Companies continue to prioritize AI and cloud for innovation investment, according to the latest Transforming Business poll

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Contactless payment with facial recognition technology.

  • Cloud and AI top the list of innovation investments by companies according to the latest Transforming Business poll.
  • Companies are still figuring out all the applications of these technologies.
  • Racial and gender biases in AI applications are issues no company can ignore.
  • Visit Insider’s Transforming Business homepage for more stories.

Companies continue to prioritize AI and cloud as key investment targets as they strive to innovate and drive growth into the future, according to the most recent Transforming Business poll.

Actionable data insights are a key outcome for these applications. “Many businesses are looking to streamline their operations and make them more efficient, as well as find new insights and connections in their data,” said Victoria Petrock, principal analyst at eMarketer. “They are turning to AI to help them achieve competitive advantage.”

While it might seem like AI and cloud are ubiquitous and widely utilized, companies are still figuring out all the ways it can change their business. Laura Urquizu, CEO of Red Points and one of the 100 People Transforming Business in Europe, wrote in an article for Insider that AI and machine learning had vastly improved customer experience by creating more personalized shopping experiences and increasing brand loyalty.

But the insights available via tools like AI and the cloud can do much more than some companies have figured out. “No matter how important customer experience is, however, it is a mistake to believe it is the only operational area that can (and should) be transformed using technologies like these,” Urquizu wrote. “The efficiency of your internal operations – your support team, supply chain, production, inventory, quality control, human resources, and so on – can all benefit from applying AI and ML technologies.”

The opportunities vary by industry, of course, and the global pandemic has created opportunities to put AI to the test as never before. BenevolentAI, whose CEO Joanna Shields is one of this year’s Transformers, used its technology to analyze vast quantities of scientific research, ultimately surfacing a drug treatment that has been used to treat moderate-to-severe COVID patients.

“One positive outcome of COVID-19 is that it has united science and tech for good, accelerating data-sharing agreements and encouraging the open publication of research results.” Shields told Insider. “This new environment of collaboration has provided a glimpse of the beginnings of a more open and adaptable R&D model that can accelerate the delivery of innovative and life-changing outcomes for patients.”

Innovation has not come without problems, however. AI applications have come under fire, demonstrably shown in some cases to reflect racial and gender bias in hiring tools, and voice and facial recognition.

Tech companies and their customers are under pressure to address these injustices with a appropriate urgency.

“[Businesses] must find a way to provide AI with the right data inputs, and give it instructions to behave in the most ethical way possible, ignoring and unfolding historical biases and to be confident in leaving the business’ past behaviors behind,” Michael Feindt, a 2020 Transformer and strategic advisor at Blue Yonder, a digital fulfillment and supply chain solutions provider, wrote for Insider.

It is possible, Feindt said, to apply these tools to actually combatting discrimination and inequity.

“Simply put, it’s down to us whether AI is a force for good or a force for bad. If you can provide it with data and instructions that are designed to shape the world in a certain way, AI will do that,” Feindt wrote. “So if businesses are willing to put in the time and effort to set things on a fairer course, AI can set about fighting discrimination and injustice.

This SurveyMonkey Audience poll targeted individuals who work in a management capacity at their company according to the Audience panel. They included respondents from Hong Kong (n=50), Singapore (n=50), The United States (n=207), Canada (n=104), France (n=52), the United Kingdom (n=51), Germany (n=50) and India (n=50), with local translations in Germany and France. Respondents are incentivized to complete surveys through charitable contributions. Generally speaking, digital polling tends to skew toward people with access to the internet. SurveyMonkey Audience doesn’t try to weight its sample based on race or income. Polling data collected total of 614 respondents March 3-4, 2021.

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CEO of data analytics firm Quantexa shares how digital resilience and data-driven decision-making will determine which businesses thrive in a post-Covid world

Quantexa CEO Vishal Marria
Quantexa CEO Vishal Marria

It’s no secret that even before the COVID-19 pandemic, there was a significant missed opportunity in enterprise data and analytics. In fact, at least three-quarters of companies today have limited their use of analytics and fail to capitalize on the operational decision-making opportunity of modern data intelligence. Organizations often struggle to operationalize analytics into the day-to-day business. However, businesses have begun to realize that state-of-the-art decision intelligence requires a blend of machine intelligence with human intelligence to ensure optimal decision-making. Applying graph representations to high performance data sets is fast becoming an imperative to modern decision-making success.

Digital resilience is the new watchword in a post-COVID-19 world

The ability to respond and adapt quickly to new situations has never been more stark than during the pandemic. The crisis has taught organizations that a new level of agility and digital resilience is needed across ecosystems, partners and the supply chain. The focus for any would-be intelligent enterprise should evolve the capabilities created to manage the impact of Covid-19 into productive analytics hubs, capable of using leading indicators to predict and react to future risk with greater frequency, while simultaneously discovering hidden future opportunities. Those that fail to implement an effective enterprise data model enabling the foundation for resilient decision-making by 2021 are forecasted to underperform on profitability by 10% according to IDC.

Data at the core – but how can you trust it?

The volume of data being created is quickly surpassing the rate at which computing and storage systems are being developed. According to IDC, the amount of data available will be enough to fully occupy a stack of tablets measuring 6.6 times the distance between the moon and the earth by the end of this year. The point is, both external and internal data are growing at such a rate, 26% year over year, that ensuring data is available in a meaningful, operationalized way is becoming more important as a core discipline. By definition, the volume, velocity and variety of big data is creating huge operational pressure – called the data-decision gap.

According to KPMG, 56% of CEOs don’t trust the integrity of their data. That said, when the analytical models and technology they use to guide decision-making work with untrustworthy data, they naturally doubt its recommendations. It’s become important to understand the context of your data so you can reveal the unseen and, in some cases, unexpected connections that either create risk or opportunity.

A new generation of intelligent decision-making

The lack of a single, trusted view of data across an organization is a serious obstacle to data-driven decision intelligence. Without this, decisions can’t be automated in an accurate or efficient way, and individual entities such as customers and transactions, cannot be properly and fully understood and analyzed. However, reliable data integration, especially at scale, is difficult, which is why data becomes stuck in multiple silos – inhibiting the connected single view and holistic, contextual analysis that is desired. Traditional rules-based approaches to decision support are not sufficiently agile or resilient in today’s uncertain and rapidly changing business and geopolitical environment – advanced analytics, machine learning, and AI are needed to empower users or automate key processes.

The good news is that new approaches and innovations to data and analytics show a path forward for maximizing the value enterprises can get from their data.

Entity resolution and network generation, surfaced through graph analytics, are key to understanding relationships and behaviors of customers and third parties in the supply chain, resulting in better, faster operational decisions. By integrating the right data, decision makers can become empowered as their new insights come from finding explainable links between fully understood, trusted data in a single view provided by entity resolution.

Machine learning to deliver big, but not without human input

Less than 15% of analytics adopters have made progress with automated decisions. This is a big problem, especially when dealing with large complex data sets. Deployment of fully automated operational decision-making moves analytics from reactive reporting to active, intelligent, and real-time decision-making. As more tasks are automated, the enterprise can focus more on differentiating work.

The key to this is augmentation – combining the best of human and machine intelligence. This allows repeatable routines of work to be fully automated and exceptional cases requiring fine judgement to be dealt with by humans. A great benefit of augmented analytics is that it accelerates the formulation of new data and analytics capabilities which, in effect, can be adapted to the skills, needs and problems of different classes of business user, which extends the reach of analytics across an organization. By maximizing the value of human and machine intelligence, there is a clear path to creating an effective data-driven enterprise.

Organization implications – creating the ability to adapt

To shift to a data-driven enterprise, business leaders need to reimagine how they operationalize the data they consume and analyze. The key to this is gaining a trusted, contextual, connected single view of the vast amounts of data that now exist for better decision-making.

Analytics now drives today’s enterprise, from formation of business strategy to powering operational excellence. Creating a culture of collaboration and getting the best out of humans alongside machines is crucial. Analytics has clearly moved from being an optional extra to serving as the core of decision-making, so creating a data-centric contextual decision intelligence framework has never been more important.

The C-suite and all business leaders need to spearhead a change across the enterprise to help drive adoption and utilization of advanced analytics. Before the pandemic, data and analytics were already the new competitive differentiators. But now, creating the right level of digital resilience across an organization that can adapt and change quickly in response to external pressure and threats will set the foundation for the enterprises that ultimately survive and thrive. The key questions we should all be asking ourselves are how well do we trust the data that we use to make decisions?  And how can organizations implement decision intelligence to ensure future sustainability and growth?

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