Why isn’t my enterprise getting value from AI at scale?

Posted: 2019-11-21

Author: Admin

Why do most AI initiatives for business fizzle out? Why do so many teams’ best efforts to develop or deploy new algorithms or predictive models end up dying a slow death on a report or spreadsheet, rather than achieving widespread adoption?
Most business leaders agree that advanced analytics and AI are the way of the future; according to a 2018 study, nearly half of respondents said their organizations have deployed some type of AI capability into their business processes, and over 70% expected that investment to increase in the coming years.
Despite this heavy investment, most companies are still struggling to capture real value. A recent study found that most firms have only seen small gains from AI—typically from single use cases or ad hoc pilots, with only a few able to scale successfully to embed analytics into the entire organization. After surveying thousands of executives, the study concluded that only 8% of firms were engaging in practices that successfully led to widespread AI adoption.

Why? Because AI applications need to work for the business user, not the other way around.

The most challenging step in deriving actual business value from AI is to bridge what’s known as the “last mile”: applying data and insights directly to real-life business outcomes, enabling people to make the right decisions at the right time. Without this critical final step, organizations may see their analytics investments go to waste.
Current consensus on AI adoption agrees that embedding analytics and AI into the entire organization is the key to conquering the last mile. One step towards that is ensuring analytics is very user-friendly and customized to each group of decision makers. But the second, more challenging step is to create a corporate decision-making culture that’s embedded with AI-empowered decision making. And without a catalyst to transform how decisions are made at all levels of the organization, this culture shift can take decades.

The Solow paradox and organizational change

The need for organizational culture change in order to derive full value from business investments into IT goes all the way back to the early days of computers, when the exponential growth of processing power and the application of IT to various office processes had little to no effect on productivity rates for several decades—known as the Solow paradox. Productivity didn’t take off until the 1990s, when companies began to make organizational changes that integrated their IT investments into redesigned processes and workflows.
The author of a recent HBR article echo this as they highlight the importance of AI’s application to business decisions, calling for an organizational culture shift from experience-based, leader-driven decision making to data-driven decision making at the front line.

“When AI is adopted broadly, employees up and down the hierarchy will augment their own judgment and intuition with algorithms’ recommendations to arrive at better answers than either humans or machines could reach on their own. But for this approach to work, people at all levels have to trust the algorithms’ suggestions and feel empowered to make decisions—and that means abandoning the traditional top-down approach. If employees have to consult a higher-up before taking action, that will inhibit the use of AI.“

This fits right in with the steps to conquering the last mile we mentioned earlier: user-friendly analytics customized for decision makers, and a corporate analytics-based decision-making culture. That’s what diwo was designed for: its opportunity-driven framework transforms business decisions at all levels by acting as a personal decision consultant for each user: identifying opportunities that would otherwise be missed, guiding the user to optimized strategies to address them in time, and showing the impacts of each decision.

Decision Intelligence: applied AI for better business decisions

For an organization to succeed, it must have the ability to consistently make timely, high-quality decisions that maximize business outcomes across the organization. We can call this capability “Decision Intelligence.”
Decision Intelligence is an emerging category with multiple facets, merging data science with behavioral and managerial sciences to describe an organization’s strategy for making effective business decisions. For continuous, instantaneous decisions, this means full decision automation, such as online product recommendations or credit-card payment approvals. But other decisions are characterized by complexity and uncertainty requiring more involvement from human decision makers, which slows down the process significantly. In these cases, value is achieved by equipping human capabilities with automated decision intelligence support: not only resulting in reduced decision fatigue, but also improving both the speed and quality of these more complex decisions.
It’s not enough for users to receive better insights and predictions; they must also be equipped to apply them to the decisions they face. It’s no wonder that AI “solutions” that simply pile more information onto decision makers’ plates have low adoption rates. For a platform’s value to be realized both by users and top leaders, it must go a step further: connecting the dots for users, applying them to their unique context to show them their next step.

diwo as a catalyst for AI adoption

With a unique focus on delivering business value by applying AI to real-world business decisions, diwo overcomes these adoption issues: its Decision Intelligence augments human decision makers, rather than replacing them, to reduce error while still valuing human experience. diwo also builds trust as it shows evidence for its recommendations and quantifies the impact of each potential decision. When combined with a business culture that empowers analytics-based decision making at all levels of the organization, the result is something remarkable: breakthroughs in agility and adaptability, higher operational efficiency, improved customer satisfaction, minimized risk, and increased profit and revenue levels.
Sources:

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https://www.mckinsey.com/featured-insights/artificial-intelligence/ai-adoption-advances-but-foundational-barriers-remain
https://mckinsey.com/business-functions/mckinsey-analytics/our-insights/breaking-away-the-secrets-to-scaling-analytics
https://hbr.org/2019/07/building-the-ai-powered-organization