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Rethinking AI for Business Resilience and Results

Simon Thomas, global head of data & AI, Avanade
Author: Simon Thomas, data & AI global market unit lead, Avanade

As I look around the world at all that is happening, I alternate between fear, frustration and overwhelming positivity at how organizations are using artificial intelligence (AI) to help change the world as we know it. And while it’s fair to say that we can never be this unprepared again, I am humbled by the use of AI to help us rapidly communicate the spread of disease, factor in new product development to aid others, develop pathologists' diagnosis, build “at a distance” capabilities like hailing public transportation, temperature scanning or any other number of inventions in this time of need.

I am also seeing organizations rapidly move to reconfigure their product portfolio and create a scalable operating model responsive to changing market and employee needs, with AI as a critical enabler. Organizations are increasingly embracing AI to reinvent their business models and address new growth opportunities.

The realm of possibilities for AI is endless; organizations are eager to find new ways to implement it so they can shorten lead times, increase productivity and efficiency and boost profits.

At Avanade, we are fortunate to work with clients around the world on all aspects of their data and AI journey, so we were naturally curious about how organizations are picking up AI and the success factors it takes for AI adoption at scale. We wanted to see where organizations are at in their journey to AI readiness, so we conducted a survey of over 1,700 business decision makers, drilling down into the maturity of their AI strategy, talent and culture, digital ethics, technology and process, as well as data supply chain and analytics.

At a time when 83% of organizations believe AI will be the competitive edge of the future, a majority are still in the infancy of adoption. Let’s take a look at how that translated across the five areas of AI adoption:

AI strategy

We define AI strategy as the ability to set and communicate a vision, define a roadmap and business case for how AI will be used over time at an organizational level. At Avanade, we know that having a rock-solid strategy is one of, if not the most important piece in rolling out new technologies. Our respondents agreed, with over 95% stating that AI strategy was the most critical area to scale AI. But with that said, nearly 1/3 of organizations surveyed cited building an AI strategy as one of the top three barriers in achieving their business objectives.

Talent and culture

We defined talent and culture as the level of sophistication in how an organization evaluates work that AI can support, develops AI-specific skills and manages its talent pool, whether by hiring or reskilling, in addition to the change management process needed to support moving to an AI culture.

The research showed 80% of organizations surveyed view culture changes as the "make or break" for AI’s long-term success, but this is an area where organizations need help. Over half expressed that they are struggling with recruiting AI talent and/or shifting their culture.

Organizational culture change can be a sticky area. Because people are your greatest asset, it’s important to make it clear that you’re designing AI for people to help them remove boring, repetitive tasks and ultimately improve their working environment.

Digital ethics

Digital ethics is all about how an organization applies values within the design, development, implementation, and operation of digital technologies to assure they’re respectful of individuals, are socially responsible, and environmentally sustainable and well-governed.

The ethics of AI is an ever-evolving topic that organizations need to focus on to maintain and grow customer loyalty and trust. Luckily, when surveyed, we found that 2/3 of respondents in the process of implementing AI also are in the process of creating a digital ethics framework.

Data supply chain and analytics

When looking at data supply chain and analytics, we’re looking at the ability for organizations to engage with AI-specific technologies and techniques, including levels of automation, and apply them in a business context.

Data supply chain and analytics is another area where our respondents felt they had a lot of room for improvement – with 65% reporting that their organization’s data quality could use improvement when it comes to supporting AI.

AI requires access to information. The more accurate, timely, accessible and structured your data is, the more AI can learn from, derive insights, improve its performance and deliver better outcomes.

Technology and process

Technology and process has been defined as the ability to engage with AI-specific technologies and techniques, including levels of automation, and apply them in a business context.

There are many different innovative technologies and techniques to apply when dealing with AI, from working with automation and advanced analytics to using cognitive agents. Our survey found that most organizations are just scratching the surface. While six in ten have experimented with automation, far fewer are using computer vision (35%) and only 34% are using cognitive agents.

With so many organizations just beginning their AI journey, we’re encouraged and excited to see what new opportunities and solutions our customers come up with next.

Author Simon Thomas is data & AI global market unit lead at Avanade. Read more from Avanade here.