It is natural for organizations to flatter their market rivals by copying their successful business strategies. In fact, corporate history is littered with examples of winning ideas being conveniently replicated. Take the now-overcrowded smart home assistant market, for example. Not many years after Amazon debuted the Echo in 2015, rival products such as Google Home and Apple HomePod arrived to compete for market supremacy.
In a similar vein, organizations have seemingly understood the power of AI in the last few years. Keen to jump on the AI bandwagon, about 48% of organizations today use machine learning, deep learning and NLP to analyze massive amounts of dynamic data efficiently. However, not every business may know how to use AI effectively to maximize their ROI. In fact, some may even mistake basic automation for AI-based functionality while investing in tools and technologies for digitization. AI and machine learning need to be employed specifically for businesses to achieve tangible profits. Then, once profitability is unlocked, is there nothing more?
Essentially, two questions need answering—is AI as profit-enhancing as every thinkpiece on the internet may have you believe? And, considering the code of ethics of AI, is profit-chasing the only reason why businesses should use the technology?
Is AI Implementation Profitable?
AI implementation, whilst following the various ethics of AI, does not automatically translate into profitability for businesses. Michael Kauffman, the Principal and Chief Legal Officer of renowned IT firm Tech DNA, stated in this insightful article that enhanced profitability with AI depends on something known as profit proximity.
According to Kauffman, for most businesses, AI-driven profitability is achieved in the shortest possible time when machine learning algorithms are used for analyzing data related to customers. For example, AI-enabled personalized marketing and product recommendations have the power to increase their sales almost overnight. Customer trends are inherently volatile, and so, using machine learning-predicted trends and suggestions, businesses can aggressively push products and services to targeted consumers. As per Kaufmann’s observations, using AI for product improvement takes slightly more time to translate into profits. For instance, automating the supply chain processes of product design, assembly and packaging allows an organization to improve their output and overall productivity, but noticeable profit generation takes longer as that depends on aspects such as pricing and purchases made by their consumers.
According to the idea of profit proximity, companies that incorporate AI into functions that will improve their reputation gradually will have to wait the longest to reap increased profitability. For example, if an organization uses AI to bolster their cybersecurity infrastructure or their financial audits, the positive influence of the machine learning algorithms will not be apparent in the sales of their products or services immediately. In the long term, the seamlessness of their services and the lack of security breaches will strengthen their reputation for their existing and prospective audiences. More importantly, hard-earned goodwill ensures that organizations using AI for this type of purpose sustain their profitability for longer periods.
Here are some of the ways in which machine intelligence improves the profitability of businesses—that also follow the ethics of AI:
Workflow automation involves the use of robots, computer vision and AI to automate repetitive tasks such as CRM, data assessment and packaging, which would otherwise consume several hours to be executed. Human workers who would handle those tasks can be drafted in to perform more creative, cognitive ability-testing tasks. This results in the reduction of human error from metronomic, repetitive business functions, while also reducing the supply chain lead time. Most importantly though, AI enables businesses to put their personnel to more productive uses.
Machine learning, as you know, enables software or hardware applications to find patterns and anomalies within large amounts of data. So, AI-driven maintenance applications can scan through the massive output generated from machines in a production, packaging or another kind of facility. This allows organizations to accurately pre-empt future malfunction or breakdown of machines and perform repairs or replacement before time. This AI-driven maintenance technique allows businesses to reduce downtime—often one of the biggest reasons for business losses.
Ultimately, most businesses will eventually figure out the different ways in which they can maximize their revenues with AI. However, limiting AI into a profit-generating entity can cause businesses to miss out on the other, bigger possibilities out there. Also, using the technology for a singular purpose undermines three of the main ethics of AI – fairness, accountability and equity.
Can AI Be Used for the Greater Good by Organizations?
Understandably, an organization cannot save or improve the world around them on its own. For that purpose, they’ll need to collaborate with NGOs, governments or local authorities. Before that phase, they’ll have to choose a problem that requires investment and AI for resolution. Here are some ways in which organizations can leverage (or are already leveraging) AI for the greater good:
Leveraging AI and Blockchain for COVID-19 Vaccine Distribution
You may be aware of how the wealthiest countries monopolized COVID-19 vaccines, leaving the poorest nations to wait until 2023 to get their jabs. Despite all the progress made in terms of vaccinating people against COVID-19, there are still millions out there with nothing but their natural immunity against the deadly virus and its many variants. To help such countries, organizations can tie up with NGOs or local administrators there. Vaccine distribution poses several logistic conundrums, such as managing cold chain channels and coordinating several stakeholders. To overcome this, organizations can create a connected IoT network for real-time information collection and transfer. Machine learning algorithms can be used to predict areas most affected (and in the highest need of jabs) at any given moment in the future. Such algorithms will assess several factors, such as the population of a given region, whether any large gathering took place there and the average age of people there, before providing recommendations. Apart from that, accurate, data-driven predictions about supply chain bottlenecks with AI can allow distribution teams to choose the least congested channels to bring the jabs to those who need them the most. Blockchain-based systems make vaccine distribution channels more foolproof and impossible to infiltrate for external entities. Blockchain, due to its decentralized nature, allows multiple stakeholders, such as transporters, warehouse managers and hospitals, to track vaccine consignments being transported in real-time. Setting up such an infrastructure wouldn’t take more than a few months and promises to improve the lowly vaccination rates of poor countries. As the saying goes, nobody is safe until everybody is safe against this virus. Using AI enables companies to save millions of lives with minimal investments.
Just like vaccinations, organizations can tie up with local authorities and use IoT, AI and blockchain-based applications to reduce food shortage, food insecurity and hunger in downtrodden countries too.
Using Machine Learning to Eradicate Preventable Blindness
One of the main applications of AI in healthcare is the prevention of diabetic retinopathy, a disease that involves high blood sugar causing partial or total vision impairment in patients. A few years ago, Microsoft collaborated with K Chandrasekhar, the owner of a technology company named Forus Health, for AI-driven diagnosis of patients who may suffer from blindness due to diabetes in the future. Together they developed a product named 3nethra Classic that enabled healthcare workers to scan the eyes of people in hundreds of villages. This AI-based tool could predict the formation of cataracts in patients’ eyes due to blood sugar, much before it occurred. Accordingly, those who could potentially become visually impaired were offered treatment or therapy. In this way, a massive megacorporation such as Microsoft used its infinite resources, technical acumen and AI to deal with a major problem in several countries around the world. Such initiatives allow organizations to live up to the ethics of AI.
AI may be one of the few ways in which organizations around the world can bridge the widening chasm between the haves and have-nots. After all, equality is a big part of the ethics of AI. By using AI for the greater good and not just more money, organizations will greatly enhance their own goodwill and reputation.
Now, that’s an idea worth copying.