CUTTER BUSINESS TECHNOLOGY JOURNAL,  VOL. 29, NO. 12
  

Today’s work environment is changing rapidly, so much so that the ever-increasing velocity of innovation and technology will create a workplace five years from now that looks nothing like the one of today. The big three factors in this coming change are:

1. The “gig” economy, or project work, becoming the norm

2. Moving from networks to ecosystems

3. Augmenting human work with AI systems

The Gig Economy

I have been a member of the “gig” economy for the last 20 years — I just didn’t know it. Because of my expertise in collaboration, I have been called on by both vendors and end-user organizations for everything from product design to training and coaching, competitive analysis, and motivational team workshops. More recently I have been testing audio components in a meeting room lab; by having meeting participants wear wireless EEG (electroencephalogram) helmets, my colleagues and I are trying to correlate their behavior from the audio interaction with their internal state (mad, sad, glad, paying attention, etc.). What all of these activities have in common is that they are all projects, and they are all being done for different organizations.

A recent study by Intuit predicted that by 2020, 40% of US workers will be part of the gig economy (con­tractors), and as a result we have seen the rise of a number of work platforms such as Elance, oDesk (now combined into Upwork), and many others. Some focus on geographies, while others focus on specialties and expertise (e.g., Maven).

A recent study published by MavenLink and GigaOM looked at the state of the services economy. In this survey of almost 500 executives, 78% of respondents perceived that the pace of change is not only quickening, but it is doing so faster than ever before. It is this velocity of innovation and disruption that is having such a great impact on the way we work today and will work in the future. Of those surveyed, 58% noted that the average length of their contracts was shortening (from years to weeks), and that over 90% of this work was primarily project work rather than retainer work. The bigger the company, the more swiftly they are changing to project work. And nearly all (96%) of service-oriented providers (lawyers, software developers, research scientists, accountants, etc.) are seeking new contractor relationships.

What this means for IT and most businesses is that more of your inhouse staff will be managing contractors external to the organization. Training in this type of team or project management would greatly increase the likelihood of these projects going smoothly. Creating a stable of regular contractors with whom you have built trust is also a good idea.

The way teams work has also been changing. Teams used to be colocated, but today the norm is for teams to be geographically distributed. Talent knows no boundaries, and thus team members can be from anywhere. Yet the biggest challenge to teams is not geography or technology (which helps to provide a common context for work), but behavior. Work by Pamela Hines of Stanford University and Catherine Cramton of George Mason University looks at addressing the behavioral disconnects in cross-cultural teams:

It takes time to learn about cross-cultural differences, understand distant team members’ context, and determine ways to reconcile these differences and adapt for thegood of the collaboration. Long-term, interdependent teams with stable membership show that the organization values its investment in the team.

Once a high-performing (distributed) team is stable and able to complete a project effectively, that team will often stay together to work on other projects. Or, as in the movie or construction industry, if members of these teams move on to other different work, they often come back together in the future to work on new projects.

But while teams are changing, work is changing even faster. It will require entrepreneurs and small businesses to start evolving their networks into ecosystems.

From Networks to Ecosystems

The gig economy is helping to drive this transition from networks to ecosystems. If you have been in business for any length of time, you know lots of people, and these people you know make up your network. Today, however, networks are not enough — relationships are the key. What matters is the ability of two or more individuals or organizations to have a common goal or desired outcome and to work toward that in a coordinated manner. Working with others you can trust and count on, and whose experience and expertise you can leverage, is what is needed to create an ecosystem.

To develop an ecosystem, you must find a few network partners and establish a closer relationship with them. But more than just an ecosystem, it has to be a purpose-driven business ecosystem (PDBE); that is, there must be a project with a business goal and outcome that all of the partners participate in. The best source of information on this phenomenon is a recently published book by Robert “Kim” Wilson titled They Will Be Giants, which deals with PDBEs for entrepreneurs.

As part of Wilson’s GIANTS ecosystem, I have started to contribute by suggesting collaboration software to provide the glue for the ecosystem and recommending other methods for sales and marketing. “Ecopartners” also reciprocate, so one of the partners is helping to create a new website with different offerings for my business.

The common goal of the GIANTS ecosystem is to provide mentoring and services (sales, marketing, and strategy) to entrepreneurial organizations. We do this through the expertise of our members as well as through our partners. The GIANTS ecosystem itself currently provides strategy, marketing, sales, and customer development services to startups, and through our ecopartners we provide legal, recruiting, PR, and accounting services, too.

As an ecosystem, GIANTS is also looking at ways we can augment our experience, expertise, and reach. One of the ways to do that is through artificial intelligence.

Augmenting with AI

A natural extension of our PDBE would be to start using AI technologies in different ways. For example, we are using WizCal (an AI-driven calendaring system) to help set up optimal meeting times and the like, since as a geographically distributed team we have to deal with that problem on a daily basis.

Indeed, AI systems will begin taking on numerous tasks, from scheduling our days to driving our kids to school, from IT administration to data mining. We are starting to see virtual assistants of many kinds popping up in our lives. Apple, Amazon, and Google all have devices on the market that can help play your music, adjust your lighting or heat, do searches for you, or remind you to do something later. They can also help you create meeting agendas and reports and find critical data for budgeting,

AI systems (and robots) have the potential to make changes to our society that are as sweeping as those of the Industrial Revolution. Many jobs done today by people will become jobs that robots and AI can do better. Once autonomous trucks are certified, for example, truck drivers will need to find new jobs. This is already happening with taxi drivers thanks to the use of shared cars through Uber and Lyft. Ironically (or perhaps fittingly), Uber drivers themselves will be out of business just as soon as Uber can automate its fleet. Even writing editorial copy is a task that is already yielding to AI.

But the news is not all bad. There may be some less-skilled jobs lost, but there are many more-skilled jobs waiting to be filled. What is needed is a massive retraining program, like the US’s Depression-era New Deal, only building on the information highway instead of physical dams and bridges. Some of these jobs may be in cybersecurity or programming. According to an analysis by Stanford University’s Peninsula Press, there were over 1 million open cybersecurity jobs in 2016 in the US alone. And the US Bureau of Labor Statistics projected that computer programming jobs in the US would increase by 28.2% in 2016.

That said, there are many other jobs that might be more appropriate for newly retrained low-skill workers. It does not take great imagination to see someone who used to drive and fix trucks now working on wind turbines or solar panels. Jobs that require judgment and knowledge of the surrounding environment can often be done better by people than AI systems or robots. Many individuals who find themselves out of jobs will create their own new jobs as entrepreneurs, creating products and services that we have not yet thought of!

For most businesses, I would suggest adopting AI systems slowly, using them in areas where people don’t currently occupy those jobs. For instance, there are most likely AI systems that can help determine optimal network configurations and routing paths on your corporate ­network (which could save you lots of money in data storage and network infrastructure). Another example is sorting through huge mounds of data. As analytics expert Tom Davenport observes:

I initially thought that AI and machine learning would be great for augmenting the productivity of human quants. One of the things human quants do, that machine learning doesn’t do, is to understand what goes into a model and to make sense of it. That’s important for convincing managers to act on analytical insights. For example, an early analytics insight at Osco Pharmacy uncovered that people who bought beer also bought diapers. But because this insight was counter-intuitive and discovered by a machine, they didn’t do anything with it. But now companies have needs for greater productivity than human quants can address or fathom. They have models with 50,000 variables. These systems are moving from augmenting humans to automating decisions.

Likewise, using AI to augment what people can currently do is a better way to start than by wholesale replacement of jobs. For example, IBM’s Watson is developing systems that will “help healthcare providers better understand patients’ diseases and recommend personalized courses of treatment.” Watson-like tools can also assist experts in doing their jobs better. Not only can they help experts improve their decision making, but they can also aid in the continual learning and training that experts require.

Conclusion

The nature of work is changing, team demographics are changing, and even the idea of an organization is changing. In many ways this velocity of change is causing us to need greater bonds of trust (ecosystems), rather than broadly scaled networks (or social networks). We have reached an inflection point where people by themselves will no longer be able to keep up with the rate of change and what they need to know, so having AI systems augment those human capabilities is the first step in dealing with a constantly evolving future.

For IT, some things will remain the same (at least for the next few years) and other things will change more quickly. We are already seeing the move of ERP systems to the cloud, and more and more companies are using consultants or contract hires. About 40% of the teams my Collaborative Strategies colleagues and I have surveyed are geographically distributed, and we expect that figure to be about 60% by 2018. As noted earlier, 40% of the US workforce will be part of the gig economy by 2020, so that transformation is happening veryrapidly.

Finally, we are starting to see AI used in everything from meeting scheduling to medical diagnosis. My best advice here is to pick your battles. Add AI systems where they can intelligently help and mostly aim to augment the abilities people already have. Look at your most overwhelmed employees and ask yourself, would the application of an AI system or additional contract resources improve that employee’s productivity, morale, and likelihood of staying with your organization? If the answer is “yes” to any of those, then you will be swiftly moving into the futures I have predicted.