PwC’s third annual AI Predictions survey reported that 18% of companies adopted AI in their businesses in 2019. By 2020 that figure dropped a few percentage points. Now it’s expected to sky rocket (54%). Why? And what are the implications for the talent industry?
The AI industry is growing in size, confidence and relevancy
Consulting firm PWC, suggests that AI could contribute up to $15.7 trillion to the global economy in 2030. Of this, $6.6 trillion is likely to come from increased productivity.
Broadly half of organizations surveyed by McKinsey say they have adopted AI in at least one function and over half of those adopters claimed their use of AI reduced costs.
This rapid pace of adoption is none too surprising considering practically every startup gaining funding in Menlo Park has an AI aspect to its business model. Sure, AI had a dip in 2020 as business leaders dealt with the fallout of a global pandemic, but in 2021 it’s likely to take centre-stage in boardroom plans to innovate out of the economic slowdown.
Applying AI to business challenges
The last decade has seen great strides in digital technology innovation including blockchain, 3D visualisation, the Internet of Things (IoT), big data and cloud computing, just to name a few. Of late, the challenge for executive teams has not been aa shortage of tech innovations, but rather the hurdles that come with supporting change projects when operational teams are already bursting at the seams.
To apply digital technologies to improve business processes, requires ready-to-plug-in solutions that are bite-size, retrofittable, configurable (i.e. not requiring customization), and easily rolled out.
From the early-stage adopters of 2019 and 2020, a blueprint of where AI is finding a home in the enterprise is becoming clearer. There are four main roles emerging for AI in the enterprise stack:
1. To automate manual tasks—for example, to use AI to automate the aggregation of data, to validate or generate reports.
2. To help people to perform tasks faster and better—for example, using AI-driven on-screen ‘prompters’ in call centres to advise customer service agents on how best to respond to requests.
3. To help people to make better decisions—such as helping humans make sense of large volumes of data, or canvas a larger data-set (that would’ve previously been uneconomic).
4. To automate decision making processes and remove the ‘human-from-the-loop’—for example, to blend AI with chatbots and design online application journeys that automate enquiry resolution by responding to answers provided by the requestor.
AI and Talent
The talent industry is likely to be more effected by AI innovations in 2020 than any other aspect of the economy.
Talent remains the biggest resourcing cost for the majority of businesses. It’s also one of the more inflexible aspects of organizational designs, which means when markets contract and scale, re-sizing and equipping talent supply is one of the more costly adaptations. That’s not good news for executive teams facing an unpredictable future outlook.
There are two very good reasons why rapid AI adoption will happen at scale in the talent industry in 2021:
1. Organizations need to double-down on labor economies
Looking back over the past 2 years, optimization of talent management is one of the top three AI use cases that most commonly led businesses to achieve cost decreases, the others being contact center automation, and warehouse automation; or so says McKinsey in their ‘The state of AI in 2020’ report, published on November 17, 2020, following a global survey on the subject
There are a number of AI use cases built into Vendor Management Systems and Talent Portal platforms like Simplify VMS that make it simpler for businesses to leverage AI. These include:
Triaging requirements – There are many ways jobs can be done these days in the enterprise including software robots, automation, contract hires, outsourcing, knowledge portals, crowd sourcing, and employment. AI tools are great for removing emotion from decisioning to recommend the best way to get a job done based on the scope and type of job in question.
Vetting applicant CVs – Hiring Managers spend hours, if not days, every year reviewing work applications and pulling out the best people for interview. Much of this work can now be done using AI tools with the added benefits of removing biases in the selection process.
Background and compliance checks – IC compliance and other forms of pre-hiring/employment checks are something that benefit from AI tooling to expose failings in applications and learn from previous errors and circumstances to fine-tune the filtering process.
Qualifying skills – Similar to the above, many companies now are moving the online skills and competency tests as more and more recruiting goes virtual. AI-driven validation tools are able to vet applicant skills and make recommendations; work that would otherwise need to be done manually.
Managing onboarding enquiries – The use of AI-driven chatbots are becoming more popular in use cases like customer service, help desks and workforce support situations. There are many occasions when new hires have questions that require quick responses (on topics such as policies, insurance, payroll, timesheet submissions, who to speak to, starting dates and arrangements, etc.) that AI tooling is now stepping up to serve through chatbots.
2. Organizations seek to achieve productivity gains by equipping their processes with AI, to take ‘the human-out-of-the-loop’ wherever a machine can best serve needs.
Another way AI is impacting on the talent industry comes as the result of artificial intelligence displacing or adapting roles. If we believe reports from market watchers like McKinsey, AI is set to transform employment requirements in many industries.
A recent study of more than 2000 work activities my McKinsey and Co. reported that about half of the activities (not jobs) carried out by workers could be automated using known technologies. They suggest, ‘…most workers—from welders to mortgage brokers to CEOs—will work alongside rapidly evolving machines. The nature of these occupations will likely change as a result.’
Even were we to ignore these direct impacts of AI, the drive to digital enablement (and broader application of AI in industry) has already led to a seismic upwards shift in demand in IT and data analysis skills; adding to the global shortage that already exists for these skills.
A recent survey conducted by Analytics Insight projecting job opportunities for 2021 suggests there will be in the order of 3 million (3,037,809) new job openings in data science, worldwide. They signpost MapReduce, Apache Pig, Machine Learning, Apache Hive and Apache Hadoop as being the most in-demand skill in this job market. In terms of coding skills, Python will continue dominating the most preferred programming language, followed by R, SQL, MATLAB and Java.
What to expect in 2021
While many organizations elected not to pursue AI adoption in 2021, perhaps sensibly deciding they had more important fish to fry, vendors haven’t stood still and neither has the growing community of individuals who’ve taken the lockdown period to get skilled up in popular AI tools and methods.
RubixML is an example of technology platform democratizing AI. It is a free Open Source project that provides a high-level machine learning and deep learning library for the PHP language. The library provides tools for the entire machine learning life cycle from ETL (extracting, transforming, loading, manipulating and summarising data) to training, cross-validation, and production with over 40 supervised and unsupervised learning algorithms. A number of algorithms in the library support Deep Learning including the Multilayer Perceptron classifier and MLP Regressor.
We’re going to see an awe-inspiring number of AI-led business services and platforms launch in 2021. Supporting this will be an army of AI enthusiasts eager to start their career on the AI track.
About the Author
Ian Tomlin is a management consultant and writer on the subject of enterprise computing and organizational design. He serves on the SimplifyVMS Management Team. Ian has written several books on the subject of digital transformation, cloud computing, social operating systems, codeless applications development, business intelligence, data science, office security, customer data platforms, vendor management systems, Managed Service Provisioning (MSP), customer experience, and organizational design. He can be reached via LinkedIn or Twitter.