The rise of artificial intelligence in the workplace to enable and sustain the digital workforce is an apparent trend for 2020.

Artificial intelligence, machine learning, neural networks or whatever other fancy terms industry is coming out with for what is defined as the sophisticated computer technology that is becoming widely utilized to understand and improve business and customer experiences. I assume you have heard of it before, but the way it is defined today is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans.

Here are ten AI trends to be on the lookout for this year:


The need for Digital Intelligence.

Digital IQ, as the measurement of how well an organization can understand its business processes and the content and data within them from a variety of critical perspectives, will play an increasingly important role in every digital transformation strategy as more enterprises come to the realization that they must have visibility into their operations.

Digital Intelligence solutions will help organizations increase this business-critical ability by optimizing automation initiatives and complementing platforms like RPA and BPM. In 2020, more organizations will adopt digital intelligence technologies into their overarching digital transformation initiatives, as enterprises realize that these solutions illuminate paths to improved customer experience, reduce operating costs and sharpen competitive advantage. (As previously published here.)


Digital workers will transform the office.

As I’ve talked about recently, the growing use of digital workers is happening across the globe. According to a new IDC research, the contribution of digital workers — such as software robots and AI — will grow by over 50% by 2022. (As previously published here.)

A digital worker for every human worker. Expect many digital robots to be taking up minimalist tasks in the office. Digital workers will be trained to carry out a business task just like any employee, only faster and without mistakes. All employees across the enterprise will have digital workers working alongside them in the future. This will become very normal in the workplace and you will surely be seeing more of these technologies in your workplace (keep reading to see why).


The proliferation of Process Intelligence.

If you haven’t heard it before, you will be sure to see it this year. Process Intelligence allows businesses to use the information contained within their systems to create a visual model of their processes, analyze them in real-time to identify outliers and bottlenecks, and predict future outcomes to facilitate decision-making of technology investments.

As more complex digital transformation technologies are deployed, the ability to monitor operations across every facet of the organization becomes increasingly important. Individual technology systems that govern very specific functions (CRM, ERP, CMS, EHR, etc.) only provide visibility into the processes that their platform controls. None of these standalone systems can provide insight in a holistic and in-depth manner.

To attain this visibility, organizations will need to leverage Process Intelligence technologies that provide a comprehensive, accurate, and real-time view of all processes — across departments, functions, personnel, and even different locations. In the coming years, more businesses will realize that Process Intelligence enables organizations to better understand and more effectively manage their processes end-to-end, and these technologies will subsequently become a standard in the enterprise. (As previously published here.)


AI not only for the consumer user but the enterprise too.

As consumers, we experience AI daily, often without knowing it’s being used. In the enterprise, expect that in 2020 process owners and those leading improvement initiatives for the customer experience will see a dramatic increase in their involvement and access to artificial intelligence tools.

Many businesses have spent millions of dollars on digital transformation projects that were never aligned with the needs of the business. Of course, they then still wonder why they failed. Ensuring digital success in the enterprise 2020 and beyond will only be possible by solving a business problem and, furthermore, ensuring it is solving the right problem. Don’t just have AI or RPA just to have it — align technology to the business.

As Nathan Furr and Andrew Shipilov explained in their article that broke the common myths on digital disruption in the Harvard Business Review, “Managers often think that digital transformation is primarily about technology change. Of course technology change is involved — but smart companies realize that transformation is ultimately about better serving customer needs, whether through more-effective operations, mass customization, or new offers.”

AI will become much more easily consumable in the workplace. Business users will soon have access to internal marketplaces of robots and other easy-to-use automation tools available to people of all technical proficiencies. These new platforms will play a role in improving the way that employees get work done so that customer experiences are improved and processes are better than the competition.


AI will be monitoring and improving business processes.

But you won’t be able to improve what you do not understand and measure.

While simple task automation has been common practice in the workplace, hyper-automation (as mentioned in Top 10 Tech Trends for 2020), and intelligent, cognitive automation projects depend on the ability to integrate AI-enabled tools to reshape and redefine how business processes execute in real-time. RPA, by itself, is not intelligent; it is simple rules-based task automation.

Derek Miers is a senior director analyst head of Gartner’s Magic Quadrant for RPA software. Miers was at this year’s Gartner Symposium/ITxpo where he gave a talk that was surprisingly critical of RPA as it exists today.

“What you’re doing is covering the organization in Band-Aids in the hope of a wellness program,” explained Miers. “You’ve got to fix the processes. So, what you’re really doing here is building like a little facade if you like, a little shop front in front of your old applications that you can reuse.”

Enabling cognitive automation will require new tools built for the task. AI enabled Process and Content Intelligence technologies will provide digital workers the skills and understanding necessary to deal with natural language, reasoning, and judgment, establishing context, providing data-driven insights.

RPA tools alone aren’t intelligent robots built with AI, therefore they require AI-powered solutions to achieve intelligence. The ability to monitor processes across the enterprise and trigger tools like RPA, capture or something else will be handled by tools that understand processes (See Process Intelligence section above) and the content that is the payload of the process.

Put simply, the role of RPA is to automate repetitive tasks that were previously handled by humans. The software is programmed to do repetitive tasks across applications and systems. The software is taught a workflow with multiple steps and applications.”– Antony Edwards, COO at Eggplant.


The normalcy of the hybrid-workforce — Human and AI cooperation.

Many people see artificial intelligence and automation within the enterprise as job killers. The emerging hybrid — human and robotic — workforce is alive and growing. Organizations are swiftly implementing cognitive AI and RPA that can handle high-volume, repetitive tasks at scale. As more and more use cases emerge the hybrid workforce grows.

Getting buy-in might be difficult, but business leaders should be open and honest with employees giving them transparency into how AI will be used and what will be the lasting impact on current human workers and their daily work lives

Overall, whether your organization works to gain buy-in or not, the message is kind of clear: Get used to it. In a not so distant future, you will probably be working alongside AI-powered tools, digital workers, and bots in your day-to-day work. The hope is that these digital workers are aligned with the business and help to solve problems that accelerate your time to value.


More human interaction with AI.

The normalcy of AI in the workplace will also be the reason we see more human interaction with AI. We will be expected to live and work alongside AI as we already live with Alexa, Siri, and other digital assistants. What will your intelligent digital worker’s name be?

As technology capability improves, regulation permits and social acceptance grows, more AI will be deployed in uncontrolled public spaces. I imagine more of us will interact with AI, maybe without even knowing it. While we have come to understand that customer experiences are often improved and customized based on our personal profiles and interest, I expect many other forms of interaction with AI, even where we don’t see it happening.


Data as the oil fueling the AI fire.

As Billy Joel said: We didn’t start the fire; It was always burning; Since the world’s been turning; We didn’t start the fire; No we didn’t light it; But we tried to fight it: Not too long ago, businesses looked at data as the excess exhaust coming out the end of many business processes and transactions. There has been a big shift here, organizations big and small now invest in systems and methods to collect and record all of the data they can — for improvement of course!

The rapid growth in data, the reduced cost in storage, and the ease to access it has grown incredibly in the last 25 years. Data is driving the improvement of the customer experience, advancing analytics capabilities (especially in the new realm of process data, and Process Intelligence), enabling machine learning and AI, and allowing businesses to harness real value from intelligent automation that is driven by data.


AI boosting cybersecurity.

Artificial intelligence will give chief information security officers an impressive new advantage in the ongoing efforts to improve cybersecurity.

Although AI can enhance security, it’s not a cure-all. “AI won’t solve all your security problems,” says Raja Patel, former vice president and general manager of corporate products at security company McAfee, and now vice president, security products at Akamai Technologies. “Think about it as a way to advance the security posture, not as a silver bullet.”

Future AI security will have 3 key perspectives as I noted this in the Top 20 Technology Trends for 2020 article.


More AI doing AI things — automated AI development.

“In 2020, expect to see significant new innovations in the area of what IBM calls ‘AI for AI’: using AI to help automate the steps and processes involved in the life cycle of creating, deploying, managing, and operating AI models to help scale AI more widely into the enterprise,” said Sriram Raghavan, VP of IBM Research AI. Source: The Next Web.

In 2019, IBM launched AutoAI, a platform for automating data preparation, model development, feature engineering, and hyper-parameter optimization.

Look out for developer technologies that include distributed deep learning, enabling developers to build AI engines faster and more effectively. The automated machine learning will make AI development available to a more diverse group of developers.


Overall, artificial intelligence has the ability to reshape and redefine the way we live and work. The growing trend we should all expect is to see more and more AI-enabled solutions in the workplace. These tools will help create new user experiences, better outcomes, and ensure we are achieving our goals in a timely and efficient fashion. When thinking about the needs of the hybrid workforce, leaders need to decide if simple task-based automation tools are the answer to their problems, or if they will require a mix of AI and other transformative technologies to achieve real intelligent and cognitive automation.

The choice remains — will your organization be a leader in enabling successful and sustainable digitization powered by AI or will you remain still and stagnant because of the fear of change.

Via TowardsDataScience.com