AI in business – how to use artificial intelligence to improve businesses

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AI in business

What is Artificial Intelligence?

Artificial intelligence (AI) refers to the use of advanced analysis and logic-based techniques like machine learning (ML) for interpreting events, supporting and automating decisions, and taking action. AI techniques can enable IT leaders and data analysts to solve an array of business problems and generate a considerable return on investment (ROI).

The main opportunities of artificial intelligence to create or accelerate the growth of the digital business are:

  • By identifying better ways of doing things by advanced probabilistic analysis of outcomes.
  • By directly interacting with systems that take actions to minimize human-intensive calculations and integration steps.

AI will reshape how work is done in the future as the technology will be able to replace some of the tasks typically performed by humans and change how everyday decisions are made.

How to successfully implement an enterprise AI strategy

Implementing an enterprise-wide AI that identifies use cases, aligns business and technology teams, quantifies benefits and risks, and changes organizational competencies to support AI adoption will help the organization to capture the maximum benefits of AI.

Key elements of enterprise AI strategy include:

AI vision: Identify the focus areas that promote and enable organization-wide fluency and adoption of AI.

AI risks: Assess your exposure to different key areas of risk, comprising regulatory, reputational, and organizational. Also, assess the mitigation plans for these risks.

AI strategic action plan: Identify the impact on business models, processes, people, and skills and assign accountability for AI strategy development and execution.

AI adoption: Explain clearly the use cases, and use value maps and decision frameworks to prioritize adoption.

Pursue buy-in to the AI program: Promote the use of AI by projecting subsequent successes to peers and giving C-suite leaders the ability to tell the AI team’s stories.

Some examples of AI applications in business

AI innovation is disrupting existing markets and enabling new digital business initiatives. It is being applied across industries, organizations, and functions in various ways. A few examples of AI in business are:

Machine learning: ML enables human-like communications and is driving common AI applications like chatbots, autonomous vehicles, and smart robots.

Deep learning: Deep learning techniques enable the use of facial recognition, voice recognition, and neural networks that deliver highly personalized content based on data mining and pattern recognition.

AI in the IT operations/service desk: Virtual support agents (VSAs) provide IT support in an IT service management (ITSM) scenario together with the IT service desk.

AI in supply chain management: AI can help in predictive maintenance, risk management, procurement, order fulfillment, supply chain planning and promotion management.

AI in sales and sales enablement: Ai improves sales by identifying new leads and opportunities based on the similar existing customer. It can also help in establishing customer relationships through intelligent activity tracking and messaging and using guided selling to improve sales execution and increase revenue.

The future of AI and AI technologies, prediction by Gartner

As AI is evolving rapidly through new techniques, dedicated infrastructures and hardware, in the next 5 years, Gartner expects that organizations will adopt cutting-edge techniques for smarter and more reliable, responsible and environmentally sustainable AI applications. Gartner also predicts that 50% of enterprises will have devised AI orchestration platforms to operationalize AI by 2025.

With the maturation of the AI market by 2025, AI will be the top category driving infrastructure decisions, resulting in a tenfold growth in computing requirements.

Organizations will continue using AI to enhance their decision-making processes and become more agile and more responsive to ecosystem changes. As the need for AI grows and the investment required increases, the cloud may become harder to afford. Therefore, the emergence of cloud/on-premises hybrid strategies that balance investment in cloud function with investments in infrastructure are becoming attractive.

Read next: The Worldwide Artificial Intelligence In Medical Imaging Industry is Expected to Reach $3.2 Billion by 2027 –

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