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Artificial intelligence is a constellation of many different technologies working together to enable machines to sense, comprehend, act, and learn with human-like levels of intelligence. Maybe that’s why it seems as though everyone’s definition of artificial intelligence is different: AI isn’t just one thing.
Technologies like machine learning and natural language processing are all part of the AI landscape. Each one is evolving along its own path and, when applied in combination with data, analytics and automation, can help businesses achieve their goals, be it improving customer service or optimizing the supply chain.
Narrow (or “weak”) AI
Some go even further to define artificial intelligence as “narrow” and “general” AI. Most of what we experience in our day-to-day lives is narrow AI, which performs a single task or a set of closely related tasks. Examples include:
These systems are powerful, but the playing field is narrow: They tend to be focused on driving efficiencies. But, with the right application, narrow AI has immense transformational power—and it continues to influence how we work and live on a global scale.
General (or “strong”) AI
General AI is more like what you see in sci-fi films, where sentient machines emulate human intelligence, thinking strategically, abstractly and creatively, with the ability to handle a range of complex tasks. While machines can perform some tasks better than humans (e.g. data processing), this fully realized vision of general AI does not yet exist outside the silver screen. That’s why human-machine collaboration is crucial—in today’s world, artificial intelligence remains an extension of human capabilities, not a replacement.
Machine Learning is a type of artificial intelligence that enables systems to learn patterns from data and subsequently improve from experience. See more.View Transcript
Artificial intelligence has long been a subject of anticipation among both popular and scientific culture, with the potential to transform businesses as well as the relationship between people and technology at large. So, why is AI usage reaching critical mass today?
Because of the proliferation of data and the maturity of other innovations in cloud processing and computing power, AI adoption is growing faster than ever. Companies now have access to an unprecedented amount of data, including dark data they didn’t even realize they had until now. These treasure troves are a boon to the growth of AI.
A critical source of business value—when done right
AI has long been regarded as a potential source of business innovation. With the enablers now in place, organizations are starting to see how AI can multiply value for them. Automation cuts costs and brings new levels of consistency, speed and scalability to business processes; in fact, some Accenture clients are seeing time savings of 70 percent. Even more compelling, however, is the ability of AI to drive growth. Companies that scale successfully see 3X the return on their AI investments compared to those who are stuck in the pilot stage. No wonder 84 percent of C-suite executives believe they must leverage AI to achieve their growth objectives.
Agility and competitive advantage
Artificial intelligence is not just about efficiency and streamlining laborious tasks. Thanks to machine learning and deep learning, AI applications can learn from data and results in near real time, analyzing new information from many sources and adapting accordingly, with a level of accuracy that’s invaluable to business. (product recommendations are a prime example.) This ability to self learn and self optimize means AI continually compounds the business benefits it generates.
In this way, AI helps businesses adapt at speed, with a regular stream of insights to drive innovation and competitive advantage in a world of constant disruption. When scaled, AI can become a key enabler of your strategic priorities—and even a lynchpin to survival: Three out of four C-suite executives believe that if they don’t scale artificial intelligence in the next five years, they risk going out of business entirely. Clearly, the stakes are high to scale AI.
3 out of 4 C-suite executives believe that if they don’t scale artificial intelligence in the next five years, they risk going out of business entirely.
There are many ways to define artificial intelligence, but the more important conversation revolves around what AI enables you to do.
End-to-end efficiency: AI eliminates friction and improves analytics and resource utilization across your organization, resulting in significant cost reductions. It can also automate complex processes and minimize downtime by predicting maintenance needs.
Improved accuracy and decision-making: AI augments human intelligence with rich analytics and pattern prediction capabilities to improve the quality, effectiveness, and creativity of employee decisions.
Intelligent offerings: Because machines think differently from humans, they can uncover gaps and opportunities in the market more quickly, helping you introduce new products, services, channels and business models with a level of speed and quality that wasn’t possible before.
Empowered employees: AI can tackle mundane activities while employees spend time on more fulfilling high-value tasks. By fundamentally changing the way work is done and reinforcing the role of people to drive growth, AI is projected to boost labor productivity.
Superior customer service: Continuous machine learning provides a steady flow of 360-degree customer insights for hyper personalization. From 24/7 chatbots to faster help desk routing, businesses can use AI to curate information in real time and provide high-touch experiences that drive growth, retention and overall satisfaction.
AI is used in many ways, but the prevailing truth is that your AI strategy is your business strategy. To maximize your return on AI investments, identify your business priorities and then determine how AI can help.
According to Accenture’s report, AI: Built to Scale, 84 percent of business executives believe they need to use AI to achieve their growth objectives. However, 76 percent acknowledge struggling with how to scale AI across their business. Until now, there hasn’t been a blueprint to getting past proof of concept into production and scale, a transition many struggle to make. At this inflection point, it’s imperative businesses take the necessary steps to scale successfully.
Define your business value
There are countless ways to use AI. How do organizations decide what to focus on? To scale successfully, start by defining what value means to your business. Then assess and prioritize the various applications of AI against those strategic objectives.
Rework your workforce
The growing momentum of AI calls for a diverse, reconfigured workforce to support and scale it. Despite early fears that artificial intelligence and automation would lead to job loss, the future of AI hinges on human-machine collaboration and the imperative to reshape talent and ways of working.
Establish governance and ethical frameworks
Organizations must design their AI strategy with trust in mind. That means building the right governance structures and making sure ethical principles are translated into the development of algorithms and software.
Applying these factors successfully can help organizations unlock exponential value and stay competitive. AI is no longer simply a "nice to have", but is critical to a business’ future.
No artificial intelligence introduction would be complete without addressing AI ethics. AI is moving at a blistering pace and, as with any powerful technology, organizations need to build trust with the public and be accountable to their customers and employees.
At Accenture, we define “responsible AI” as the practice of designing, building and deploying AI in a manner that empowers employees and businesses and fairly impacts customers and society—allowing companies to engender trust and scale AI with confidence.
Every company using AI is subject to scrutiny. Ethics theater, where companies amplify their responsible use of AI through PR while partaking in unpublicized gray-area activities, is a regular issue. Unconscious bias is yet another. Responsible AI is an emerging capability aiming to build trust between organizations and both their employees and customers.
Data privacy and the unauthorized use of AI can be detrimental both reputationally and systemically. Companies must design confidentiality, transparency and security into their AI programs at the outset and make sure data is collected, used, managed and stored safely and responsibly.
Transparency and explainability
Whether building an ethics committee or revising their code of ethics, companies need to establish a governance framework to guide their investments and avoid ethical, legal and regulatory risks. As AI technologies become increasingly responsible for making decisions, businesses need to be able to see how AI systems arrive at a given outcome, taking these decisions out of the “black box.” A clear governance framework and ethics committee can help with the development of practices and protocols that ensure their code of ethics is properly translated into the development of AI solutions.
Machines don’t have minds of their own, but they do make mistakes. Organizations should have risk frameworks and contingency plans in place in the event of a problem. Be clear about who is accountable for the decisions made by AI systems, and define the management approach to help escalate problems when necessary.
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