Top 10 Ways AI is Transforming Business 

Language barriers can be some of the biggest challenges for any business wishing to expand its market. As well as causing problems during in-person meetings and conference calls, language barriers can hinder email communication, create misunderstandings and irritations and lead to errors and huge mistakes across the supply chain. While AI-enabled technologies, such as Google Translate, already exist to partially remove language barriers, they can be inaccurate and impractical for live multinational communication. 

But new AI developments relying on National Language Processing (NLP) could do wonders to remove the challenges caused by language barriers in the enterprise. NLP provides machines with the ability to listen, understand, read and even infer in a variety of different human languages. Through it, companies will be able to use devices to apply linguistic definitions to text or speech. 

One of the major areas of digital transformation in the enterprise has been manufacturing. Companies already employ AI-enabled technologies to pick up anomalies in the manufacturing process as well as alert manufacturers of production errors and malfunctions before they happen. This has helped companies lower operational costs, improve efficiency, and reduce delivery time to the market. 

With the advancement of AI, machines on the manufacturing line will be able to manage more and more repetitive tasks, removing the need for human intervention. AI and machine learning will allow companies to extend the reach of potential applications in the manufacturing process from real-time equipment maintenance to virtual design, allowing for the utilisation of new, improved and customised products in the supply chain.

With the environmental applications for artificial intelligence broadening, the technology is beginning to be implemented into sustainability projects. A wide range of economic sectors can harness AI in a variety of situations to contribute to managing the environmental impacts of climate change. This can include AI-infused clean distributed energy grids, precision agriculture, sustainable supply chains, environmental monitoring, and improved weather and disaster prediction and response. 

Microsoft recently commissioned research by PwC which models the economic impact of AI’s application to manage the environment across four sectors – agriculture, water, energy and transport. The study estimates that AI for environmental applications could contribute up to $5.2 trillion to the global economy by 2030, a 4.4 per cent increase from today. 

Generative AI has exploded in popularity in recent months with the launch of AI-wired tools like OpenAI’s image generator DALL-E 2 and ChatGPT. These technologies have sent shockwaves across the marketing world as teams discover new ways to streamline their workloads – using AI to automate everything in email marketing and content creation.

Over the past few months especially, more and more advertisers have been turning to AI for more predictive analysis of data, allowing them to make more informed marketing decisions and greatly improve their marketing strategies. Meanwhile, the knock-on effects of AI developments on other sectors are changing the way marketers position their businesses in the digital world. Generative AI chatbots like Microsoft’s Bing Chat, which aims to provide direct responses, potentially overhauling the search and SEO industry of today.

The typical recruitment process for most companies involves posting a job ad online, reviewing candidate applications, and conducting interviews. While human interaction will likely always remain the best candidate, many companies have already begun using AI-enabled recruitment and talent-sourcing solutions to filter skilled applicants rapidly and effectively. An example of these solutions is AI application screening tools that read job descriptions and recommend the best candidates based on their described qualifications. 

Other AI-wired chatbots can also help recruiters with hiring questions, asking candidates questions about their skills and experience so that hiring managers can receive answers from a large pool of candidates with minimal effort. This greatly accelerates the recruitment process, allowing businesses to find the most qualified candidates faster and stopping applications from being forced to wait weeks to hear a response from an application. 

Cyber attacks can be devastating for any business.  A study by Positive Technologies estimates in 93% of all hacking cases, that actors can breach security defences and access local network files. In the midst of a global shortage skilled cybersecurity professionals, organisations find themselves struggling to keep up with the ever-evolving threat landscape and the recent surge in cybercriminal activity. As the threat of cybercrime heightens, more and more organisations are turning to AI to move pressure away from security teams and create solid defences against cybercriminals. 

AI-wired tools allow teams to detect, discover and respond to threats in real-time using machine and deep learning. They also automate manual and repetitive tasks that would otherwise be left to humans, while also analysing networks in depth to identify gaps in security that could leave a company’s infrastructure vulnerable to attack. As well as building defences against attacks, AI accelerates detection and response times when attacks strike, identifying malicious activity early enough to block them before they cause any serious damage. 

Many companies have adapted to a data-driven approach for operational decision-making. The term “data-driven” even implies that the processor is human-curated and summarised for people to process. But to fully make the most of the value of data collection, companies can employ AI to take the place of humans,  evolving from data-driven to AI-driven workflows.  

The average company must analyse huge amounts of data before making a decision – a process that can be wasteful, inaccurate and time-consuming. AI and ML technologies allow data analytics teams to have huge amounts of data readily available. With this data, AI can help teams identify trends, make predictions on future results, and suggest the right action plan for decision plan. Given that AI is generally more accurate and quicker at making decisions than humans, it can save companies time, money, and resources used in manual decision-making. 

Despite the recent layoffs across Big Tech caused by the post-pandemic economic slowdown, many large companies are continuing to expand their AI teams and accelerate the development of the technology. Companies have become aware of the efficiency gains that can be achieved through leveraging the power of AI and machine learning, computer vision, and similar technologies, and demand for skilled workers in the space is quickly outstripping supply.

This has further heightened the already high demand for workers able to work with intelligent machines, turning AI engineers and data and machine learning experts into some of the most sought-after professionals in the industry. It’s predicted that 97 million jobs involving artificial intelligence (AI) will be created between 2022 and 2025. 

Software programs like Salesforce and Zoho require great amounts of human intervention to remain up-to-date and consistently accurate. But when AI is applied to these platforms, a normal CRM can be transformed into a self-updating, auto-correcting system able to manage and maintain relationship management within a business. 

As well as maintaining CRM, however, AI has the potential to forecast capabilities and insights regarding sales trends, metrics and statistics. AI coupled with CRM software could there will allow users to predict customer requirements and sales trends that are unique to their business. Generative AI technologies such as OpenAI’s chatGPT could also be integrated into CRMs to access insights from recorded meetings, revenue and deal lifecycles, and go-to-market datasets to dynamically generate contextualized prompts and personalized email suggestions.

The pandemic and the subsequent disruptions have demonstrated the dramatic impact of uncertainties on supply chains, establishing the need for smart contingency plans to help companies deal with these uncertainties in the right way. While AI adoption rates remain relatively low across the supply more and more companies have turned to AI in recent years for better inventory management, smart manufacturing, dynamic logistic systems, and real-time delivery controls. 

But the main benefit of an AI-wired supply chain comes from its ability to automate multiple management processes. This allows for more accurate capacity planning, improved demand forecasting, enhanced productivity, lower supply chain costs, and greater output, all while fostering safer working conditions. Experts believe that AI-enabled supply chain management could soon transform the industry, allowing the tore network supervisory group to deal with stock consistently and intelligently.