Reinforce Your Artificial Intelligence (AI) Ecosystem

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 these 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.