Any mystery fan knows that in addition to paying attention to clues and evidence, the genre’s best detectives also work tirelessly to uncover subtle details and connections that may not be immediately obvious. As companies around the planet race to achieve their net-zero emissions targets, identifying the optimal path to sufficient carbon-production reductions will require an unprecedented level of investigation.
Although AI is still a relatively new technology, companies are already embracing it as an incredibly insightful decarbonization detective to help sleuth out hidden clues to find sustainable business practices. The speed with which AI can analyze vast amounts of data, quickly detect patterns, and suggest solutions makes it particularly well suited to the task. In fact, some experts believe that no company will reach its net-zero goals without it.
According to a report from Google and Boston Consulting Group, AI has the potential to help mitigate up to 10% of global greenhouse gas emissions by 2030, which is the equivalent of the European Union’s total annual emissions.
Incredible AI efforts are already underway within many of the core sectors that Raise supports. Every day, we see exciting examples of companies leveraging AI’s power to help achieve net-zero emissions targets. Here is just a small sample of encouraging illustrations from the energy, oil and gas, and manufacturing sectors, as well as some perspective on how these efforts are shifting talent trends in each industry.
Raise Focus: Power & Energy Sector
Climate Change AI, an organization of volunteers from academia and industry that is leading the creation of a global movement in climate change, sees electricity systems as a key climate change influencer, ripe for the fact-finding powers of AI and machine learning.
The power grid is quickly shifting from directing energy from centralized power stations to managing multi-directional flows of electricity between generators, the grid, and users. AI and machine learning will play a central role in squeezing value from the billions of data points being generated.
According to the International Energy Association, one of the most common uses for AI in the energy sector to date has been predicting supply and demand. Due to the variability of weather, clean energy supply sources like wind and solar can be much trickier to predict, but there is a wealth of data that can provide valuable clues. The world’s fleet of wind turbines, for example, is estimated to produce over 400 billion data points each year!
One critical skill required to manage all this data is programming language fluency. Python, Java, R, and C++ are some of the languages most frequently used in AI. Expertise in Big Data platforms, like Hadoop, Pig, Hive, Spark, and MapReduce, is also essential. In addition to these AI hard skills, however, there are some key non-technical skills that will be vital for those working on AI-driven clean energy projects. Ensuring teams have members who are creative problem solvers with strong decision-making and communication skills will help prevent data analysis paralysis.
Raise Focus: Manufacturing Sector
The manufacturing sector, which is the source of one-third of global emissions, has its work cut out for it to reach net-zero targets and remain competitive. While large multinational firms tend to be what comes to mind for most people when considering the sector, small and midsize manufacturers contribute 60% of global carbon emissions. AI tools will play an essential role in supporting these smaller businesses in reducing their environmental impact. While capital-intensive zero-emissions efforts like carbon capture and transitioning to renewable power may not be in their budgets, smaller companies can see big wins from implementing AI and machine learning solutions with a much smaller investment. For example, manufacturers who use 3D printing can use AI algorithms to optimize print designs, schedules, and material mixes to minimize waste.
As the manufacturing sector becomes increasingly dependent on AI tools, the skills that companies need in their workforce will begin to shift and expand. The area of predictive maintenance offers a telling illustration. With the right sensors, data, analytics, and machine learning algorithms, manufacturing teams can be much more effective at detecting patterns that point to the machines at risk for possible breakdowns, to address the problems and prevent downtime. Being able to use AI and machine learning tools to address small problems before they turn into big issues has the potential to decrease between 30 and 50 percent of equipment stoppages and increase the lifetime of equipment by 20 to 40 percent.
But it’s not as easy as simply investing in the technology to generate and manage predictive maintenance data. New skill sets, like feature engineering, will be increasingly important for engineers charged with predictive maintenance tasks in the manufacturing space.
Feature engineering is a collection of techniques used in machine learning. In the example of predictive maintenance, feature engineering would narrow down and manipulate the columns of predictive maintenance data that have been generated to create an optimized set of data with the strongest potential to find meaningful models. For engineers who don’t have a lot of experience with AI and machine learning solutions, putting their trust in a computer to tell them the problem can be a learning process. As Data Scientist Scott Genzer told Plant Engineering Magazine, “The danger that engineers almost instinctively get into is they think they know which columns are going to drive the predictive class. This biases the model. You really want the computer to find what it thinks the columns are and have an open mind. You need to apply a little bit of judgment because you don’t want it to find things that are ridiculous, but we don’t want to limit the computer’s ability to find a signal that you may not know exists.”
Raise Focus: Oil & Gas Sector
The magnitude of data created in the oil and gas industry offers great potential for AI to mine it for opportunities to fuel environmental improvements. Exciting AI-driven efforts are becoming the norm for this sector. The leading oil organizations are implementing a variety of initiatives including:
- Using real-time data collected by oil and gas rigs to optimize their routes and reduce the amount of fuel used
- Monitoring over 10,000 pieces of equipment to improve predictive maintenance
- Developing a self-learning and AI-powered smart assistant to “talk” with control room operators, helping them optimize daily production of high-value fuels and chemicals
Research from Ernst & Young confirms that this increased use of technologies like AI and machine learning is gradually replacing routine and repetitive tasks and freeing up workers to focus on higher-value activities. The report, which looks specifically at the Canadian oil and gas industry, evaluated 124 different jobs and predicts that there will be very few, if any, that will not be impacted by AI by 2040. While many positions traditionally associated with the sector, including equipment operators, trades, and technicians, are expected to decrease between 60 and 65 percent over the next 15 years, professions like data scientists and software developers will be in higher demand.
Cybersecurity professionals are already in particularly high demand. As the oil and gas sector incorporates more and more AI solutions, those countless digital touchpoints increase the risk of cyberattacks. According to the World Economic Forum, the world is already facing a shortage of four million cybersecurity professionals. Skills that have been identified as most important for cybersecurity professionals in the oil and gas sector include digital forensics, cybersecurity operations, systems and app development, and vulnerability assessment.
Whether looking for data scientists, software developers, or cybersecurity professionals, the Ernst & Young study’s researchers urge the oil and gas industry to prepare to compete outside its own walls for talent.
Raise Managing Director Jessica Matty agrees with this prediction and thinks it has broader applicability, “As more and more organizations tap into AI to solve their most pressing problems, flexibility is going to be key across all industries. The industry that a candidate has direct experience in will matter less and less. In many cases, finding someone with proven AI, machine learning, Internet of Things, or cybersecurity skills will be more of a priority than the specific sector where the candidate may have initially earned that experience. The other thing that’s going to be important to keep in mind is that finding that digitally fluent, multidisciplinary worker may also require a shift from your traditional hiring practices to appeal to those highly skilled workers who tend to favour the gig economy.”
Raise Focus: Recruitment vs Upskilling
As the landscape of AI continues to rapidly evolve, organizations and hiring managers are faced with a critical question: is it better to hire external AI experts or invest in upskilling the existing workforce? There is no right or wrong with this; both approaches have their merit. Competition for AI experts is fierce, and many organizations are looking outside their sector to find the talent they need. At the same time, companies are finding success in skilling up key employees to drive their AI projects. Some questions Raise encourages hiring managers to consider:
- What are your specific AI needs? Are you looking to improve an existing process or aiming to bring in a whole new technology?
- What is the current team composition? Consider the growth and adaptability of your team members. Do you already have team members who have demonstrated a capability with machine learning or AI?
- What are the time constraints? What is your timeline to implement AI initiatives? Is it a single project or an on-going need?
- Cost-Benefit Analysis: Both hiring and upskilling come with costs. Consider the long-term benefits and ROI of each.
- Attrition and Knowledge Loss: AI talent is in high demand right now so an external hire may leave for a new opportunity. Upskilling an employee can help you retain organizational knowledge as well loyalty.
- What is the organization’s AI strategy and commitment? Does leadership have a vision to prioritize AI projects over all else? Is there a sincere willingness to invest in the development and learning of employees?
Ready to make an informed decision about AI expertise? Let’s chat about finding the best path for your team’s growth.
