Intelligent impact: Why applied AI is different and what it means for everyone

Across this wide spectrum of AI, there is a vast and real potential for people and businesses of all kinds to deliver services in smarter, more efficient ways using AI. It can help organizations to save time, money, and resources on critical tasks, empowering productivity, improving services and reducing waste.

Yet, as researchers debate whether machines can ever truly ‘think’, and if AI models should be exactly like humans or just human-esque, the sense of progress in practical AI solutions can seem slow. Debates over the definition of ‘intelligence’ within AI research can make the subject feel academic, limiting its appeal and practicality for business users.

So while AI is positioned as the next big thing, its technology can seem on the horizon. Out of reach for most companies. 

Yet there is a strand of AI that stands out as a leading way to develop a profitable, sustainable future: applied AI.

What is applied AI?

Applied AI is a branch of computer science that takes AI software out of the lab and applies it within business environments to perform real-world tasks. 

Applied AI uses many of the same machine learning methods as traditional AI research. The key difference with applied AI is an emphasis on output and solutions, rather than its internal processes or how closely it mirrors human capabilities. 

The success of applied AI is defined by its ability to achieve positive and immediate human impact in the world at scale, and for it to be trusted and commercially viable. In fact, the commercial focus of applied AI has created the largest share of advancements within all AI research. 

According to the 2022 AI Index Report from Stanford University, private investment in AI rose to $93.5 billion in 2021, more than double the previous year. The boom in investment has helped many companies create successful applied AI solutions that are readily available to businesses today. 

Moreover, applied AI has developed extreme proficiency across a range of tasks, meaning their solutions can offer significant efficiency gains to the businesses that adopt them natively. 

Why many AI solutions are limited

Many of today’s AI solutions fall short in four main categories: 

• Exciting but yet to be realized
  • AI is creating new and exciting services that fundamentally rethink certain areas of human life — like self-driving cars. 
  • Unfortunately, the self-driving vehicle category is difficult to scale for a variety of reasons, such as the practicalities of software training or the wider legal environment. For example, Europe lags in deploying autonomous vehicles due to regulatory hesitancy. 
  • Consequently, AI solutions in the ‘yet to be realized’ category are largely limited to proof of concept stages, meaning their applications won’t be available en masse anytime soon.

• Available today but underutilized
  • ⁠AI applications like natural language processing are being widely deployed with great commercial success, and have become household tools for many millions of people. Amazon Alexa and Apple’s Siri are just two examples. However, their positive impact is limited, relative to self-driving cars. 
  • Rather than reshaping a key aspect of human life (like transport), for many people natural language processing software has been constrained to assisting in search queries and small day-to-day tasks like setting alarms.

• Beneficial but in the background
  • AI can be applied to background operational tasks within business or government. These types of AI solutions can manipulate and analyze data at scale, helping to optimize new advertising campaigns or, most recently, model outcomes from the pandemic. 
  • Despite the potential benefits of greater applications, personal data usage is a growing concern for customers. These AI tools lack a strong foundation of consumer confidence which limits their wider rollout.

• Nice solution but no application
  • DeepMind’s AlphaGo was the first computer program to win a game of Go against a professional human player and the reigning world champion at the time.
  • The board game is profoundly complex, with 10 to the power of 170 possible board configurations (that’s more than the number of atoms in the known universe). Consequently, AlphaGo’s proficiency undeniably represents a significant advancement in AI technology, as the software is able to identify and deploy strategic game solutions amid billions and billions of options. 
  • However, AlphaGo doesn’t currently solve any real-world or enterprise problems, meaning its place in AI’s future is yet to be defined.

Why applied AI is different

Each of the above types of AI solution falls short in one or more of five key success factors of applied AI: 

1. It is immediate.
2. It creates a positive human impact.
3. It is ready to scale.
4. It is a trusted solution. 
5. It has commercial applications.

We call this disparity between AI and applied AI the ‘application gap’ — the shortcoming of AI solutions to quickly, effectively, and ethically deliver a solution that’s commercially viable and beneficial to its greater communities.

For this reason, our definition of applied AI is that it:

• is available today;
• ⁠has an obvious positive impact on people’s lives;
• ⁠is accurate and trusted; and
• ⁠solves systemic, real-world problems affecting millions of people.

Applied AI offers businesses an opportunity to transform their processes and reimagine customer journeys and outcomes for the better.

Applied AI for growth and sustainability

Businesses must innovate, adapting to today’s challenges with available technology. Applied AI solutions are the key to unlocking greater growth potential without sacrificing the health of our planet. 

Industrial AI tools can help businesses become more efficient and productive, allowing employees to eliminate repetitive time-consuming tasks from their workflows. 

With newly available time gained by applied AI, employees can add meaningful value elsewhere and play more active roles in business growth — benefiting customers and enhancing market competition.

To fulfil its positive impact, applied AI must also be considered as a key to addressing sustainability issues, helping to maximise resources and reduce waste.

Consumers increasingly make spending choices based on environmental issues. Studies show 84% of consumers claim a poor environmental track record may cause them to stop buying from a brand. 

Equally, Chief Marketing Officers (CMOs) cite customer experience as one of their top marketing challenges in 2022, meaning that consumer opinions are more essential than ever to long-term viability.

Sustainability, corporate social responsibility (CSR) and profit can intersect to serve both the interests of businesses and the planet. 

CSR can limit your operational costs, increase your agility, improve consumer perceptions, and create a more distinct brand in a crowded global marketplace. Applied AI for sustainable applications can achieve the same outcomes. 

Applied AI and AI for Good

Our definition of applied AI isn’t altruistic or overly optimistic — it’s the new model of how organizations can approach 21st century enterprise challenges. And the approach has precedent.

AI for Good is a United Nations initiative that seeks to identify and scale AI-based solutions for sustainable human development. One of the organization’s campaigns is to build a stakeholder network to tackle the climate crisis. 

The aim is to develop innovative technologies to model new resource management strategies by monitoring consumption levels and measuring environmental impact. 

And AI for Good is part of a broader movement: Tech for Good. The world’s biggest firms, including Microsoft, IBM, Huawei, and Google, have invested significant resources into research projects to identify more ways to combat climate change. 

Tech for Good companies are trying to leverage the latest tools and platforms to identify the root causes of major humanitarian issues to create lasting, sustainable solutions. But how can businesses remain profitable whilst investing in sustainable practices? 

Simultaneously creating business value and meeting climate change goals is no small task. Here, companies can capture significant commercial opportunities by identifying inefficiencies in processes and workflows. By allowing AI to undertake manual tasks, you can find solutions that reduce climate burn. 

In this way, AI can empower organizations to meaningfully combat climate impact while also improving their wider operations. In particular, applied AI can help reduce business costs and free up staff talent to work on keeping companies competitive.

Applied AI, when realized to its fullest potential, is synonymous with a Tech for Good approach: combining social, environmental and commercial benefits to achieve a net-positive effect.

How the numbers add up

Not only is AI enabling social, environmental and commercial benefits, there are significant financial benefits for AI-enabled businesses.

IBM’s report on the Business Value of AI identifies at least three areas where quantitative gains can be made:

Reduced operating costs: More than 85% of advanced adopters are reducing operating costs with AI. 47% have realized cost improvement in process efficiency, 41% in supply chain and production, and 39% in headcount efficiency improvements. Reduced costs typically equate to reduced environmental impact.

Revenue gains: ⁠Advanced AI adopters attribute 10-12% points of revenue gains (or erosion offset). AI companies report 6.3% points of direct revenue gains directly attributable to AI on average, which offset revenue erosion for those hit hardest by the pandemic, or helped capitalize on new growth opportunities for those experiencing greater demand.

Operational benefits using virtual agent technology: ⁠99% of companies report a reduction in cost per contact from using virtual agent technology—estimated at $5.50 cost savings per contained conversation, which corresponds with a 12% rise in customer satisfaction, a 9% rise in agent satisfaction, and 3% revenue gain.

So we can see how AI can create significant business value and operational efficiencies, while also effectively tackling critical sustainability concerns. 

Applied AI technologies offer substantial gains for all kinds of organizations, simultaneously helping businesses to identify new efficiencies and mitigate their environmental impact.