A slew of articles caught my eye this week that could provide us with a sneak peek into future skills and tasks that will impact the types of jobs available, particularly for what I call the middle strata—program and project managers, change practitioners, and mid-level leaders—career professionals who will shoulder the brunt of transformational change for organizations, particularly as AI and other advanced technologies take root.
Let’s begin by taking a look at three areas—human data labeling, AI communications, and human-to-machine collaboration—in order to get a better idea of how the dots might connect for the middle strata in the next few years.
Human Data Labeling
For a few years now, AI has been touted as a workforce liberator with its potential for dramatically reducing the number of repetitive, tedious tasks that many workers experience today. But until that day comes, AI will require humans to manage the repetitive, tedious tasks associated with data labeling where millions of pieces of data and images are manually inventoried.
AI can’t function properly without data. This is how AI learns. With data labeling expected to be a billion dollar market by 2023 we’ll likely see the pace of adoption of AI in business speed up in 2020, particularly as automation providers offer more entry-type solutions that get companies excited about introducing AI in the workplace.
As more companies introduce chatbot assistants into their service and support lifecycles, we’re seeing the evolution of this technology in call centers as they utilize AI as a means of providing better customer experiences.
For example, by monitoring conversations between an agent and the customer, AI learns to listen for emotional triggers (sentiment analysis and emotion processing) where a well-timed intervention by a (human) supervisor via a quick chat with the agent could serve to de-escalate a situation and close the call with a satisfied / delighted customer. AI will determine patterns over time and learn what a top notch customer experience looks and feels like and how support agents are able to deliver consistent, repeatable great customer experiences.
Scaling the use of AI personal assistants in business could increase workflow productivity by helping humans work smarter. How many of you would delight in having an AI assistant manage many of the mundane tasks in your workday? Or what about being able to augment your research by sifting through large data sets that provide you with insights you might have missed?
As enterprises look to operate their businesses better, faster, and at less cost by improving customer satisfaction, increasing workflow productivity, closing better deals and hiring / onboarding talent, AI personal (also referred to as intelligent) assistants will be a space to watch.
With the advancement of Natural Language Processing (NLP) we’ll see smart machines become more prevalent in the workplace. Functional human-to-machine collaborations will grow in importance for getting things done in new ways.
We’ll see a concentrated effort to move the machine learning (ML) needle forward, faster in the next few years. How can AI become smarter? How can these emerging technologies improve efficiency and productivity? How can AI increase creative problem solving by eliminating redundant tasks and improve decision-making?
We’re already seeing this in communications and project management software where AI tracks the progress of planned schedules, alerting teams of potential risks and delays, and making recommendations on how to bring the project back on track.
McKinsey found that only 30% of the 1,800 software projects analyzed for their study were delivered on time making the potential for direct and indirect cost savings huge as AI embeds itself into projects and assumes many of the responsibilities managed today by PMs and project specialists.
Let’s connect a few dots
Now that we’ve looked at a few AI focus areas let’s connect the dots about what this could mean for the middle strata—program and project managers, change practitioners, and mid-level leaders.
With data labeling set to become a billion dollar market by 2023 we can expect automation providers to launch more products and services which, in turn, could trigger an uptick in companies willing to speed up their timeframe for purchasing AI solutions—refreshing business strategies and adjusting integration milestones, accordingly.
Program and project managers will need to be prepared for what could be quick shifts in priorities and budgets. You’ll be tasked with learning the software first before implementing it within the organization—one step ahead of everyone else. Setting up an agile beta program before rolling it out to the larger organization will leverage your ability to adapt, pivot and think on your feet.
Artificial intelligence will simplify many of the tasks that project managers and project specialists manually track and manage today. Much of the granularity associated today with managing project details will not be a “wheelhouse skill” of the future.
Change strategists and practitioners will continue to play significant roles in retooling AI strategies and implementing advanced technologies for big and mid-sized companies. Even as change managers can expect automation to support their day-to-day tasks, e.g., bots and personal assistants, a key focus area for change teams is to build, grow and retain stakeholder trust due to the propensity for resistance and fear associated with AI and automation. It is the job of change professionals to help pave the way—support a vision of what can be achieved and streamlined with the deployment of advanced technologies. It’s about preparing the workforce to take advantage of opportunities and to mitigate the risks associated with transformational change.
Mid-level business leaders represent strategic operations partners for AI initiatives, programs, and projects coming down the pike. This role is critical. As both a key stakeholder and business implementation partner, this dual role is responsible for ensuring the success of strategic AI efforts. It’s imperative that as a mid-level leader you understand the dynamics associated with resistance to change as your role is instrumental in helping to lead people through transformational change.
Career professionals who make up the middle strata of most large companies should be taking aggressive steps to strengthen foundational capabilities and to close skill gaps that they’ll need to tap in future.
The future of project management is out of the weeds and into the trees. Program and project managers might want to take a page out of the still evolving data scientist playbook where their role is examining and analyzing data, then presenting their findings and recommendations to decision-makers.
Scope out any AI beta projects inside your organization—get yourself some practical experience. Approach the Project Manager overseeing the project or the Change Manager on the team to see if they could use an extra resource. Sell it to your manager as a stretch goal + supporting metric that benefits your current role and work group.
Check out Andrew Ng’s online course—AI for Everyone—a baseline for understanding artificial intelligence.
Begin a formal reinvention process ahead of the curve—where an inflection point might be anything from a surprising economic turn of events to a technological breakthrough that speeds up the adoption and implementation of AI in the workplace. Check out our Seeding Change online course 30 Days to a Recession-Proof Reinvention and this summer’s AI in the Workplace.
Barring an unexpected inflection point, AI in business should remain fairly stable for a year, maybe two, as companies assess advanced technologies and determine the types of jobs and skills needed to compete in a global market. Why not make the most of this opportunity window?