The COVID-19 pandemic forced companies to move quickly to shift to remote working, reimagine how they serve customers, build new business models, enhance technology capabilities, assemble multi-disciplinary teams and expand partnerships and alliances in ways they never thought possible. Now, companies need to figure out how to keep that momentum and maintain the speed and agility they gained in a world shaped by changing customer demands and ongoing economic uncertainty.
At the heart of the issue for many companies is an outdated operating model that is too big and too slow to respond to major market and competitive changes. According to Accenture research, 85% of C-level executives say they are not very confident that their operating model can meet shifting strategic priorities. Companies need operating models that propel new strategies aimed at boosting growth and innovation. Our research also shows that the long-term EBITDA growth for truly agile organizations is 16%, compared with 6% on average for non-agile organizations.
Last year, I discussed how to reimagine your organization as an intelligent enterprise by using technology in new ways, adopting agile ways of working and transforming value chains. While it’s clear the intent is there, and company leaders are working toward putting in place intelligent operating models, many still aren’t fully achieving the speed and agility they desire. In part, that’s because they’re getting caught up on several misperceptions of what it takes to undergo agile transformation—the biggest one being that they need a blank canvas. Yes, it’s true that legacy systems and complexity can make agility harder to attain, but it doesn’t make it impossible. Here are five key starting points that companies can take to achieve agility even without a clean slate.
Targeted experimentation
Controlled experimentation is a great path of choice for companies that have more traditional operations and can’t immediately establish the necessary buy-in for new ways of working and technology. Prove first; then scale. Looking at ways to “hack” the organization and use data to experiment with and continuously improve the organization.
