Today’s business landscape is significantly tech-driven, and AI is at the heart of this change. Indeed, for an organization to fully reap the benefits of artificial intelligence, it is no longer sufficient to experiment with it in bits and pieces. Instead, a full-blown AI-driven digital strategy has become a “must-have” for companies that wish to accelerate growth and retain a competitive advantage.
Mitra Azizirad, Corporate VP for Microsoft AI, was quoted as saying, “In the next five years, every successful company will become an AI-company. It is now the next level of competitive differentiation.” No wonder the global AI market is expected to reach $15.7 trillion by 2030. Those still caught in the loop of experimentation and risk assessment face the dangers of being left far behind than organizations taking practical measures to implement AI.
Indeed, with its ability to operate and analyze faster and more effectively than the human brain, AI provides unprecedented performance benefits. The purpose of this blog is to get an in-depth insight on how organizations can leverage AI to accelerate competitive advantage, look at a few real-life examples of companies using AI to solve business problems, and tips for leaders to implement AI at-scale.
AI capabilities are advancing quickly, and companies are achieving tangible benefits from implementation. While it is true that many companies haven’t deployed AI technologies yet, and some are working towards scaling it, the early-mover advantage of adopting AI may fade away soon.
Research conducted by Deloitte has found, “AI adopters are investing significantly, with 53% of the respondents spending more than US$20 million over the past year on AI-related technology and talent. At the same time, 71% of adopters expect to increase their investment in the next fiscal year by an average of 26%.” Consequently, to retain a competitive edge, leaders may quickly need to move beyond leveraging AI to optimize and automate processes and start using AI to create new products and operation methods.
In such a scenario, starters, or businesses still dipping their toes into AI adoption, with no concrete plan for the future, are at significant risk of jeopardizing their operations.
To implement and scale AI, organizations must consider the technical aspects and the ethical and cultural ones too. Consequently, it is not only essential to strategize in terms of the data, technology, and skills required. Instead, leaders must determine the business ventures in which AI can achieve something productive and ensure the benefits are felt inclusively by everyone in the organization.
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AI solutions can act as intelligent assistants of employees and streamline business operations by merely taking over most of the mundane, repetitive, low-involvement work, allowing employees time to focus on the more productive, valuable, and expertise-requiring tasks.
Companies have a significant amount of customer data at their disposal. However, it is only AI, with its ability to learn and provide insights on such data, that helps organizations adopt an objective and data-fuelled approach to meeting customer expectations. Advanced AI analytics enables companies to obtain a holistic, 360-degree view of their customers. Keeping track of customer behavior helps companies tailor product recommendations to the customer’s taste and generates brand loyalty.
When the business landscape is becoming increasingly digitized and many organizations are moving their resources to the cloud, data security becomes a significant matter of concern. AI solutions can track patterns and locate any deviations or anomalies. They can predict and prevent threats and other suspicious activity or outages (for example, a DDOS attack) that can bring the entire network and business operations to a standstill.
Here’s a case study by Microsoft:
Centrica is an energy and services company serving in Ireland, the UK, and North America. Their biggest AI venture, ‘Ask Wilbur,’ leverages natural language processing (NLP) technology to support contact center agents. When a customer reaches out to support, the bot pops up and asks the agent what the customer wants. So, if the agent says, “the customer wants a brand new supply connection,” the bot comes up with a quick list of questions that the agent may ask further. In Centrica’s own words, net promoter score for customers serviced with Wilbur turned out to be higher than for those who weren’t.
Today’s customers expect fast, easy, personalized, and accurate support. Moreover, employees need quick access to information to carry out daily operations and address customer concerns in the shortest time-frame.
AI-enabled chatbots provide information faster and more accurately, thus delivering a fair, competitive advantage. Automated support is available 24/7, and chatbots can quickly provide the relevant service and handle requests at scale with increased efficiency and reduced costs.
Let’s look at how Microsoft helped one of the biggest retail chains implement AI:
M&S, one of the most prominent names in UK retail since 1884, holds an outstanding brand image for quality products and services. Within M&S, AI-driven predictive analytics has proven to be valuable in building an efficient supply chain, forecasting customer requirements, and deriving useful insights from complicated datasets to assist with more accurate product management.
With the abundance of data available at every organization’s behest, decision-making at any organizational level has become more data-centric. Such information is used for AI-driven predictive analytics.
Be it decisions related to inventory reporting, purchase dynamics, demand planning, supplier discounts, supply predictions, or even customer preferences and customer loyalty programs, AI-enabled analytics helps leaders make smart choices in every aspect of the business.
East Suffolk & North Essex NHS Foundation Trust (ESNEFT), a healthcare organization, post implementing its first-ever RPA solution, did a pilot project for invoice processing in the finance team. By the 12th month, not only was the company able to avoid potential human errors, but it also increased efficiency by releasing about 4,500 hours a month.
Robotic Process Automation (RPA), or RPA, leverages AI and machine learning to perform various routine, repetitive tasks, including calculations, data entry, etc.
When the cognitive ability of chatbots is combined with the automation capacity of RPA, enterprises can automate tasks end-to-end. Moreover, an RPA-enabled bot can integrate with legacy enterprise systems to retrieve information and handle more complex tasks. read more