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Ideas Made to Matter
4 digital transformation insights from MIT Sloan Management Review
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Companies around the world are adapting to new ways of doing business, with automation and artificial intelligence playing an important role amid the ongoing pandemic. These insights from MIT Sloan Management Review can help ensure digital transformation initiatives are successful while also resilient in the face of new disruption.
The 5 digital capabilities critical to success after the pandemic
As enterprises consider what digital transformation will look like after the pandemic, MIT Sloan senior lecturerencourages business leaders to leave behind their pre-pandemic assumptions about innovation. Instead, he said in a recent webinar, lean into how COVID-19 forced enterprises to change for the better. The collective response to the pandemic challenged longstanding notions about the efficiency of remote work, the agility of corporate IT departments, the rigidity of government regulators, and the willingness of customers to embrace (and pay for) digital interactions.
To adapt to these changes and “keep the digital momentum going,” Westerman highlighted five digital capabilities that provide enterprises opportunities for growth and market differentiation.
- A customer experience that emphasizes emotional engagement and is further influenced by customer intelligence data.
- Operations that are more connected, more dynamic, and more informed by data-driven decisions.
- An employee experience that provides workers the skills and the flexibility to adapt to change.
- A business model that embraces a multisided platform strategy and offers digital enhancements to existing capabilities.
- A digital infrastructure that consists of core back-end systems, external-facing applications and services, and an increasingly valuable data layer.
To ensure success, executives must complement these digital capabilities with five core leadership capabilities: Vision, engagement, governance, technology stewardship, and a culture that’s digital-ready. Merging these capabilities ensures that transformative technology is integrated to provide end users and customers with a unified experience. And according to Westerman, it leads to a lower cost structure and higher profit margin.
How to support employees as roles within the enterprise shift
Today’s enterprises face a one-two punch of major transitions. Along with the digital transformation necessary to succeed in the 21st century economy and recover from the pandemic, companies must confront the impact of remote work and the return to the office. MIT Sloan senior lecturersandcaution leaders not to focus on how tasks are changing — after all, most workers have dealt with new tasks before — but to look at how organizational change can impact the individual sense of self.
Amid major transitions in work and in life, it’s hard to see the forest for the trees. Individuals may not see themselves filling a particular role within an organization, whether it’s the idea person, the negotiator, the problem solver, or the generalist. (The authors make a point to distinguish a role from a job title, suggesting that a role more accurately describes an individual’s true impact.) Nor may they see that these roles need to be dynamic as an organization grows and changes.
Gregersen and Lehman recommend an approach called organizational role analysis. This amounts to a SWOT analysis that examines how organizational changes impact an individual’s existing role. The result is an action plan for reshaping the tasks that workers concentrate on as they adjust to new roles. Done right, these adjustments can make individuals’ roles clearer, more aligned, more manageable, and more autonomous amid significant transitions.
4 key areas of AI activity amid industry transformation
Like many companies, the clinical research organization Parexel International faces disruption on several fronts. Not all are necessarily negative. As two executives told Thomas Davenport of the MIT Initiative on the Digital Economy and NewVantage Partners CEO Randy Bean, there are challenges like collecting data once products are on the market and processing real-world evidence. But there are also opportunities such as testing in a wider geographic area, transitioning clinical trials into patients’ homes, and simulating the impact of a drug rather than directly injecting it into a test subject.
Effectively harnessing artificial intelligence capabilities will play a critical role in determining how well Parexel can respond to these disruptions. To that end, the company emphasizes four major areas of AI activity:
- Automation reduces the manual labor burden for common administrative processes.
- Risk detection scans for fraud and compliance issues across a range of data types, from clinical trial reports to medical device data to social media content.
- Data analysis plays a key role in post-market surveillance of product safety and effectiveness, especially when multiple drugs are used in combination.
- Prediction and monitoring focuses on whether patients respond to a treatment as predicted — and what may be contributing to a lack of response.
For organizations such as Parexel, Davenport and Bean write, it’s more important to make progress toward creating a data-driven culture and developing data-driven products than it is to prematurely declare their AI activities a success. Even when taking an aggressive approach to AI adoption, there’s much work to be done.
Why enterprises should create an “automation center of excellence”
Enterprises invest in a range of automation use cases, including marketing campaign automation, document processing, database oversight, and “low-code” computer programming for nontechnical users. Interest in automation was picking up before 2020, and COVID-19 accelerated it: A Deloitte survey found that two-thirds of business leaders incorporated automation into their pandemic response.
With disparate categories of automation emerging, enterprises risk mismanagement of resources. Writing with Gina Schaefer, a managing director with Deloitte Consulting LLP, Davenport suggests companies establish an automation center of excellence to set a vision for the use of automation. Whether the center is placed within the IT, data science, or quality management functions, the first step for the center’s leadership is to determine what humans can do better and what machines can do better. A “humans with machines” workplace will foster greater collaboration, engagement, and acceptance from employees, Davenport and Schaefer write. Coordinating with human resources can help address concerns employees may have about skills training, job descriptions, or personnel reductions.
From there, the center of excellence should set a course for how the business approaches automation. There are four common approaches:
- Ensure a return on investment by linking automation to process improvement.
- Prioritize efficiency over savings by scaling automation as fast as possible.
- Identify opportunities to reduce administrative costs, particularly for financial processes.
- Automate repetitive tasks to free employees to do more creative and interesting work.
Regardless of the approach, it’s critical for the center of excellence to first measure and then aggregate automation’s benefits and value to the organization at large.
