Artificial intelligence is changing consumer experiences and looking into the future, AI will enable human-machine interactions in productive ways. The ability of AI to understand the environment, process information, and respond through smart decisions¹ makes it a valuable technology.
Netflix’s use of AI systems to recommend movies to customers is an example of technology for decision-making with JP Morgan using AI and ML tools to make lending decisions.
The retail sector² adoption of AI keeps increasing as Walmart, Amazon, and Alibaba use machine-learning algorithms to predict market trends and boost customer experiences.
In the health care industry, Partner Healthcare and Mayo Clinic’s deployment of AI for patient management is assisting to improve patient care.
AI Transforming Enterprises and Consumers
Digital transformation means using technology to eliminate tedious roles that hamper productivity. Artificial intelligence is reducing these burdens by making us better and accurate in decision-making.
A pew research survey estimates a $15T addition to the global economy in 2030 as artificial intelligence drives human decision-making and value addition. Business revenues increase from AI deployment³ because of streamlining workflows and increasing efficiency.
Algorithms are assisting in response to climate change as scientists use data and intelligent systems to track weather patterns.
AI is transforming national security with global governments increasing their defense budgets.
The British Ministry of Defense recently allocated a £5M AI budget for national security to build intelligence systems. The Defense and Security Accelerator (DASA) noted that the British government is using AI for intelligence operations and data analysis.
These use cases of AI involve smart decision-making⁴ and attest the value of artificial intelligence to enterprises and consumers.
Let us explore ways AI is enabling human-decision making in the digital world:
1. Customer Recommendation
Machine learning tools enable customer experience by recommending products or services with Netflix¹¹ a good example here. Netflix uses machines learning to explore customer viewing habits and recommending movies.
For example, customers who watch Game of Thrones get recommendations to watch new movies in the same genre such as Lord of the Rings. This raises customer experiences through smart predictions and responding to market needs.
Alibaba¹² increased investments in AI and ML to meet consumer demand. Shoppers using the Alibaba platform explore product categories recommended by machine learning tools making their experiences better.
The AI systems use customer history to recommend products⁵ the customer could find interesting to purchase. #Machinelearning systems interact with customer data enabling companies to understand customers’ needs translating to more sales.
2. Customer Relationship Management
Airbnb is using data analytics⁶ and machine learning for customer relationship management in the hospitality industry. Customer data in the Airbnb¹³ database undergoes processing where AI systems offer information about current customer trends including problem areas, complaints, and assisting service agents to make good choices.
Customers who miss booking require changes and machine-learning #algorithms identify them and communicate with service agents. This reduces customer complaints and retains them.
Enterprises fail in customer relationships because of inaccurate information and slow response times, and AI makes this possible. Apple is using AI technology to understand customer preferences, communication, and complaints.
AI systems recommend to Apple¹⁴ decisions about customer complaints and improving services. In the past, customer complaints delayed prior to reaching a company but with AI systems, forecasting customer problems has become easy.
3. Analytics Management
The process of acquiring, processing, and implementing data information makes the difference between a competitive and less competitive business. Amazon consists of strong analytics systems in its retail strategy⁷ to assist the company manage operations and offer valuable decisions.
The retail market requires consistent mapping of trends for companies to customize products and boost customer experiences. #Analytics bridge this gap by keeping businesses updated with information.
Analytics is augmenting the retail sector by deriving value from data content and risk management. By using advanced analytics and machine learning tools, retail companies respond to market changes by creating better services/products. Analytics communicate to the C-Suite about emerging market trends and enables allocation of resources in a responsible manner.
4. Team Productivity and HR Systems
Corporate organizations use teams to achieve objectives and AI makes the process smooth by communicating data to teams. Knotel¹⁵ deploys machine-learning algorithms in their workspace modeling to help companies boost productivity. According to Knotel, machine learning assists the company to measure and predict workspaces models based on current market conditions.
Teams face challenges making decisions and for remote ones, the problem becomes complicated. AI solutions are changing the coordination of teams with Zoom a company investing in AI and Ml to address team coordination.
Machine learning solutions⁸at Zoom enable fast response to customer challenges by predicting problems facing teams. Team management systems differ depending on market conditions and Zoom is using artificial intelligence to optimize team communication and management.
NASA makes use of #artificialintelligence tools in enhancing collaboration between teams. Astronauts analyze machine-learning systems and coordinate with their colleagues in real-time. For complex projects/missions, NASA finds AI systems beneficial for analyzing trends and communication. These ML tools used at NASA enable the organization to keep teams together.
5. Solving Problems
Artificial intelligence assists in generating solutions to problems by using data to reason and recommend decisions. The expert software utilizes machine-learning algorithms for decision-making by assisting enterprises to explore challenges and generate creative solutions. Humans handle multiple roles and using machines in solving problems increases accuracy and competitive ability.
Enterprises make unpopular choices in their strategic management and expert systems bridge this gap by using data for generating outcomes. Intelligent AI systems⁹ process data sets by recommending practical solutions for business challenges.
Businesses respond effectively to these problems as machines guide in decision-making. Walmart utilizes artificial intelligence in learning the shopping behaviors of consumers and predicting new market trends.
6. Market Intelligent Systems
The survival of companies in the next decade will depend on automation and adoption of technologies such as AI and ML. The enterprise faces challenges in predicting market trends and results in losses because of poor decisions. Facebook’s adoption of artificial intelligence makes the social media giant competitive in this area as the company maps consumer patterns in real time.
AI tools at Facebook enable the company to judge customer behavior based on their posts and interact with other online users. Other social media companies including Snapchat, Twitter, and Instagram are using machine learning to predict customer behaviors.
Healthcare is another industry augmenting AI and ML for decision-making with Partner Healthcare using predictive analytics for patient management. Partner Healthcare utilizes a patient database to judge patient conditions and predict underlying health conditions.
Their AI systems connect patient history with the current health conditions and predict medical solutions. This makes patient care effective and improves quality as technology augments humans in the decision-making process. Mayo Clinic investments in AI are assisting in patient management and providing quality care.
7. Risk Management and Efficiency
Every business needs an understanding of prevailing risks and this is where AI comes in. Artificial intelligence through predictive analytics is assisting companies to figure out risk areas and respond early.
Insurance companies use AI systems for data management with a focus on risk factors. Algorithms aid insurance companies to forecast losses and chances of no premium payments from customers.
The financial sector is following suit by using AI and ML tools to predict financial risks and developing solutions¹⁰ based on intelligence systems. JP Morgan is using AI and #bigdata analytics to judge customer behaviors and extend lending.
AI systems assist JP Morgan to disapprove loans to customers with a poor credit history. Consequently, the bank reduces overall risks and safeguards customer interests by using analytics to assess the market situation.
AI Support System for Consumers and Enterprises
From these examples of AI augmenting humans, one thing is clear: AI technology is making decision-making fast and reliable as machines interact with people for productive outcomes.
The decision-making process comes with limitations for people but AI and ML technologies are supporting these repetitive tasks. Why keep doing tedious tasks when you can focus on important areas of your business that need attention?
AI adoption by businesses is accelerating but many organizations lack information about the deployment process. Enterprises set aside AI and ML budgets but in reality, focus on other areas leaving AI projects to gather dust.
A recent Gartner report found that 40% of enterprises have an understanding of AI benefits in augmenting decisions while 60% have no idea about AI solutions.
Organizations sift through data and complex information within a short time as machine learning and #deeplearning tools optimize processes. Businesses explore market trends through AI systems and respond to market changes making them competitive.
Digital transformation from AI is enabling companies to increase output and take charge of the future through strategic decisions. As computing capabilities and storage increases, the time is right for technology to augment us. Digital transformation calls for adopting artificial intelligence systems and machine learning to make operations effective and add value to customers.
¹Smart Decisions, ²Retail Sector, ³AI Deployment, ⁴Decision-Making, ⁵Product Recommendations, ⁶Data Analytics, ⁷Retail Strategy, ⁸Machine-Learning Solutions, ⁹Intelligent AI Systems, ¹⁰Developing Solutions