AI adoption has witnessed a monumental surge in recent years. The COVID-19 pandemic has accelerated enterprise AI adoption as businesses pushed for digital transformation while a majority of the workforce was working remotely. However, generating significant return on investment (ROI) from AI-powered applications can be a complicated task for business leaders. Business leaders need to be aware of the changing landscape of their industry and use an agile approach for AI implementation. Along with these, businesses need to understand how to identify and utilize the strengths as well as assess the risks of AI utilization in a specific situation.

While nearly every company has adopted digital transformation buzzwords in recent years, the actual implementation of these disruptive technologies remains another story. For a variety of reasons, new technologies often fail to meet expectations. One example is artificial intelligence, which is an advanced technology that companies cannot ignore in an era of increasing digitalization and remote work. Many organizations deploying AI projects through tactical initiatives that seek to deliver an immediate payback have learned that this approach rarely delivers meaningful value. AI is a powerful tool, one that has the capacity to redefine entire industries.

In the past five years, one trend that has made AI more accessible and acted as the driving force behind several companies is automated machine learning (AutoML). Many companies such as H2O.ai, DataRobot, Google, and SparkCognition have created tools that automate the process of training machine learning models. All the user has to do is upload the data, select a few configuration options, and then the AutoML tool automatically tries and tests different machine learning models and hyperparameter combinations and comes up with the best models. Does this mean that we no longer need to hire data scientists? In fact, AutoML makes the jobs of data scientists just a little easier by automating a small part of the data science workflow.

Many companies are willing to throw themselves in the fascinating world of AI and learn from the benefits it brings. Usually the story goes as follows: the directors committee decides that their company should lead the AI innovation ecosystem in their industry. In order to this they hire a team with great skills, spend a vast amount of money on resources and after a few months'PUM' they find themselves at the starting point with less money and no trust at all for AI. What mistakes have these companies made? Is AI just a hype and has no benefit at all?

I recently surveyed danish CIO's(Chief information officers) about their relationship with AI and I had some interesting results. One of the results was that one of the biggest barriers to get started on AI projects is that building the business case is difficult. I completely understand the issue and I agree with the CIO's. Building an AI business case is difficult and if you try to build it as a traditionnel IT business case it's down right impossible. Building a business case is all about understanding the cost and revenue drivers well enough to work them into a model that yields a profit with high certainty within an agreed timeline.

Agile data governance improves data assets by iteratively capturing knowledge as data producers and consumers work collaboratively. "Our platform was built for a modern workforce where data-empowerment and data stewardship co-exist," said Jon Loyens, Chief Product Officer and co-founder of data.world. "Being able to access the data you need and collaborate remotely on critical datasets is more important than ever. Taking an agile approach to data governance addresses these needs by simplifying data requests, adding transparency to how data is being used internally, and automating workflows so the entire business can get more from its data." These new agile data governance capabilities mean organizations are no longer constrained by data silos, complicated workflows, or opaque data resources.

At SAP InnovationX this week, one of the first break out talks was delivered by Cloda O'Dea, Human Resources Executive at EY. She spoke about the experience of implementing SAP SuccessFactors across the EY global business. This was a massive undertaking considering that EY employees 260,000 people in 150 countries across four service lines. Her role is the Transformation Director within the internal HR team. They were assisted by the EY SAP SuccessFactors practice.

The unbridled enthusiasm for digital transformation has prompted enterprises to storyboard roadmap to embrace the next wave of technology. The pace at which technology is mushrooming around, enterprises are in the dire need of automating labor-intensive business processes. This is where Robotic Process Automation-led digital transformation has acquired the center stage. RPA successfully replicates human actions to handle heaps of repetitive tasks that normally involves multiple human resources fully engrossed in completing them without adding noticeable business value. Needless to mention AI, machine learning and other technologies have emerged on the scene to make this happen, and yet Robotic Process Automation has proved to be impactful as it can smartly mimic human actions and perform mundane technical tasks and thereby evolving workforce to perform other high-value tasks. It can quickly fix perpetual legacy system problems such as integration, migration and information sharing issues leaving fragmented customer experience behind.