Consulting has lots of jargon, acronyms, abbreviations and technical terms – this is particularly true when looking at specialised consulting in the fields of artificial intelligence and machine learning. In this AI consulting glossary, we attempt to list common terms and phrases and explain what they mean.<\/p>\n\n\n\n
Adding value to the cost of a product in terms of either its cost or its worth is a value-added feature. For example, a one-year warranty on an electronic device can indicate the quality of the product.<\/p>\n\n\n\n
A concept in computer science which focuses on developing intelligent machines that can match the reasoning and decision-making of humans.<\/strong><\/p>\n\n\n\n Comparing a company’s policies, strategies, or systems to industry best practices to identify opportunities for improvement.<\/p>\n\n\n\n A technique, method, or procedure that consistently yields the most effective or superior results. Used as a benchmark by companies to enhance their systems and processes.<\/p>\n\n\n\n Description of how a business Creates and delivers value to its target market. This involves designing and implementing strategies, organizational structures, processes and policies that are tailored to the needs of the target market. <\/p>\n\n\n\n Stands for Compound Annual Growth Rate <\/p>\n\n\n\n A key strength that adds value to a company. The ability to utilise knowledge, technical capabilities, commitment, skill sets and production techniques to create unique products and services, providing a competitive edge.<\/p>\n\n\n\n Promised client task, part of a larger project, either tangible like progress reports or market study summary, or intangible like presentation.<\/p>\n\n\n\n An in-depth investigation and assessment of an organization’s operations and activities prior to making any business deal or agreement with another entity.<\/p>\n\n\n\n Earnings Before Interest, Taxes, Depreciation, and Amortization. Indication of a company\u2019s operational profitability.<\/p>\n\n\n\n This view displays a horizontal bar chart of the project’s work breakdown structure, named after Henry Gantt, showing the start and finish dates of each category. Gantt, a consultant in the early 1900s, originally introduced this chart..<\/p>\n\n\n\n A set of measurable objectives or goals that indicate successful performance when accomplished.<\/p>\n\n\n\n An acronym for McKinsey, Bain, and BCG, are the three most prestigious international consulting firms, commonly known as the “Big 3”.<\/p>\n\n\n\n Is a principle used by McKinsey to create subcategories that ensure all relevant facts and details are considered. These subcategories must not overlap (mutually exclusive) and must cover all possibilities (collectively exhaustive) – often used in market research.<\/p>\n\n\n\n Machine Learning, a subset of Artificial Intelligence, is the process of computers learning from patterns in data without being programmed to do so.<\/p>\n\n\n\n A documented description of how an organisation is structured to deliver its operational goals<\/p>\n\n\n\n The cost of foregoing an alternative product or service in order to pursue another. For example, a retailer may choose to sell bags instead of other items such as school supplies, clothes, etc. This decision comes with the cost of foregoing the potential sales of these other products.<\/p>\n\n\n\n A significant change in the way of thinking that can have a significant effect on a business. An example of this would be transitioning from manual to digital documentation within an organization.<\/p>\n\n\n\n This suggests that 80% of the outcomes are produced by 20% of the inputs. For businesses, this means that managers should identify the most important elements and give them greater focus. <\/p>\n\n\n\n Machine learning algorithms can predict data values by creating graphs that best fit the given data points in a regression problem. This kind of problem usually involves forecasting time series, understanding cause-and-effect relationships, and making predictions from data sets.<\/p>\n\n\n\n A scoring system that “rewards” the AI, when it produces a positive result enables an AI-driven system to learn through trial and error and feedback from its actions.<\/p>\n\n\n\n Is a measure of net income against investment, used to assess the efficiency of an investment or compare different investments.<\/p>\n\n\n\n A technique for uncovering the underlying causes of faults or issues. It is commonly employed in IT, manufacturing, telecommunications, process control, accident investigation, medicine, and the healthcare sector. Its goal is to identify the sources of the problem and prevent similar issues from occurring in the future.<\/p>\n\n\n\n A three-step feedback process designed to provide positive reinforcement for projects or ideas. It begins by recognizing the good points, followed by corrective feedback, and finishes with praise. This approach helps to lessen the impact of criticism.<\/p>\n\n\n\n The work expected to be covered by a consulting project, including its objectives and boundaries, .<\/p>\n\n\n\n An issue that can arise when project requirements are subject to uncontrolled changes, leading to an extended duration of the project. This can happen when the scope and definition of the project are not clearly defined.<\/p>\n\n\n\n The time taken before a project delivers a cumulative contribution to an organisation. Can be considered as the time taken for a project to break even.<\/p>\n\n\n\n A set of tasks that comprise a key element of a project.<\/p>\n\n\n\n <\/p>\n\n\n\n Related Articles<\/p>\n\n\n\n Consulting has lots of jargon, acronyms, abbreviations and technical terms – this is particularly true when looking at specialised consulting in the fields of artificial intelligence and machine learning. In this AI consulting glossary, we attempt to list common terms and phrases and explain what they mean. Added value Adding value to the cost of […]<\/p>\n","protected":false},"author":3,"featured_media":243729,"template":"","meta":{"_et_pb_use_builder":"off","_et_pb_old_content":"","_et_gb_content_width":""},"blog_category":[19],"_links":{"self":[{"href":"https:\/\/solvedby.ai\/wp-json\/wp\/v2\/blog\/243727"}],"collection":[{"href":"https:\/\/solvedby.ai\/wp-json\/wp\/v2\/blog"}],"about":[{"href":"https:\/\/solvedby.ai\/wp-json\/wp\/v2\/types\/blog"}],"author":[{"embeddable":true,"href":"https:\/\/solvedby.ai\/wp-json\/wp\/v2\/users\/3"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/solvedby.ai\/wp-json\/wp\/v2\/media\/243729"}],"wp:attachment":[{"href":"https:\/\/solvedby.ai\/wp-json\/wp\/v2\/media?parent=243727"}],"wp:term":[{"taxonomy":"blog_category","embeddable":true,"href":"https:\/\/solvedby.ai\/wp-json\/wp\/v2\/blog_category?post=243727"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Benchmarking<\/h2>\n\n\n\n
Best practice<\/h2>\n\n\n\n
Business model<\/h2>\n\n\n\n
CAGR<\/h2>\n\n\n\n
Core competency<\/h2>\n\n\n\n
Deliverable<\/h2>\n\n\n\n
Due diligence<\/h2>\n\n\n\n
EBITDA<\/h2>\n\n\n\n
Gantt Chart<\/h2>\n\n\n\n
Key Performance Indicators (KPI)<\/h2>\n\n\n\n
MBB<\/h2>\n\n\n\n
MECE (Mutually Exclusive, Collectively Exhaustive)<\/h2>\n\n\n\n
Machine learning<\/h2>\n\n\n\n
Operating Model<\/h2>\n\n\n\n
Opportunity Cost<\/h2>\n\n\n\n
Paradigm Shift<\/h2>\n\n\n\n
Pareto Principle<\/h2>\n\n\n\n
Regression problems<\/h2>\n\n\n\n
Reinforcement learning<\/h2>\n\n\n\n
ROI (Return on Investment)<\/h2>\n\n\n\n
Roo<\/strong>t Cause Analysis<\/strong><\/h2>\n\n\n\n
Sandwich Method<\/h2>\n\n\n\n
Scope<\/h2>\n\n\n\n
Scope creep<\/h2>\n\n\n\n
Time To Value<\/h2>\n\n\n\n
Workstream<\/h2>\n\n\n\n
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