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Business enterprises are built on core values, upon which their daily activities and commitments are met to generate profit. Enterprises are competing to deliver the best products, services, and solutions, which keep them running in the global market. Introducing “disruptive technologies” like Artificial Intelligence (AI) helps revamp the enterprise offerings and processes, mostly leading to a make-or-break situation for the said business.

It has been quoted as the next big step into the evolution of our industries, where it is possible to simulate the best outcomes. As per the Worldwide Artificial Intelligence Spending Guide from the International Data Corporation (IDC), the global spending on AI is expected to cross $110 billion in 2024.

For businesses to achieve agility, succeed in innovation, and pursue growth, it is becoming inevitable to focus on AI technologies. With more and more companies incorporating AI capabilities into their value chains, AI is changing from mere technical adoption to enterprise-level acceptance.

AI supporting enterprise operations

The operations management team of any organization effectively runs the show by taking care of production and redesigning, ensuring customer expectations are met on time and everything in between. With the proper intervention and assistance of technology, operational efficiency can be improved drastically, thereby boosting the business.

With AI onboard, we have a solution for everything from real-time forecasting and sourcing to warehouse automation. Machine learning and computer vision can detect patterns in data and physical and digital environments to help improve efficiency and avoid setbacks. For example, AI can help redesign manufacturing processes and assembly line practices in the manufacturing industry to bring down costs and delivery time. It can also help ease the procurement process by enabling a better view of global supply availability and costs. Further, Advanced analytics allow analysts to review and improve supply chain processes.

Retail operations like demand prediction, inventory level forecast, seasonal customer demands, etc., are dealt with using various Analytics, ML, and Natural Language Processing (NLP). In addition, virtual shopping assistants powered by NLP help customers with their shopping and ensures a better shopping experience for the customers.

The Healthcare industry has also opened its door to AI-powered solutions. For example, machine learning and analytics help create better treatment plans, identify new healthcare insurance approaches, and offer many healthcare transformations. In addition, AI-powered automation can increase healthcare productivity by taking care of routine tasks. To give an example, we see that virtual assistants with NLP capabilities are acting as primary contact agents for patients. AI, without a doubt, is changing operations and processes across industries, slowly but steadily.

AI to facilitate Marketing activities

Industries are finding opportunities for AI in their various departments. For example, the marketing department has been using marketing automation tools for some time now. Imagine incorporating AI capabilities into the marketing software. It is a recipe for better results.

AI makes marketing “data-centric,” helping marketers easily find data for their needs. Promoting products/services with the right price and the right message to the right people is possible with AI. Further, it enables the prediction of sales maintenance, service optimization, and refine sales lead prioritization.

Using natural language to remodel content from existing ones for better impact automatically is another use case for marketing. AI can enable personalized communication with relevant user audiences and improve customer service, which is handled by intelligent marketing engines

Innovation through AI

AI technologies have gained mainstream recognition and adoption over the past decades, reaching a stage where innovation in AI demands attention. Technologists say AI is evolving from machine learning to deep learning and now to deep reasoning, where machines show strong reasoning abilities. Deep learning and reasoning are the AI abilities for tomorrow, where devices can act with reason and make moral decisions on facing complicated situations. With the elements and dynamics of IoT, the Digital twin concept is often integrated with machine learning and software analytics. As a result, it continuously learns and updates itself from multiple sources in its environment.

The digital twin can be effectively used for optimizing the operation and maintenance of physical assets, systems, and manufacturing processes. Another upcoming AI innovation is the capsule networks, a new type of deep neural network with the ability to process visual information the same way a human brain does.

Every other day innovations are happening in AI that is bound to be disruptive in nature. Enterprises across the globe are looking forward to the what and whatnots of these technologies through rigorous research and development.

Overcoming the challenges in AI adoption

Despite all the excitement, AI adoption also faces challenges. Regulatory changes in technical and commercial space will put a damper on the ability and rate of adoption and bring an increase in cost. While research and innovation are pushing the boundaries and finding new means to improve the technology, there is a lack of engineering solutions that can help in adoption. Privacy considerations need to be addressed at every stage of adoption to avoid creating situations where user data is used wrongfully or without permission

Definite steps are required to overcome the challenges while adopting AI. First, there is the need to encourage the broader use of AI and supporting technologies to improve productivity and growth across the global economy. Also, it is possible to make sure that AI adoption is becoming quicker and more accessible by addressing human resources with mismatched skills. Finally, it is essential to resolve ethical, legal, and regulatory issues by ensuring algorithm transparency and accountability.

Conclusion :

The adoption of AI into businesses is still in the initial phase but the adoption is quite fast. AI starts in many business processes like customer service management,  workflow management, price optimization, etc. Personalization to AI is making AI more powerful to predict customer needs and providing mote better support. The adoption process of AI may need a clear and proper long-term strategy. So companies may need to have a collaborate with a third-party digital transformation solution company like the UST visit their website to find out how they can help you to strategize the process of adopting AI.

I want to hear from you – comment, ask questions and join the discussion in the coming weeks


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