Enterprise AI: how AI implementations are impacting enterpriseBy Jon Taylor on April 21, 2022 - 15 Minute Read
The widespread adoption of artificial intelligence (AI) in enterprise is no longer a distant pipe dream – it's well and truly here.
Enterprises are now spending big to reap the rewards of AI. From Amazon to Google, the world’s largest enterprise companies use AI to make smarter business decisions, improve the customer experience, drive more efficient processes and – ultimately – boost revenue.
Gartner predicts AI software revenue will total $62.5 billion this year, while 43% of businesses say AI and machine learning (ML) matter much more than perhaps initially expected; nearly one in four said both initiatives should have been their top priority sooner.
AI is clearly important to enterprise companies, and more organizations are investing in their AI infrastructure. So, how can enterprise businesses ensure they understand the impacts of AI so they can reap the benefits?
Let’s find out! In this blog, we’ll cover…
- A brief definition of Enterprise AI
- How does Enterprise AI work?
- Why is Enterprise AI important?
- What is the impact of artificial intelligence (AI) on enterprise?
What is Enterprise AI?
Enterprise AI is a segment of enterprise software that uses tools like machine learning (ML) and data analytics to deploy products and improve business processes.
A simple definition of Enterprise AI is that it does things a human brain cannot do. From analyzing supply chains to calculating data to maximize profit or predict customer behavior, AI can be a powerful tool to help boost revenue. All of these tasks are performed more efficiently and accurately with Enterprise AI.
How does Enterprise AI work?
While in some circles AI may still be associated with robots and science fiction, that’s quickly changing as more households and businesses invest in the software.
Homes now have AI in their living room thanks to devices like Amazon’s Alexa and Google Home, and businesses benefit from ML and data mining to sell more products. According to an Algorithmia study into Enterprise trends, 76% of Enterprise companies are now prioritizing AI and ML over other IT investments. Companies are also employing more data scientists and using AI to improve customer experience.
Companies now use AI and ML to interact with customers, generate insights and automate processes. Source: Algorithmia 2021 enterprise trends in machine learning.
So, what does using AI in enterprise look like? Let’s look at Alibaba, where AI is used every day. With over 1.3 billion customers, it’s the world’s largest e-commerce platform, selling more products than Amazon and eBay combined.
How do they handle this overwhelming number of customers? ?
One key step on Alibaba’s enterprise AI journey is through the use of predictive analytics. Alibaba can predict what customers might want to buy and uses this data to create algorithms that suggest products and vendors to improve customer experience. Then there’s the use of Natural Language Processing (NLP), which automatically generates product descriptions for products instead of paying a human to do it.
The company has also invested in:
- AI chatbots. The company’s chatbot, Dian Xiaomi, understands around 90% of customer queries and cuts the need to invest in a human customer service staff.
- Robots. 200 robots across automated warehouses pick, pack and deliver over one million shipments for Alibaba. Every. Single. Day.
These improvements in processes have enabled Alibaba to branch out into other projects, like City Brain (where AI algorithms monitor vehicles to cut traffic jams) and Alibaba Cloud (which helps farmers improve yield and cut costs using AI).
Alibaba’s City Brain. Image source.
Why is Enterprise AI important?
Companies can now solve problems in unprecedented ways thanks to the advanced capabilities of Enterprise AI.
By combining AI with chatbots, robotics and other intelligent systems, companies can interact with more customers at scale. Enterprise AI can automatically make accurate predictions and decisions, leaving teams with more time to engage with customers and work on growing the business.
But, more than anything, Enterprise AI is a powerful partner for humans to increase their capabilities. Just take a look at Amazon’s evolution.
The company uses AI in many of its processes, from product delivery to customer experience. Many readers will immediately look at Alexa, the digital voice assistant, as an example of the company’s use of AI – but it goes far beyond that.
Amazon uses AI to collect data and predict what products you’ll buy next. In the process, it discovers more about your buying behavior and preferences. This data then feeds Amazon’s algorithm so you are shown products you’ll love and buy while increasing their profits.
The tactic is also used on Amazon Go, where customers shop in cashier-less stores and are charged for the items they pick up instead of waiting until they get to the checkout. AI makes this happen. Sensor technology and machine learning replace humans in the store, but still give customers the same experience. For example, if a customer picks up an item (but they don’t want to purchase it), they can put it back and the sensors will detect they’ve changed their minds. This information is then fed back into the app so the customer isn’t charged.
An Amazon Go store in San Francisco. Image source.
Customers simply scan items with the Amazon Go app and leave the store when they have everything they need. As soon as they do, an itemized receipt will land in their Amazon Go app so they can review it and make sure there are no mistakes.
So, why does this all matter?
It shows the capacity and opportunity available for enterprise companies that invest in AI. Instead of replacing jobs, AI has the potential to build on human capabilities, but it also won’t destroy jobs as many perhaps initially feared. In fact, a recent PwC study found any job losses from automation will likely be offset in the long run by new jobs created as a result of the larger and wealthier economy made possible by these new technologies.
From startups to SMEs and leading enterprise giants, AI has the potential to transform business from the ground up – and partner with humans to create a better experience for users to enjoy.
What is the impact of artificial intelligence (AI) on enterprise?
AI allows companies to use every piece of data they have to its fullest potential.
Enterprise companies, by their nature, typically have extremely large data sets, packed full of customer information and intelligence that have the potential to unlock improvements in processes. But, without AI, it’s almost impossible to capitalize on all of this data to full effect – and use it to make the most efficient decisions for a business.
That’s all changing as more businesses invest in AI. According to Forbes, 83% of enterprises increased their AI and ML budgets in 2020, which continues to rise. This investment is helping companies to hire data scientists and specialists with ML skills to improve operating processes and integrate AI into existing systems.
Another key impact that AI is having on the enterprise is its ability to remove bias from decision making.
McKinsey research found that AI can reduce our subjective interpretation of data. Machine learning algorithms learn to consider variables that improve their predictive accuracy, and the improved decision making means processes are fairer. The result is companies can make decisions on customers and revenue using cold, hard data – instead of predictions, guesses and gut-feel!
The benefits of using AI in the enterprise
There are several use cases for using AI in enterprise. From automation to better decision making, here are the biggest benefits for investing in Enterprise AI ?
Revenue growth by finding problems early
AI combines better customer experiences with better processes and decision making to bring more money into a business.
AI can detect weak points in the company, like churn or slow product sales, and use data to figure out why. It can also create accurate forecasts at an individual SKU-level, using data around prices and supply/demand to calculate how much inventory is needed to keep customers happy.
Automated decision making to increase productivity
According to a Gartner study, 44% of all AI investment globally in 2023 will be for decision support and augmentation.
These stats underline the belief businesses have in the power of AI to help their teams make better decisions. The goal of AI should be to empower humans to be better, smarter and happier, not to create a ‘machine world’ for its own sake, according to Gartner research vice president Svetlana Sicular.
Augmented intelligence is a design approach to winning with AI, and it assists machines and people alike to perform at their best.
Research Vice President, Gartner
Enterprise companies are already realizing the power of automated decision making, with one PwC study finding that AI could improve employee productivity by 40%.
That may explain why when Netflix invested in AI to help with day-to-day operations and improve its algorithm: it saved the company $1 billion. The investment – a mix of machine learning, improved customer experience and automated, algorithm-based recommendations – has helped the company retain customers and continue to grow.
Better processes to help stay competitive
Finally, AI can automate a lot of the manual tasks that humans simply don’t have the time or capacity to do to help companies scale efficiently and remain consistent.
AI can automatically analyze data, predict outcomes and interpret analytics to help companies make better decisions and stay competitive. In fact, a Deloitte study found companies that invest in AI have been able to edge slightly ahead or even leapfrog their competitors.
The survey found 82% of companies gained a financial return from AI investments, and that AI was improving everything from product development to internal operations and decision making. But like any investment, AI also has some hurdles that businesses will need to overcome in order to make a success of it.
The cons of using AI in the enterprise
One of the main challenges for AI in the enterprise is around education and, crucially, team adoption.
If teams don’t have experience with AI tools, there can be a significant learning curve. When Deloitte asked business leaders what the main challenges with AI were, they cited implementation and integrating AI into existing tech stacks.
Apart from the learning curve, some businesses may hesitate around factors like cybersecurity and customer data privacy. Some AI systems can be vulnerable to “data poisoning,” where information is manipulated by virtually untraceable software. But as Bloomberg’s Tim Culpan explains, companies can spot malicious software by feeding systems data and allowing the machine to learn independently.
Computers armed with numerous examples of both good and bad code can learn to look out for malicious software (or even snippets of software) and catch it.
Technology columnist, Bloomberg
What does the future hold for Enterprise AI?
AI isn’t going anywhere. Enterprises need to juggle adopting new technologies or risk falling behind their competitors. And it doesn’t have to be a choice between AI or humans – enterprises can leapfrog ahead by using both!
AI will help enterprise businesses make better decisions, automate processes and scale in a way that human-only workforces can’t. There’s no way to predict just how far AI will go. Still, if enterprise companies invest in systems now, they can harness the power of machines and humans to create better experiences and most importantly – retain customers.
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