Build a Post-Pandemic AI Strategy for Resilience, RecoveryBuild a Post-Pandemic AI Strategy for Resilience, Recovery
As companies recover from the pandemic, scaling AI capabilities across the organization can bring the agility and speed demanded to stay competitive.
April 2, 2021
Expectations are high for artificial intelligence’s ability to prime businesses for post-pandemic resiliency, and rightfully so. Research firm IDC predicts that global spending on AI will double over the next four years, growing to more than $110 billion in 2024. Research from Accenture also shows that companies that successfully scale AI achieve nearly 3x the return on investment and a 30% premium on key financial valuation metrics.
While the hype around this technology is not new, the COVID-19 pandemic sharpened the contrast between those who have professionalized their AI capabilities to scale across the enterprise, and those who have yet to tap into the full value of their AI investments. In an attempt to recover and achieve sustainable growth beyond 2021, it will be crucial for companies to embrace evolving AI capabilities by transforming into an intelligent enterprise that embeds analytics into the core of its operations.
Stages of AI maturity
As we enter a new era of technology, work and life, there will be increasing pressures for IT leaders to quickly scale AI and its techniques -- including machine learning, natural language processing, knowledge representation, computational intelligence, and more -- to enable an automated, intelligent and insight-driven organization. Our research shows that most C-suite executives (84%) believe they must leverage AI to achieve their growth objectives, but most do not know where to start, with 76% of execs reporting that they struggle with how to scale.
If you’re still in the early stages of AI maturity, you’re not alone. In our experience, most companies (80-85%) are still in the initial proof of concept phases, resulting in a low scaling success rate, and ultimately a lower ROI. Often IT-led, these small-scale efforts tend to be siloed within a department or team and lack a connection to a business outcome or strategic imperative.
In parallel, we’ve seen that very few organizations (<5%) have progressed to the most advanced point of AI sophistication. These companies have a digital platform mindset and create a culture of AI with data and analytics democratized across the organization. Businesses that are industrialized for growth are consistently scaling models with a responsible AI framework to promote product and service innovation. Our research shows that strategic, wide-scale AI deployment will enable competitive differentiation, correlated with significantly higher financial results.
Putting principles to practice
To scale effectively -- no matter where your company currently stands in its AI journey -- IT leaders and their teams must professionalize their AI approach, categorizing AI as a trade with a shared set of principles and guidance. Here are four strategies to keep top of mind as you advance your organization’s digital capabilities:
Establish sustainable multidisciplinary teams of diverse perspectives, skills and approaches that work together to innovate and deliver AI products or services that can be cross-functional. When doing this it is also important to clarify core roles and primary skills for team members and business product owners to ensure teams have clarity in what they are expected to bring to the table.
Define ways of working that enable multidisciplinary teams to work together effectively, deliver the best products and services, and innovate predictably and efficiently. For example, Accenture’s Legal organization teamed with our Global IT Applied Intelligence group to develop a solution to the challenge of managing and sorting through the thousands of legal documents that are transacted each month. The Applied Intelligence team worked side-by-side with the Legal team to apply predictive models, artificial intelligence, and machine learning to create a robust and self-learning search tools that helps our Legal team easily perform precise information searching and extraction, unleashing data that was previously not easily accessible. User interface and experience skills were just as important to ensure our end-users were able to easily exploit the power of the AI models.
Demand education and training to create confidence in AI technology, with clear qualifications and standards for practitioners. Implementing regular assessment points throughout employees’ careers can be a useful benchmark to test their knowledge and maintain their technical education. Equipping all employees with the understanding and examples of where AI technologies are most effective helps to direct scarce resources towards areas with the highest chance of initial success. If needed, partnerships with research and academic institutions can be a useful strategy to reskill employees and strengthen future talent pipelines.
Democratize AI literacy to empower your entire workforce to have access to specialist teams or technology they can leverage in this quickly advancing space. Making AI intelligence accessible to everyone at your organization will help your company as a whole achieve stronger and faster returns on investment. It will encourage new ideas and better collaboration across the business.
A big challenge for any technology is scaling across the enterprise, and AI is no exception. To push an idea through to a real solution with tangible benefits often requires rethinking the role of the technology completely. By formalizing your AI strategy, IT leaders will be poised to help their organization achieve more value from AI, create a more agile and connected workplace, and gain a competitive advantage in the race to scale.
Mark Dineen is a part of the Applied Intelligence team for Accenture’s global IT organization, leading the company’s internal AI studio and delivery capabilities. These global teams have responsibility for creating and delivering new data-driven insights to all of Accenture using design thinking, advanced analytics and machine learning.
About the Author(s)
You May Also Like