How Machine Learning is Transforming Digital Business Strategies

In the information age, it has become a non-negotiable mission for businesses to embrace digital transformation to gain a competitive advantage. At the core of this shift lies artificial intelligence. It is an effective tool that is changing the traditional ways businesses function. The key attributes of AI include helping companies become more efficient and self-reliant. For instance, AI in the medical industry improves diagnostic accuracy. On the other hand, in the finance sector, it optimises risk management and helps identify scams. 

The global artificial intelligence industry is growing exponentially, with a CAGR of 37.3%. According to statistics published by Forbes, it is estimated to achieve a value of a whopping $1.8 trillion by the year 2030. This enormous market demand is reflected in Expleo's AI report - Integrating AI: Navigating the Next Wave of Business Transformation, which found that 72% of companies intend to deploy AI solutions within the next 12 months, with 91% of C-level executives confident that the benefits of AI outweigh the potential risks, opening new opportunities for businesses in the digital ecosystem. 

Artificial Intelligence: A Revolution in the Business Sector 

Businesses employ AI tools and technologies to enhance productivity, improve efficiency, and reduce costs. With continued improvements in AI, it is quickly becoming an indispensable tool for enterprises across industries.

Critical AI Components in Spearheading the Digital Transformation

The primary goals of artificial intelligence include developing systems that can replicate the thinking capabilities of humans. These systems rely on business data and utilise innovations such as machine learning (ML), natural language processing (NLP), and deep learning to streamline business processes. That said, incorporating AI into businesses demands a basic understanding of these components:

Machine Learning Algorithms

Machine learning algorithms are a subset of AI. They are generally used to make predictions and classifications based on the available data. With training, these algorithms can learn to recognise patterns, identify discrepancies, or predict future product demands. Machine learning algorithms can mine large data sets for insights to help make informed business decisions. 

Deep Learning

Deep learning is a subset of machine learning that enables the automation of tasks without human interference. Technologies such as fraud detection, virtual assistants, chatbots, etc., are some examples of deep learning mechanisms. Deep learning models can predict users' future behavior by analysing their behavioural patterns. In comparison to general machine learning, these models can accurately acquire information from structured data.

Natural Language Processing (NLP)

Natural language processing (NLP) is an extended branch of AI that allows computers and other smart devices to understand, recognise, and generate text and speech. Virtual assistants, chatbots, and voice-operated technologies like GPS systems rely on NLP. NLP can draw insights from unstructured data when used in conjunction with learning algorithms and deep learning models.

Cases of AI in Business

With new technologies invading the market and the existing ones undergoing constant development, AI applications in business are also increasing. The following are some of the scenarios where companies can use AI.  

IT Operations 

Artificial intelligence for IT operations includes using AI, ML, and NLP models to optimise operations and service management. IT members can quickly analyse large data sets, identify anomalies, monitor system performance, troubleshoot errors, and save time. 

Customer Service 

Businesses can use AI technologies to provide 24-hour customer service and quick responses, improving the overall experience. AI-powered chatbots can even resolve customer queries without human intervention, allowing the human customer representative to deal with more complex issues. In relevance to this, McKinsey & Co. reported that a South American telecommunications company saved $80 million by using conversational AI to prioritise their high-value customers.

Marketing 

Customer data helps marketers develop strategies by identifying the latest trends and spending patterns. In this scenario, AI tools can process these large data sets and forecast future spending trends while also conducting competitive analysis. They can also allow digital marketers to curate marketing campaigns for specific customers based on their interests. 

Supply Chain Management

AI in supply chain management is proving to be a transformative force. It uses predictive analytics to help organisations determine future shipping and product pricing. It also helps them sustain appropriate inventory levels, reducing possible bottlenecks or material overstocking. 

The Future of AI in the Evolving Business Environment

AI tools are becoming increasingly relevant in the changing business ecosystem. More businesses are adopting AI technologies to execute their daily operations, such as solving customer queries, developing business strategies, enhancing security, etc. In addition, AI technologies provide businesses with targeted insights and capabilities to navigate digital complexities. With ethical considerations and strategic planning, companies can proactively prepare for the future to drive unprecedented growth and customer satisfaction.

Authored By: Saket Newaskar, Director & Head of Transformation, Expleo 

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