A quick guide to scaling the digital workforce

Amalgamating lessons learned ensures a scaled digital workforce

Seth Adler

What is the Digital Workforce?

The digital workforce is comprised of machine-based intelligence or rules-based automation.  Artificial intelligence, intelligent automation, robotic process automation, and machine learning have been infused into global corporate enterprise as a means of improving productivity, cutting costs, growing revenue, retaining talent, and reducing the amount of repetitive, menial tasks for human workers. AI and IA are impacting businesses by modernizing technology, streamlining processes, outpacing the competition. AI also helps your enterprise identify niches and new potential products or services. 

As stated in our Enterprise AI Report, the most common goals of AI are:

  • Improve productivity
  • Improve job satisfaction
  • Increase sales/revenue/profit
  • Streamline core business operations
  • Excellent customer service/satisfaction
  • Enable efficient security

Most workers at essentially every global organization are not as productive as they could be due to their being bogged down with mindless tasks. The ideal vision of a digital workforce does not eliminate human workers, but instead optimizes that under-utilized human talent while harnessing the power of cognitive technology to take care of the tasks that take away from human potential.

Additionally, that ideal digital workforce vision showcases predictive abilities to assist the enterprise in making the right transformative decisions. The general consensus seems to be that enterprises must be proactive about not only implementing but optimizing a digital workforce to keep pace with market demands. This type of digital transformation is no longer optional.

Driving an AI-Enabled Culture

Executives and senior management must strongly invest in and support holistic change management strategies. There is no end to the digital transformation journey. But there is a beginning to ensuring that the enteprise reaps true reward from the digital workforce- setting a roadmap. While that roadmap takes into account technology and process, global corporate enterprise has realized that a people-centric approach is working best. 

Fostering a workplace culture that respects and cherishes its people, enables ease in attracting top talent— smart, resilient, adaptable, and flexible people. Having the best people on your team is essential for building, implementing, and maintaining a successful digital workforce. The collective intelligence of your human and digital workforces provides the greatest opportunity for success.

Training, upskilling, and reskilling your current workforce is the next step of the digital transformation journey. Training should be an ongoing, strategic initiative rather than a half-hearted, one-time-only initiative. Leading enterprises are investing in transformation training programs for employees and providing the tools and knowledge that they need to harness technological advances.

Many enterprises that have successfully implemented their digital workforce strategies have, as part of their change management, established Centers of Excellence (CoE). A CoE is, in essence, its own department designed to guide and supervise an enterprise’s change management:

  • Provides leadership and ongoing training throughout the enterprise (workshops, mentoring, regular check-ins, etc.)
  • By building their own knowledge, they are able to provide insight to the rest of the enterprise
  • CoEs test pilots but, more importantly, deliver results
  • Initiatives are clearly defined, baselines are measured, and specific metrics and goals are created

A Digital Workforce Vision

The pace of change of this fourth industrial revolition is unique. While it is important to recognize the urgency of adopting a digital workforce, it is equally important to be deliberate in identifying desired final outcomes and long-term goals. Implementing technology with the sole reason of not being left behind is fraught with danger.  Indeed, starting with a long-term, fully fleshed-out vision is imperative. Ask the following questions:

  • What are we providing to your customers now?
  • How can that change with a digital workforce?
  • What does that mean for the future of your enterprise?
  • Will our goods/services change?
  • How will that affect our consumers?

Elements of long-term thinking include not allowing fear of the unknown to be a barrier to entry, not immediately linking success to ROI, and being willing to fail and learn as we covered in our AI2020 Update Report.

Rather than ROI, an accurate long-term marker of a successful digital workforce is improved customer experience and improved employee experience. If consumers are happy, the numbers follow. When enterprises try to prematurely remove human workers from complicated processes, inevitably extra costs and significantly longer deployment times follow.

Fusing Business & IT

Unfortunately, implementation attempts often end up falling flat due to the disconnect and competing priorities between business and IT, as this type of competition commonly leads to a rushed mentality and desire to prove use cases prematurely.

The enterprise, from both the business and IT sides, must come up with clear and concrete expectations about a digital workforce governance that works. Roles from both sides should be outlined clearly and carefully in order to mitigate the risk of friction and misunderstanding. No matter what an enterprise decides to do, the business and IT must be working together with a clear, outlined path forward.

Standardizing Data

Having set clearly set out to optimize processes, the next big step for enterprise is to optimize data. Although Robotic Process Automation (RPA) is rules based and not intelligent, it has been an excellent first step for many enterprises and is an important driver for AI in process automation:

  • RPA requires structured data, and, although 80% of your data is unstructured, RPA goes through your unstructured data and converts it to structured data. This is a useful initial method for gaining insight from data.
  • By automating processes with RPA, enterprises have a better understanding of automated decision-making and of the potential role of AI in the enterprise.
  • Enabling ML advancements on top of your RPA-stack is essential to procuring the optimal data for the current and future enterpruse

Predictive Analytics

In order to master data and turn it into relevant, functional insights, you must first cleanse, mine and prepare that data. This process means understanding which historical data are valuable for future AI decision-making. Although these steps may be painstaking, gaining this type of insight is something mandatory; using data incorrectly is just as bad as doing nothing with it at all. RPA and dynamic cloud strategies are two great, arguably necessary ways to gain insight from data. Optimized RPA augmented with ML leads an enterprise to vigorous  predictive analytics. You can read more about predictive analytics here.

Key takeaways

  • Be people centered
  • Encourage a collaborative, adaptable, and transparent workplace culture that will be open to change
  • Invest in change management, training, and upskilling/reskilling of your current work force
  • Create a long-term vision and refuse to implement technology for the sake of technology
  • Align business and IT
  • Start implementing your strategy by gaining insight from your data through RPA and dynamic cloud strategies
  • Don’t be afraid of failure and don’t measure success only by ROI

The intelligent automation journey has uncovered certain truths. Enterprise must have:

  1. An AI-enabled culture
  2. A long-term AI vision
  3. Business & IT in lock-step
  4. Standardized Data
  5. Predictive Analytics Implemented

Five out of five must be true for the enterprise to scale the digital workforce.