Using Artificial Intelligence to Grow Your Business
Even in the early 2000s, companies with one eye to the future were processing large volumes of structured data to train their algorithms to learn from this data, and then optimize the outcomes to provide better business results and customer experiences. This was mainly done in the financial services industry.
The use of Artificial Intelligence (AI) technology has become integral in the financial services industry that has gone from the use of rule-based expert systems in the 1980s and 1990s to machine learning for things like credit card fraud detection today. So, traditional businesses must have taken notice and invested in AI technology, right? Only partly!
Although traditional businesses have always had access to large volumes of data, the main hurdle stopping these businesses has been an inability to use the data efficiently. Further, businesses have been slow in labeling business outcomes that aid the learning process of machine learning algorithms to transform or improve the consumer experience. Primarily, digital-first companies in the tech industry have been using AI technology, and that's because they have large amounts of labeled business outcomes that are being continually used to train algorithms. Some examples of labeled outcomes include bought or not, abandoned or bought, watched in entirety or skipped, canceled or ordered, spam or not, etc.
Major players like Amazon, Microsoft, and Facebook are reportedly spending over 10% of their annual revenue on IT and AI research, whereas this figure is considerably low in other businesses. When will businesses jump on this train, and where do they even begin? Discussed below are four catalysts that are practical and could accelerate enterprise investments in AI applications.
Automation and the Digital Workforce
Let's look at an example of how AI and automation improve customer experience. In 2018, WorkFusion, a top Intelligent Automation (IA) solutions company, showed the world how applying automation can improve customer experience. They teamed up with a top U.S. health coverage executive to improve the healthcare appeal system.
The Problem – Healthcare appeals are sent via email, phone calls, or by using an official web form. To start processing these appeals, essential information such as the complaint name, type of complaint, etc., had to be manually registered. This was a laborious process prone to human errors. Too much time spent to start the review process costs time and money for both parties involved.
The Automated Solution – WorkFusion devised an Intelligent Automation Cloud to convert a fully manual procedure into an automated one. The Automation Cloud mined data from emails using Optical Character Recognition (OCR) tools and then applied Machine Learning (ML) models to categorize and direct requests into queues.
The Benefit – WorkFusion's Intelligent Automation Cloud was able to automate close to 90% of the workload, reducing the risk of human error, and improving the overall customer experience.
Such examples should give other businesses hope and inspiration to invest in similar automation models and switch towards a digital workforce.
Low Code, No Code, & The Citizen Data Scientist
Another reason why AI hasn't progressed fast enough is the shortage of data scientists. How do data scientists help businesses? Data Scientists:
- Provide a clear understanding of industry-specific automation problems
- Have the programming knowledge needed to build advanced reports
- Use math and statistics to come up with data management solutions
If there were enough data scientists around, businesses could massively scale their workloads and improve their yearly productivity levels exponentially. How can more industry-heads get involved in the field of data science?
Automated Machine Learning (AML) technology can help. Companies like Microsoft, DataRobot, and Google are some of the several leading tech companies to have launched products that make data science processes scalable by enabling data analysts, developers, and engineers to participate in the creation and management of machine learning models.
Unquestionably, there needs to be more investment at grassroots levels. There needs to be more investment in the creation of Centers of AI Excellence to enable a sustainable and scalable operating model for innovation and real-world application of AI models.
Customer Analytics, Personalization, & Operationalizing AI
Digital transformation needs to focus more on consumer experience. Adopting a 'Customer 360' approach is the best way forward for businesses. A 'Customer 360' approach involves creating AI models that generate valuable insights leveraging customer data such as transactions, customer behavior, interactions, and demographics.
This model will form a solid basis for machine learning models to aid personalization. Personalizing customer experiences will gradually lead to more improvements and, ultimately, a fully automated approach. Some instances of personalization include posting ad recommendations based on what links the user has clicked, using automated customer service emails, and advertising personalized offers.
Companies like Amazon and YouTube are already extremely advanced in personalizing user experiences. The goal should be to deploy and integrate Machine Learning models and improve all forms of customer experience.
A successful example would be L'Oréal's venture into a data-driven customer service approach. To gain entry into the smartphone market, L'Oréal launched a mobile app called 'Makeup Genius.'
Makeup Genius allowed consumers to virtually try on their makeup products before purchase. The app scanned their customers' faces and analyzed over sixty facial features to recommend the best products. The app also learned their preferences to make deductions based on similar customers' selections and adapt its responses.
L'Oréal also teamed up with Google to collect data about specific makeup related questions so that they could recognize, forecast, and address their requirements. To track the success of this app, the brand also launched a series of YouTube tutorial videos, which amassed over 9 million views. In these videos, they shared the details of these apps and how users could get better makeup experiences by using their app. Overall, they were able to improve their customer service process massively.
Empowered edge is an IT term denoting the process of facilitating computing centralization in such a way that every user device connected to a cloud gets equal access and benefits of big data usage. Also known as 'device democracy', this process would be great for businesses that invest in it.
By 2023, the number of these 'smart' devices is set to increase up to 20 times in the IT industry. AI at the Edge will benefit industries such as:
- Retail – Facilitating sensible signage, identify the shopping patterns of customers, and present them with personalized offers.
- Industrial Automation – Connecting automated machines, predictive maintenance, and efficient quality assurance.
- Surveillance – Recognizing face patterns from images and videos.
- Smart Vehicles and Homes – Creating connectivity within each device in these structures.
- Smart Cities – Enabling smart waste management, energy management, and metering systems.
Rapid changes are coming to businesses that embrace AI. Perhaps the most significant catalyst of them all will be the competitive spirit of enterprises. As many companies have already devoted themselves to delivering transformative solutions by commissioning the latest AI and ML models, others are bound to follow soon.
Productive Edge is a leading organization specializing in helping businesses draw actionable insights from their data. We partner with our enterprise clients to enable customer-driven, technology-powered experiences that reimagine and transform the way people live and work.
By teaming up with other leaders in this field such as Microsoft, DataRobot, Talend, and WorkFusion, Productive Edge has been able to provide their clients with instant results, using tactics like data alignment, problem framing, road mapping and piloting new Data Analysis models for AI initiatives.