Artificial intelligence and business, it seems, is a marriage that is all but inevitable. Artificial intelligence is intelligence built into computing systems, or as MIT professor Marvin Minsky put it, “the science of making machines do those things that would be considered intelligent if they were done by people.” In sum, this concept of extending the “intelligence” of systems is the core of how AI enables critical advantage for businesses.
AI isn’t new. People use it every day in their personal and professional lives. What is new is are new business offerings thanks to two major factors: 1) a massive increase in computer processing speeds at reasonable costs, and 2) massive amounts of rich data for mining and analysis.
AI accomplishes business activities with greater accuracy and at a fraction of the time that humans take. But it’s no slam dunk: AI can be expensive to buy and install, takes a lot of management cost and time, and has some ethical baggage. Its benefits often outweigh its disadvantages, so we’ll look at both.
This report from Harvard Business Review reflects the nascent use of AI in business, with the many respondents in the exploration phase.
Artificial Intelligence in Business: The Awakening
InfoSys in its survey report Amplifying Human Potential: Towards Purposeful Artificial Intelligence reported that the most popular AI technologies for business were big data automation, predictive analysis, and machine learning. Additional important drivers include business intelligence systems and neural networks for deep learning.
Certain industries have adopted advanced deployments of AI at a faster rate than others. Pharma/life sciences, automotive and aerospace, telecoms, energy, oil/gas and utilities, manufacturing, shipping logistics, healthcare, and financial services lead the pack.
Artificial intelligence in business brings AI benefits – and challenges – into business areas including marketing, customer service, business intelligence, process improvement, management, and more.
Major Use Cases for Artificial Intelligence in Business
The biggest use cases driving AI in business include automating job functions, improving business processes and operations, performance and behavior predictions, increasing revenue, pattern recognition, and business insight.
1. Automate job functions to improve efficiency. This driver crosses industry verticals and horizontals, and includes any repetitive job function that AI can automate. Manufacturing robots are the most obvious example but far from the only one. AI can automate any job function that requires repeated manual input, such as generalist IT functions. AI also drives efficiency by automating machine workloads, such as collecting and analyzing sensor data. And for better or worse, the public is used to automated phone attendants, chats, and emails that are driven by AI.
2. Improve business processes. Process improvement is well advanced in manufacturing, where industrial robots have made inroads for many years. AI also improves business processes in transportation and logistical supply chains, such as dynamically adjusting trucking routes by tracking weather and traffic delays. Business continuity uses AI systems that automatically detect and mitigate anomalies like power surges or potential security breaches. Another important AI frontier is biomedicine. Matching donor kidneys to transplant patients is traditionally a long and laborious process because the patient and donor population frequently changes. A Carnegie Mellon University research team created AI technology to accelerate and improve the matching process.
3. Predict performance and behavior. AI applications can predict time to performance milestones based on progress data, and can enable customized product offers to web search and social media users. Predictive AI is not limited to traditional business: Disney Labs, Caltech, STATS, and Queensland University partnered to develop a deep learning system called Chalkboard. The neural network analyzes players’ decision-making processes based on their past actions, and suggests optimal decisions in future plays.
4. Increase revenue. Companies can increase revenue by using AI in sales and marketing. For example, Getty Images uses predictive marketing software Mintigo. The software crawls millions of websites and identifies sites that are using images from competitive services. Mintigo manages the huge sales intelligence database, and generates actionable recommendations to Getty sales teams. Northface uses IBM Watson to analyze voice input AI technology and recommend products. If a customer is looking for a jacket, the retailer asks customers what, when, and where they need the jacket. The customer speaks their response, and Watson scans a product database to locate two things: 1) a jacket that best fits the customer’s stated needs, and 2) cross-references the recommendation by weather patterns and forecasts in the customer’s stated area.
5. Pattern recognition. AI pattern recognition improves investigations into non-compliance or fraud in digital communications, while social semantics and sentiment analysis works for purposes as different as social media marketing and terrorist investigations. Customer activity patterns can also generate product recommendations and content curation. Netflix saves $1 billion a year by using AI to recommend videos customized to each viewer. Netflix discovered that viewers are 4-5 more times more likely to click on a video that appears on their Netflix recommendations screen. Netflix uses their AI system to track individual watching patterns, then recommends less well-known videos to the viewer. This system optimizes Netflix’ $6 billion a year content spend, since most of these videos are less expensive for Netflix to buy than blockbuster premium content.
6. Business insight. AI can interpret big data for better insight across the board: assets, employees, customers, branding, and more. Increasingly AI applications work with unstructured data as well as structured, and can enable businesses to make better and faster business decisions. For example, sales and marketing AI applications suggest optimal communication channels for content marketing and networking to best prospects.
Based on the HBR report, predictive analytics is a leading business use of AI, followed closely by text classification and fraud detection.
AI Business Concerns
For all its benefits, AI projects are often costly and complex and come laden with security and privacy concerns. Don’t let these issues blindside you: carefully research the business challenges around AI, and compare the costs of adopting an AI system against losing its benefits.
· AI is expensive. Advanced AI does not come cheap. Purchase and installation/integration prices can be high, and ongoing management, licensing, support, and maintenance will drive costs higher. Build your business case carefully; not just to sell senior management, but to understand if the high cost is worth the benefits – especially if a big business driver is cost reduction.
· AI takes time. Give installation plenty of time in your project plan, and build your infrastructure before the system arrives. High-performance AI needs equally high-performance infrastructure and massive storage resources. Businesses also need to train or hire people with the knowledge skills to manage AI applications, and complex AI systems will require training time and resources. Many businesses will decide to outsource some or all their AI management; often a good business decision but an added cost.
· AI needs to be integrated. There may also be integration challenges. If your AI project will impact existing systems like ERP, manufacturing processes, or logistics systems, make sure your engineers know how to identify and mitigate interoperability or usability issues. Businesses also need to adopt big data analytics infrastructure for predictive and business intelligence AI applications.
· AI has security and privacy concerns. Cybersecurity is as important for AI applications as it is for any business computing – perhaps more so, given the massive amounts of data that many AI systems use. Privacy issues are also a concern. Some of AI’s most popular use cases — ranging from targeted social media marketing to law enforcement — revolve around capturing user information. Businesses cannot afford to expose themselves to security or privacy investigations or lawsuits.
· AI may disrupt employees. Some positions will benefit from AI, such as knowledge workers who give up repetitive manual tasks in favor of higher level strategic thinking. But other employee positions will be reduced or eliminated. Although businesses must turn a profit, employee disruption is awkward, unpopular with the public, and expensive. According to Infosys, companies with mature AI systems make it a point to retrain and redeploy employees whose positions were impacted by AI automation.
Deploying AI systems is a big project, but is ultimately a business technology like any other system. Carry out due diligence. Research and build your expertise and infrastructure. Then deploy, use, refine, and profit.