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Going from Analytics to Artificial Intelligence, what does your Organisation need?
Artificial intelligence in business is the use of AI tools such as machine learning, natural language processing, and computer vision to optimize business functions, boost employee productivity, and drive business value.It’s important because organizations are creating and consuming data at a rapid rate.
Artificial intelligence, or the development of computer systems and machine learning to mimic the problem-solving and decision-making capabilities of human intelligence, impacts an array of business processes. Organizations use artificial intelligence (AI) to strengthen data analysis and decision-making, improve customer experiences, generate content, optimize IT operations, sales, marketing and cybersecurity practices and more. As AI technologies improve and evolve, new business applications emerge.
To use AI in an effective business strategy, an organization must have a clear understanding of its business functions, how AI works and what aspects of the business can be improved through AI implementation.
Data architecture Defines how data is structured and how it flows between systems. This includes selecting storage solutions like relational databases and cloud storage.
how could AI empower your business?
What are the main established AI techniques?
The majority of use cases in AI today rely on robust and mature techniques that fall into three main categories:
Probabilistic reasoning These techniques (often generalized as machine learning) extract value from the large amount of data gathered by enterprises. This category includes techniques aimed at unveiling unknown knowledge held within a large amount of data (or dimensions). These techniques reveal unknown knowledge by discovering interesting correlations linked to a particular goal or label within that data. For example, a machine learning technique may involve sifting through a large amount of customer records, identifying certain factors and unveiling how the factors are correlated — allowing the organization to anticipate if those customers are potential churners.
Computational logic Often referred to as rule-based systems, these techniques use and extend the implicit and explicit know-how of the organization. These techniques are aimed at capturing known knowledge in a structured manner, often in the form of rules. Business people can manipulate these rules, but the technology guarantees the coherence of the rule set. (That is, the technology makes sure that rules do not contradict each other or lead to circular reasoning — which is not that obvious when you are 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. They do this by finding optimal combinations of resources, given a number of constraints in a specified 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.