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The AI Dossier

Top uses for AI in every major industry — now and in the future

Use of artificial intelligence across six key industries

After decades of science fiction fantasy, artificial intelligence (AI) has made the leap to practical reality and is quickly becoming a competitive necessity. Yet, amidst the current frenzy of AI advancement and adoption, many leaders and decision-makers still have significant questions about what AI can do for their businesses.

This dossier highlights dozens of the most compelling, business-ready use cases for AI across six major industries. Each use case features a summary of the key business issues and opportunities, how AI can help, and the benefits that are likely to be achieved. The dossier also includes several emerging AI use cases for each industry that are expected to have a major impact in the future.

Of course, the best uses for AI vary from one organisation to the next, and there are many compelling use cases for AI beyond the ones highlighted here. However, reading through this collection should give you a much clearer sense of what AI is capable of achieving in a business context—now, and over the next several years—so you can make smart decisions about when, where, and how to deploy AI within your organisation (and how much time, money, and attention you should be investing in it today).

How will the industry harness the power of AI, particularly as it relates to data quality and complexity?

Encompassing a wide range of businesses including automotive, consumer products, retail, wholesale & distribution, transportation, hospitality & services, the industry has a strong and defining focus on serving customers – and a common set of current and future business issues they are trying to solve.

Why does AI adoption and deployment seem to be less extensive and mature in ER&I than in most other industries?

The main challenges to increased AI adoption and deployment largely revolve around data. To succeed with AI, ER&I companies should have strategies and roadmaps based on a practical understanding of what parts of the business are best suited for AI.

Aside from numerous FinTechs that are fully embracing AI, why are most financial services firms still in the early stages of AI adoption and investment?

To increase adoption rates, the important next step is to stop dabbling with AI and start embracing and industrialising it so that AI solutions can be deployed on a large scale across the entire enterprise. Simply throwing more money at the problem won’t be enough.

Why do AI adoption and maturity levels vary widely across government agencies?

AI Adoption rates vary depending on the government agencies, their existing infrastructure’s reliance on legacy systems, and workforce fluency. However, a common trend has emerged from the growing use of robotic process automation (RPA) to automate back-office activities.

Even though AI is widely recognised as a strategic business imperative, why have many organisations only scratched the surface of AI’s potential?

Most health care and life sciences organisations have been primarily using AI to automate repetitive tasks and standard business processes. By combining AI technology with the fields of medicine and science, many are looking for opportunities to transform some of their most critical processes and achieve sustainable competitive advantage through AI.

How is AI driving value in the technology, media and telecommunications sector?

Telecom companies tend to be the furthest along at embracing AI, thanks to the sector’s longstanding focus on operational efficiency and customer acquisition/retention. Many technology companies have been slower to embrace AI, while others were born on AI. In the media sector, most of the focus on AI has been on personalising content and customer engagement.

Six ways that AI creates business value

Looking across all AI use cases, there are generally six major ways that AI can create value for a business:

  • Cost reduction. Applying AI and intelligent automation solutions to automate tasks that are relatively low value and often repetitive, reducing costs through improved efficiency and quality. (Example: Automating data entry and patient appointment scheduling using natural language processing).
  • Speed to execution. Reducing the time required to achieve operational and business results by minimising latency. (Example: Accelerating the process of drug approval by using predictive insights to create a synthetic trial).
  • Reduced complexity. Improving understanding and decision making through analytics that are more proactive, predictive, and able to see patterns in increasingly complex sources. (Example: Reducing factory downtime by predicting machinery maintenance needs).
  • Transformed engagement. Changing the way people interact with technology, enabling businesses to engage with people on human terms rather than forcing humans to engage on machine terms. (Example: Using conversational bots that can understand and respond to customer sentiment to address customer needs more effectively).
  • Fueled innovation. Redefining where to play and how to win by using AI to enable innovative new products, markets, and business models. (Example: Recommending new product concepts and features based on customer needs and preferences mined from social media).
  • Fortified trust. Securing a business from risks such as fraud and cyber – improving quality and consistency while enabling greater transparency to enhance brand trust. (Example: Identifying and anticipating cyber attacks before they occur).
Make smarter decisions about when, where, and how to deploy AI

Organisations have the opportunity to unlock the full potential of AI when they embrace it and deploy it at scale throughout their enterprise.

AI is quickly becoming a competitive necessity for nearly all types of businesses – driving unprecedented levels of efficiency and performance and making it possible for businesses of every shape and size to do things that simply weren’t possible before.

The key to success is to start small but think big. According to a recent Deloitte survey – State of AI in the Enterprise, 3rd Edition – 74% of businesses surveyed are still in the AI experimentation stage with a focus on modernising their data for AI and building AI expertise through an assortment of siloed pilot programs and proofs-of-concept, but without a clear vision for how all the pieces fit together. By contrast, only 26 percent of businesses are focused on deploying high impact AI use cases at scale, which is when the real value kicks in.

While AI adoption rates and maturity vary widely across industries, AI is driving new levels of efficiency and performance for businesses of all sizes.

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