Define Your Artificial Intelligence Roadmap

Leverage artificial intelligence to optimize your resources and differentiate in your market

Maximize benefits and reduce the risks of artificial intelligence

The hype around AI has led to exaggerated expectations and vague details. With sufficient understanding of the capabilities of AI — and the best ways to determine how it can serve the organization — leaders can adopt and exploit the promise of AI with realistic expectations.

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    I used Gartner to understand trends like chatbots and other AI techniques. The Gartner expert helped us in reviewing our service management platform, put a flexible architecture in place and with our vendor selection with pros and cons. We saved about 60 hours of time we would have spent researching on our own.

    CIO, transportation industry

    Craft your artificial intelligence strategy

    You don’t need to be an expert to make sense of AI for your business — you can draw on experience you already have in two ways. First, assess how the use of artificial intelligence would benefit business outcomes, then evaluate how the power of AI and deep learning can be used to achieve those outcomes.

    Watch the Special Report Video from Whit Andrews, VP and Distinguished Analyst, Gartner Research

    Pragmatic artificial intelligence insights you can use

    Marketing hyperbole is increasing confusion around AI, resulting in enterprises struggling to put a realistic value on an important source of innovation and differentiation. Gartner insights, advice and tools help you make the business case for the application of AI techniques, then create and implement a plan to pragmatically operationalize AI systems to achieve and measure success.

    Five steps to pragmatically implement AI

    Moving from “I want to use AI” to a tactical approach aimed at practically and sustainably solving business problems while managing expectations does not require superpowers. CIOs can follow these five steps to operationalize AI and pursue an AI strategy in a pragmatic fashion.

    Identify the right AI use cases

    Choosing the right AI use cases to deliver business value in a particular domain is an essential first step in seizing new AI opportunities. This tool helps executive leaders lay the foundation for prioritizing AI investments based on use-case categories and subcategories.

    Use AI to fight through COVID-19 and to recover

    Artificial intelligence offers powerful technologies that must be used in the battle against COVID-19, the unfolding economic crisis and also how we reshape our workplace as the pandemic subsides. To be effective, we must turn the promise of AI into pragmatic and concrete applications.

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    Artificial Intelligence questions Gartner can help answer

    As defined in the Gartner Glossary, artificial intelligence (AI) applies advanced analysis and logic-based techniques, including machine learning, to interpret events, support and automate decisions, and take actions.

    AI is a computer engineering discipline — a series of mathematically or logic-based techniques for uncovering, capturing, coding knowledge, and leveraging sophisticated and clever mechanisms to solve problems, i.e., a simulation of cognitive processes by means of computer programs.

    In a business setting, this can range from basic automated payroll templates to detecting fraudulent activities, flagging cross-selling opportunities and optimizing a set of resources to smart robotics carrying out workplace tasks.

    Is your business ready for AI? Let us assess the viability for your business today.

    Businesses are diving into three main categories of AI. These categories include a number of popular applications of technology that can be applied across multiple industries/technological uses.

    The three main categories of techniques that include the most common uses in AI are:

    Probabilistic reasoning: These techniques, often generalized as machine learning, extract value from the large amounts of data gathered by enterprises. This includes techniques aimed at unveiling unknown knowledge held within a large amount of data (or dimensions). This is done by discovering interesting correlations linked to a particular goal or label within that data. This might include, for example, sifting through a large number of customer records and identifying the factors and how these factors correlate.

    Computational logic: These techniques, often referred to as rule-based systems, use and extend the implicit and explicit know-how of the organization. They are aimed at capturing known knowledge in a structured manner, often in the form of rules. These rules can be manipulated by businesses, while the technology guarantees the coherence of the rule set (by making sure that rules don't contradict each other or lead to circular reasoning, which is not that obvious when dealing with tens of thousands of rules). A new series of compliance laws has brought rule-based approaches to the forefront.

    Optimization techniques: Traditionally used by operations research groups, optimization techniques maximize benefits while managing business trade-offs by finding optimal combinations of resources given a number of constraints in a given amount of time. Optimization solvers often generate executable plans of action and are sometimes described as prescriptive analytics techniques. Operational research groups in asset-centric industries (such as manufacturing and utilities) or functions (such as logistics and supply chain) have been using optimization techniques for decades.

    Natural language processing (NLP): NLP provides intuitive forms of communications between humans and systems. NLP includes computational linguistic techniques (symbolic and subsymbolic) aimed at recognizing, parsing, interpreting, automatically tagging, translating and generating (or summarizing) natural languages. The phonetic part is often left to speech- processing technologies that are essentially signal-processing systems. That is why applications dealing with speech-to-text or text-to-speech functionalities are often delivered by different software solutions. Additional knowledge capabilities, such as dictionaries or ontologies, are also part of NLP systems.

    Knowledge representation: Capabilities such as knowledge graphs or semantic networks facilitate and accelerate the access to and analysis of data networks and graphs. Through their representations of knowledge, these mechanisms tend to be more intuitive for specific types of problems. For instance, new knowledge representations provide fertile grounds for AI techniques in situations where one needs to map out specific relationships among entities (investigative research, process optimization or manufacturing assets management, for example). Those techniques include graph traversal, memorization and hybrid learning (while using composite AI systems). For example, in the first half of 2020, adoption of knowledge graph techniques critically accelerated.

    Knowing how to harness the machine learning, rules, optimization, NLP and graph techniques delivered by AI is critical to AI’s success at your organization.

    The key challenges with AI in today’s workplace is that there is so much hype—and consequently misinformation—surrounding the application of artificial intelligence.

    The frenzy being generated by the technology industry, the media and overenthusiastic software vendors has created confusion that makes it difficult for organizations to set the right expectations for business outcomes.

    This gives rise to projects that have no chance of success. Subsequently, business leaders with unrealistic expectations will blame the technology and the science for its inability to transform lead into gold, when realistically, it’s the incorrectly harnessed or misunderstood application of the AI to business outcomes that is the culprit, along with integration and security issues.

    Before implementing any AI system or program at your organization, you must have a clear understanding of the final business impact. What do you want the AI to achieve?

    Start by asking your team of strategists the following questions:

    • What is the business problem that you are trying to solve with AI?
    • Who is the primary consumer of the technology?
    • What is the business process that will host that technique?
    • How will the impact of implementing the technology be measured (compared to more traditional techniques)?
    • How will the value provided by the technology be monitored and maintained? And by whom?
    • Which of the subject-matter experts from the lines of business can guide the development of the solution?

    Any AI strategy must first assess and focus on the organization’s readiness. It must allow for learning and practical use, before committing to an AI program.

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