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Artificial intelligence (AI) applies advanced analysis and logic-based techniques, including machine learning, to interpret events, support and automate decisions and take action.
Here at Gartner, we define artificial intelligence (AI) as applying advanced analysis and logic-based techniques, including machine learning (ML), to interpret events, support and automate decisions and to take actions. This definition is consistent with the current and emerging state of AI technologies and capabilities, and it acknowledges that AI now generally involves probabilistic analysis (combining probability and logic to assign a value to uncertainty).
Other organizations and individuals may use different definitions. There is no single, universally accepted descriptor for artificial intelligence as there is such a wide range of ways in which AI can support, augment and automate human activities, and learn and act independently (see “What is machine learning?”).
To capture the opportunity of AI as an organization, however, you will need to articulate and agree on a generally accepted definition focused on what you want AI to accomplish. (See “What is enterprise AI strategy?”).
Leave room for differences of opinion, but make sure that business, IT and data and analytics leaders don’t fundamentally disagree about what AI means to the organization or you will be unable to design a strategy that captures the benefits.
Note that AI technology vendors also are likely to have their own definitions of the term. Ask them to explain how their offerings meet your expectations for how AI will deliver value.
Machine learning is a critical technique that enables AI to solve problems. Despite common misperceptions (and misnomers in popular culture), machines do not learn. They store and compute — admittedly in increasingly complex ways.
Machine learning is a purely analytical discipline. It applies mathematical models to data to extract knowledge and find patterns that humans would likely miss. ML also recommends actions, but it does not direct systems to take action without human intervention.
More specifically, machine learning creates an algorithm or statistical formula (referred to as a “model”) that converts a series of data points into a single result. ML algorithms “learn” through “training,” in which they identify patterns and correlations in data and use them to provide new insights and predictions without being explicitly programmed to do so.
Deep learning, a variant of machine learning algorithms, uses multiple layers of algorithms to solve problems by extracting knowledge from raw data and transforming it at every level. Deep learning can outperform traditional ML (or shallow learning techniques) by working with complex and often high-dimensional data, such as images, speech and text. Still, either rule-based systems or traditional ML can effectively solve many AI problems.
In most organizations, deep learning solutions are not yet a significant part of the product roadmap (rule-based systems or traditional ML can effectively enable most AI use cases today), but their use is quickly increasing alongside advancements in data processing and breakthroughs in computational techniques.
Using ML, including deep learning, to make predictions enables an AI-driven process to automate the selection of the most favorable result, which eliminates the need for a human decision maker.
IT and data analysis leaders can use AI techniques to solve a wide array of business problems and can generate significant returns on investment; however, the question for most organizations is how to use artificial intelligence to create or accelerate the growth of digital business.
The main opportunities of artificial intelligence lie in its ability to:
Gartner research consistently shows that CIOs see an enormous opportunity in the benefits of AI but still struggle to capture those advantages in practice. Nevertheless, AI will ultimately reshape how work is done as the technology replaces some tasks typically performed by employees and changes how day-to-day decisions are made. Use cases mainly fall into three categories: automating and optimizing, generating insight and creating human-like engagement (e.g., chatbots and virtual assistants). (See “What are examples of artificial intelligence applications in business?”).
For now, however, AI hype can be rife, making it difficult for some organizations to set the right expectations regarding business outcomes. Untamed hype gives rise to projects that have no chance of success. When that happens, business leaders with unrealistic expectations blame the technology and science for its inability to create the transformations for which they hoped.
Make sure to establish an enterprise strategy for AI to identify use cases and metrics of success from the outset. Common ways of measuring benefits include risk reduction, speed of process, improved sales, increased customer satisfaction, and reduced labor needs or costs. Many business cases rely on a combination of tangible and intangible benefits. (See “What is enterprise AI strategy?”).
As an emerging technology, AI’s full impact and benefits have yet to be realized. AI innovation is one of a multitude of forces disrupting existing markets and enabling new digital business initiatives, for example. But AI is also being applied across industries, organizations and functions in a range of ways. A few examples from business operations are:
For a business to capture the benefits of AI, executive leaders should establish an enterprisewide AI strategy that identifies use cases, quantifies benefits and risks, aligns business and technology teams and changes organizational competencies to support AI adoption.
To ensure you derive value from AI, choose initiatives strategically, focusing on what your organization is trying to accomplish and the business problems you’re working to solve. For AI to really take off, you’ll need to employ AI as part of your existing application family — and that includes having data from every area of the business to power the features it offers.
Organizations at the earlier stages of AI maturity are more likely to pursue use cases around cost control before advancing to key elements of the value proposition, such as customer experience. Gartner research shows that as maturity grows, AI is applied more broadly and more impact is realized.
Key elements of enterprise AI strategy are:
The AI discipline is evolving rapidly through new techniques, dedicated infrastructures and hardware. Over the next five years, Gartner expects organizations to adopt cutting-edge techniques for smarter and more reliable, responsible and environmentally sustainable artificial intelligence applications.
The trajectory of AI now more closely follows that of technologies that have preceded it. For companies and governments, AI is becoming more:
Going forward, organizations will continue to pursue AI to enhance their decision-making processes. Savvy ones that adopt these methods quickly will drive more competitive differentiation and become more agile and more responsive to ecosystem changes.
Executing AI strategies remains a challenge for infrastructure and operations teams. Starting on-premises means investing in infrastructure and architecture that can be difficult to predict, staff and fund. That makes cloud options appealing, but as the need for AI grows and the required investment increases, the cloud may become harder to afford (and commitment to cloud providers more concerning). That’s why the emergence of strategies that balance investment in cloud function with investments in infrastructure are so attractive (so-called cloud/on-premises hybrid strategies).
Among Gartner strategic planning assumptions for AI are that by 2025:
Most business organizations do not know or understand the inner workings of artificial intelligence, which creates potential for concerns about fairness, security and privacy. But AI cannot thrive if the business does not trust AI techniques, so organizations need checks and balances to assess and respond to threats and damage and to ensure integrity is embedded into AI.
Gartner refers to its AI risk management framework as “MOST” because it has the following three pillars:
As AI goes mainstream in an enterprise, threats will inevitably follow and result in serious organizational risks. Organizations must evaluate the threats proactively. In doing so, they can increase stakeholder trust in AI.
Indeed, Gartner expects that by 2025, regulations will necessitate a focus on AI ethics, transparency and privacy, which will stimulate — instead of stifle — trust, growth and better functioning of AI around the world.