Updated 12 Nov 2022
It sounds amazing to become an AI-driven enterprise and use your company’s data to streamline your operations, cut unnecessary expenses, and increase revenue.
However, implementing AI and ML across your entire company is more difficult than simply creating a machine learning model and putting it into use. It can be difficult to even get models into production in the first place.
Because there are so many barriers to adopting AI, businesses frequently miss out on the cost savings and added revenue that new technologies can provide.
It’s good to know that this is not necessary, however. In this article, we’ll discuss what AI adoption is, some of the common obstacles businesses encounter when implementing AI and how you can prepare your company to overcome them.
Artificial intelligence (AI) is specialised software that aims to replicate the cognitive processes of the human brain in order to solve issues and accomplish objectives. Since the 1950s, AI has been developing, and several efforts have emerged in recent years. Currently, machine learning (ML) is the most widely used kind. Computers learn from a vast amount of data and use algorithms to make predictions. To translate human language into programming for chatbots and other virtual communications systems, Natural Language Processing (NLP) collaborates with machine learning (ML).
Businesses and organisations are embracing AI, with most investments going toward automation for customer service requirements, such as chatbots and virtual assistants. Other common justifications for using AI include social data mining, automating human resources, improving goods and services, and interpreting languages (i.e., Google Translate). Though still in its infancy, AI usage is rising globally among businesses trying to improve productivity, increase efficiency, and streamline operations. The financial services, high-tech, and telecom industries are where AI adoption in the enterprise is most prevalent.
Although the use of AI is growing, there are still some significant obstacles to overcome. AI requires a significant amount of pertinent data, which not all businesses and sectors have gathered. Due to the fact that artificial intelligence (AI) is still in its infancy, organisations have difficulty finding talent with these talents. When deploying AI, businesses are also concerned about data privacy and cybersecurity vulnerabilities.
Businesses of all sizes, small and large, are becoming more and more aware of the value of implementing AI to meet both their immediate and long-term objectives. AI technologies have the potential to significantly change how businesses are structured by:
Gains in productivity and efficiency are two of the advantages of integrating AI in businesses that are frequently mentioned. The velocity and scale at which the technology completes tasks surpass that of humans. Meanwhile, AI frees up human workers to focus on higher-value tasks that machines can’t handle by taking over those tasks from them. This enables businesses to maximise the skills of their human capital while minimising the costs associated with performing routine, repeatable tasks that can be handled by technology.
Customer support is the capacity of a company to address and satisfy the demands of its clients when those clients’ consumer preferences alter over time. Such activities, from a digital perspective, include the development of Help Desk tickets that record customer issues and digital outreach initiatives that cater to customers’ interests and strengthen client connections.
Through the use of virtual chatbots, customer intent prediction, multi-channel communication tailored to customer preferences, and the rerouting of business-consumer interactions to the appropriate departments, these relatively simple tasks are easily automated and improved.
AI may be used by executives to expand their company models. Data and analytics deployment in the company creates new potential for organisations to compete in several fields.
For instance, with the vast amounts of data that autonomous vehicle companies are gathering, they may be able to find new insurance-related revenue streams, and an insurance company may be able to enter the fleet management market by applying AI to its enormous data stores.
Behaviour data surplus has evolved into the most valuable piece of knowledge a company or for-profit organisation may have in the age of surveillance capitalism. Consumer sentiment has a significant effect on market dynamics, but it was challenging to transmit and quantify this information prior to the development of big data.
Businesses today have indirect access to a wealth of digital data that documents the unique characteristics of every person’s life, including age and gender as well as demographics, personality traits, political and social interests, socioeconomic status, professional aspirations, social networks, and much more.
Countries all over the globe have been establishing rules and making significant investments in the field of AI in the race to acquire and create the most effective and inventive technology. the International Development Research Centre (IDRC) and Oxford Insights performed the Global AI Readiness Index in 2019. The statistics are listed below. The index rates 194 nations and territories according to how ready they are to use AI in internal operations and the delivery of public services. Each nation receives a score based on 11 input parameters divided into 4 clusters: governance, data and infrastructure, education and skills, and government and public services. According to the report, nations with strong economies, a wealth of data and information, and powerful governments dominate the rankings.
From these statistics, we can see that Asia, Europe and North America are dominating AI adoption.
It is crucial for the industry that technological behemoths like Microsoft, Google, Apple, and IBM contribute to the healthcare sector. AI is now being used for various healthcare services, including medical imaging, medication management, drug development, robotic surgery, and data mining for pattern recognition and subsequently carrying out the more precise diagnosis and treatment of medical disorders.
Perhaps the only industry where the majority of end consumers can see the deployment of AI in action is retail and e-commerce. Retail businesses always search for methods to identify trends in consumer behaviour so they can better align their strategy and outperform rivals in this highly competitive industry.
The industrial food processing industry has also been impacted by AI development. For instance, a company has created AI-based food sorting machinery for the market for ground and diced beef, french fries, and peeled potatoes. Their food processors can assist food-processing businesses in automating food analysis tasks such as calculating the fat content in meat or gauging the size, shape, and colour of french fries.
In the farming industry, where we have seen a rise in the use of intelligent tractors and intelligent plucking machines, AI applications have also been introduced.
The emergence of AI applications has caused a significant upheaval in the banking and financial services sector. There is a tonne of AI application cases in this field. In many cases, sophisticated software robots are replacing human workers to handle loan applications in milliseconds. Similarly to this, robo-financial advisers quickly sort through several levels of data to suggest the best investments for clients.
The logistics and transportation sector is about to undergo a transformation powered by AI. Supply chain management has already undergone significant change and become a smooth operation thanks to the application of machine learning and predictive analytics. Robots with AI capabilities are widely used in warehouses to sort and package merchandise. Additionally, AI algorithms are being used more and more to support last-mile delivery and find the quickest shipping route.
The widespread adoption of AI-enabled chatbots is expected to have a big positive impact on the tourism sector. Because they are available around the clock and provide prompt answers to questions, chatbots have been shown to be an effective way to increase customer satisfaction and engagement.
Chatbots are being powered by increasingly effective AI algorithms, enabling them to respond to client inquiries with more accuracy. To enhance the customer experience, many sizable travel companies adopting AI are hiring AI companies to build their own AI-based mobile apps and chatbots.
AI’s use in the real estate sector is creating new opportunities for brokers, clients, and agents alike. Brokers are becoming more strategic, clients are feeling more in control, and agents are becoming more productive and efficient. Brokers and agents can help people looking to buy, rent, or sell a property find the ideal match with the aid of AI-powered bots.
There is no question that the industrial sector is setting the pace for the use and acceptance of AI technologies. AI is being used in manufacturing at all levels and lines of operations, from labour planning to product design, enhancing productivity, product quality, and worker safety.
Machine learning and artificial neural networks are used in factories to enable predictive maintenance of essential industrial machinery that can foresee asset failure accurately. It enables management to take prompt action to repair the equipment and avoid expensive unscheduled downtime.
AI tools are commonly used by organizations for a few primary reasons. One of the biggest is intelligent automation, which reduces stress on company resources. Others include business intelligence, the practice of deriving insights, natural language processing, customer engagement, and visualization.
Infosys Nia is a collection of solutions for typical business issues including contract analysis and client interaction. Automating intricate operational operations and streamlining business data workflows are the core responsibilities of Nia. The data collection and analysis tasks are efficiently handled by this AI platform, freeing up company resources to concentrate on mission-critical tasks.
Businesses may examine their data more easily with Periscope, a platform for data visualisation and business insight. Periscope uses artificial intelligence to assemble and examine information from many sources. The user can then select a chart or visualisation from a selection to view the gathered data and derived insights. As a result, the product gives businesses a complete solution for visualising data and getting insights from it.
The infrastructure and customer management requirements of a firm are managed in many ways by Wipro Holmes. It offers services including contract intelligence, infrastructure automation as a service, enterprise diagnosis solutions, and automated service request fulfilment. Holmes enables businesses to integrate AI into many of their core processes, boosting productivity while streamlining current workflows.
Business intelligence solutions for companies are Sisense’s primary emphasis. It is a self-service, cloud-based application programming interface (API) that offers insightful data and is accessible from anywhere. Sisense, which offers a variety of visualisation and analytics choices, has an emphasis on finding a rapid fix for business intelligence issues.
Receptiviti is an innovative AI system that draws ideas from a company’s employees by using psychology. Receptiviti gathers information from a company’s workers through connections with platforms like Slack, Gmail, and Office365. It uses technology to provide insights into psychology, emotion, social hierarchy, and relationship quality.
Even if investments in AI may be expensive in the near term, companies who choose not to adopt and benefit from it will soon fall behind those that do and will incur more significant financial costs in the long run. We never said that implementing AI across your organisation would be simple, but when done correctly, you’ll enjoy the benefits for many years to come. Starting an AI implementation may be intimidating, especially if you don’t have the right tools in place to help your team as they figure out how to best integrate data science across the organisation.
While your route to success with AI may not be precisely the same, now is an excellent opportunity to stand back, evaluate your company, and make any necessary adjustments.
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