Automation trends for technology jobs: Planning for innovation, while increasing efficiencies
Experts say automation will eliminate 73 million more jobs by 2030. However, while automation will likely negatively impact work requiring repetitive or menial tasks, it will also shift and create talent demand in more specialized areas. According to one report, automation is expected to create 23 million new jobs in areas such as AI architecture, development, programming, testing, support, and maintenance by 2030.
Artificial intelligence (AI) refers to computer systems built to mimic human intelligence and perform tasks such as recognition of images, speech or patterns, and decision making. AI can do these tasks faster and more accurately than humans.
Robotic process automation (RPA) is a subset of AI-enabled enterprise software meant to take on the repetitive but necessary tasks such as filling in forms, generating reports and diagrams, and producing documentation and instructions. According to Gartner, RPA is outpacing all other segments of the enterprise software market worldwide, with expected revenue of $1.3 billion this year. The market grew 63 percent last year to $846 million.
Machine learning (ML) is a subset of AI. With ML, computers are programmed to learn to do something they are not programmed to do. They learn by discovering patterns and insights from data.
Despite the misconception of AI being ubiquitous, most organizations have only begun to explore and deploy AI technology on a limited scale. Aside from digital pioneers like Google, Amazon, and Facebook, many organizations are not equipped to readily develop and implement their own AI solutions at the moment.
The lack of organizational capacity to capture the robust data sets needed for AI algorithms and AI workloads has resulted in limited strategic deployments by most companies. Most often, this has led executives to identify the areas of their business that are most readily able to adopt the technology or would stand to gain the most immediate value such as data analysis, customer support/experience, and task automation.
Automation business value
While AI and ML applications will vary widely, a report by Morgan Stanley provided some proven ROI from applications of these technologies in selected industries:
- Machine learning is analyzing wind farms to make power predictions 36 hours in advance, enabling providers to make supply commitments to power grids a full day before delivery and increase the value of wind energy output by 20%.
- In Australia, mining companies are using autonomous trucks and drilling technology to cut mining costs, improve worker safety, and boost productivity by 20%.
- If U.S. utility companies used AI-powered asset management software, costs could be cut by $23 billion annually, reducing outage frequency, overall footprints, installation times, and copper cabling usage.
- A European automaker built a “fully digitized” factory and significantly reduced manufacturing time while boosting productivity by 10%.
McKinsey also reviewed 16 case studies of companies that have had successful RPA deployments, and companies were able to realize 30-200% of ROI within the first year alone. Further, the report also notes that there were multiple intangible business benefits in areas such as customer service improvement, compliance, and employee satisfaction.
Expected benefits of AI
Potential uses for AI
AI growth is significant and businesses that have adopted AI have started seeing positive returns.
According to a McKinsey survey, there has been a 25% YOY growth (19-20) in AI adoption across a multitude of industries. Sixty-three percent of respondents also noted revenue increases and 44% reported cost decreases from AI adoption in the business unit of deployment. Use cases with the greatest returns included pricing optimization, sales and demand forecasting, and the creation of AI-based products and services for sales, among others.
Artificial intelligence represents a new way of thinking about software.
Most businesses—especially on the smaller end of the scale— are not actively developing their own artificial intelligence algorithms. Instead, they are adopting products that have AI features built in. Even so, there must be an understanding of how AI differs from previous software applications. Rather than operating on inputs in a deterministic way to produce results, AI programs take in large amounts of data and work in a probabilistic manner. This means that there is a higher degree of uncertainty in the results. AI may produce unique and disruptive insights, but those insights require some amount of validation.
Innovative use cases require strong foundations.
Using a broad definition for AI, many existing IT activities could be placed on an AI spectrum, and building on these activities could lead to better automation or stronger data analysis. However, most companies take a modern view of AI, imagining use cases such as personalized customer experience or security incident detection. Along with the introduction of new AI components, companies must also consider the infrastructure needed, the data that will drive the work, and the processes for integrating AI into workflow.
A mix of basic skills and new skills is needed.
From an ownership perspective, 51% of companies say that AI projects are mostly handled by the IT team. Considering the scope of potential impact, businesses should make sure that AI projects are handled similar to most technology projects in a digital organization—as a collaboration between IT and business units. When it comes to detailed implementation, companies are often looking for specific skills around troubleshooting or developing AI, but there are also foundational skills in software development, security, and data management that contribute to AI success.
But, there is a talent shortage in AI/ML.
Perhaps due to the rapid growth of adoption and success stories, there is a significant shortage of talent for AI/ML in areas such as algorithm development, data scientists, and AI/ML engineers. Further, the talent that does exist is geographically scattered. Companies working with AI should look to either upskilling existing resources and/or leverage outside resources to identify and secure key talent quickly.
Skills needed for AI
Wanted: artificial intelligence experts
References: IT Chronicles, The AI Skills Shortage; McKinsey, Global AI Survey: AI proves its worth, but few scale impact Morgan Stanley, Investing in the Second Machine Age – Picking the Winners McKinsey, The Value of Robotic Process Automation
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