely, the best way to start learning is to simply play around. You can download the language’s most recent stable version on the Python downloads page (and if you can’t decide between versions 2 and 3, this page will help you choose). If you want to interact with others on their Python journey, there are also a variety of meetups around the world. Good luck!

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In recent years, enterprise machine learning has revolved around crunching numbers: analyzing datasets or tracking customer behavior. But what organizations will soon realize is that applying machine learning to content—physical documents, images, presentations and even conversational UIs—removes the cap on who machine learning impacts, and how far its value extends across the enterprise.

Tracking down lost documents and images, or drafting abstracts and case studies only to realize they’ve already been written are just a few of the daily frustrations that we typically consider unavoidable. But as it turns out, it’s these issues specifically—content discovery, tagging and classification—where machine learning is in a strategic position to make a substantial impact.

Numbers-driven algorithms have informed strategy for years now, but applying machine learning to content will likely have a similar, if not greater, impact on the enterprise. As companies generate written, verbal and visual content that’s measured and impactful, they’ll start to realize that numbers aren’t the only insights driving business forward.

Solving the problems you never knew existed

As words and phrases start to fuel machine learning algorithms, each and every member of an organization can reap the benefits. More often than not, these benefits will manifest in ways we never thought possible.

The inability to access, manipulate and leverage the right content at the right time is the hidden speed bump in your daily workflow. Too often, marketers, content creators, IT professionals or project managers are inundated with digital assets—blogs, websites, Google Docs, etc.—each serving separate business goals with different audiences and categories. Hours are wasted writing, editing, organizing and analyzing content that already exists, sparking frustrations and decreasing new output.

However, machine learning techniques can now classify content by category, provide answers to questions while you type, or offer suggestions that add depth to written material. Capabilities like these  offer solutions to the problems you never thought could be fixed. By building on progress that’s already been made, companies can finally combine creativity with productivity to make future content even more compelling.

Internal content is only the half of it. Inbound materials like customer service inquiries, emails and requests may seem manageable at first, but can pile up over time. Questions arise around how to utilize this content effectively, and ultimately improve business communication as a whole. Applying machine learning in this context welcomes tools like FAQ generators that quickly consolidate inquiries, identify common questions, and generate FAQ documents accordingly. Simplifiers like these put time back into your schedule, and bring momentum to the enterprise from the inside out.

Discovering machine learning’s value

As organizations generate better (and smarter) content, the entire company will move down the path of least resistance. With content becoming more intelligent, materials are rolled out more quickly, inbound requests are analyzed and turned actionable automatically, and IT isn’t summoned to help with unnecessary tasks.

Even better, with content turning conversational, the potential for machine learning gets even stronger. Right now, virtual assistants like Alexa or Google Assistant lack context, essentially making the term “conversational UI” a misnomer. They are far more instructional than they are conversational, but applying content-driven machine learning to these systems will transform them into discovery mechanisms for the enterprise. Pretty soon, you’ll be able to ask Alexa what image you used in a blog post in June 2015, with the correct image appearing on your screen within seconds. As we inch closer to truly conversational content, we’ll reach a level of efficiency unlike ever before.

There’s no telling what type of content will flow through the enterprise next. But as content of all shapes and sizes becomes the playground for machine learning, productivity hacks that crunch words, rather than numbers, will start to prove their value.

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