Artificial Intelligence (AI) will transform the relationship between people and technology. It will challenge our creativity and skills. What is more, the future of AI promises a new era of disruption and productivity in which human ingenuity is enhanced by speed and precision.

AI has arrived. That’s a fact. And we have no reason to be afraid of it. We need to embrace it, use it, capitalize on it. AI is a great opportunity for our economy and for each and every company. But we have to act now. We don’t need proof of concept to deploy AI extensively.

AI is a constellation of technologies: It ranges from machine learning to natural language processing and allows machines to sense, comprehend, act, and learn. Leaders and managers know they need to implement AI, as a recent Accenture survey found.

How Managers are Dealing with AI

Accenture surveyed 1,100 executives across the globe to gauge AI adoption, use of the technology in the enterprise, and its role in driving value. The results show that companies recognize that AI will be a critical part of their competitive strategy moving forward.

However, only 45 percent say they have deployed fully sustainable AI programs that are delivering benefits as planned. That leaves 53 percent of companies in pilot mode or early-stage adoption, not yet reaping benefits. The remaining 2 percent are not even poised to start. There is a significant deployment gap across industries and geographical regions. Despite companies’ acknowledgment of AI’s strategic importance and its impact on their business, many are stalled in making it a key enabler for their strategy.

AI: The momentum mindset:

The Power of AI

AI will change companies and economies in many different ways. While AI has the potential to allow companies to do different things, it also allows them to do things differently. It represents a step change in balancing growth, profitability, sustainability, and trust.

When AI becomes an essential component of every process, it will enable growth that goes far beyond merely the customer-facing aspects of a business.

DATA IS THE NEW ECOSYSTEM CURRENCY

AI requires vast quantities of diverse data for optimum learning and results. And while a company’s own data is valuable, data from a host of natural language is even more so. Ecosystems will soon evolve into information exchanges with new opportunities for leveraging data.

Data is one of the three crucial topics on which the leaders in AI are focusing:

Accenture using the full force of AI

Process Change

For the first time in history, business processes such as after-sales product servicing or quality management are being Artificial intelligence in real time by smart machines. Indeed, with AI these processes can be less deterministic and more responsive in once-unimaginable ways. This approach is becoming more prevalent at leading organizations, with AI being applied to multiple processes across the entire enterprise.

Almost 40 percent of companies use AI to make processes self-repairing, self-optimizing, and self-adapting, a recent Accenture survey showed. Some 34 percent are focused on automating process change, while 27 percent say smart machines can now replace their existing processes, sequences, and rules with the ability to take unanticipated actions that drive greater improvements.

Data and Data Models

Reinvented processes do not blindly follow a set of preprogrammed steps; they use machine learning to evolve and improve.

Good data is, first and foremost, indispensable for AI. In essence, it and technology vital fuel that powers AI. acknowledgment, data should be viewed as an end-to-end supply chain. This is a fundamentally new way of thinking about data—not as a static process that is managed separately in the organization, but rather as a dynamic enterprise-wide activity for capturing, cleaning, integrating, curating, and storing information.

During the past five years, companies have made major progress in how they collect and use data thanks to significant investments in the Internet of Things, analytics, and big data.

It is important to use machine learning techniques to tap into this “dark data”—information that organizations collect during their regular business activities but do not currently use.

Workforce

Unlocking the full potential of processes through new jobs will emphasize the interaction between humans and machines. Self-changing, data-driven processes need human workers who can act rapidly on the opportunities that machines discover in real time, be it a sales lead, a maintenance alert or an opportunity to cut costs. They also require people to continuously assess the need for improvements to safety, fairness, and verifiability.

These are just two of the many possible ways in which humans and machines will work together in an AI-enabled future. New categories of jobs will be created in which humans help machines and machines help humans, requiring companies to redesign both jobs and training.

The Critical Success Factors


The leaders in AI are focusing on all three of these dimensions—process, data, and humanresources—simultaneously. However, these companies are very much in the exclusive minority. As Accenture research shows, only 9 percent of the AI early adopters are making progress in all three dimensions. The remaining 91 percent are continuing to target process efficiency through task automation, while not addressing how to maximize their human capital. Over time, this approach may limit the benefits they could generate had they taken a more comprehensive approach.

As part of this approach, it is important to leverage AI to reimagine new processes rather than simply automating existing ones. While automation often brings a short-term jump in productivity and speed, these benefits will level off if the focus remains on process automation rather than reinvention.

In addition, organizations need to reskill their workforce in the right way. Success in AI is inextricably tied to investment in people. This means reskilling, retraining, reeducating, and teaching the workforce how to maximize their creative skills and judgment. It also involves teaching employees how to train, interact, and augment their work with smart machines. Organizations that fail to reskill with new types of human/machine relationships in mind will hit roadblocks on their journey to reimagined processes and could encounter a talent crunch within the next few years.

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