Artificial Intelligence, or AI, seems to be the talk of the town for businesses globally. Yet, most remain clueless about its tangible constructs and applications for their organisations.
AI can learn, evolve, act intelligently, and engage in human-like activities. Essentially, it tries to mimic human intelligence as best as possible via a computer program. These software programs, once coded, do not require human interference and can autonomously come to a solution provided they have sufficient information embedded.
Hence, AI can be of immense utility to businesses in the following ways:
- Employees tend to lose interest when work starts getting monotonous. To tackle this, organisations often go out of their way to hire associates at lower pay. You can avoid this extra expenditure and productivity loss by automating the drab, yet essential routine tasks.
- Secondly, businesses are increasingly relying on data and statistics to make relevant business decisions. Considering how data-driven firms have become these days, it’s wise to enable AI to gather and organise large datasets.
These are just two scenarios where businesses can find AI to be extremely relevant, timely and tailor-made for their business needs. There are countless other scenarios where this technology remains meritorious. And it is these advantages that have allowed automation to gain a strong standing in the business landscape these days.
Be it identifying your weak points by analysing customer service requests, or pinning down your most profitable assets, AI finds a wide range of applications in the industry. And failing to capitalise on AI may lead organisations to lose their relevance and competitive advantage, not a fate that many envision or even desire.
Let’s understand more of this technology that can be such a major ‘make-or-break’ factor in an organisation’s journey.
The most common types of AI –
Here are three of the most common and widely used application types for artificial intelligence today:
1. Robotics process automation
This refers to the automation of digital and physical tasks, mostly routine ones like updating customer records, taking data backups, analysing service feedback, and so on.
RPA, with its on-point intelligence, is much more advanced than your typical business process automation tools. It can function like a human for a number of tasks, while for others, it can flag inadequacies to be worked on.
This technology is also quite cost-effective and typically brings quick and high returns on the investment made. While it’s the least expensive among the three types we’ll be discussing here, it’s also the least ‘intelligent’ in the sense that such applications aren’t evolved enough to learn and improve—yet.
For example, at NASA, RPAs are used in the form of bots with varying levels of intelligence to automate multiple functions across the four core verticals.
2. Cognitive insight
This type of artificial intelligence deals with everything related to analytics. Data, coupled with evidence-based decisions, makes analytics non-negotiable for most businesses, which is where Cognitive Insight AI comes to the rescue.
It studies vast amounts of data sets and provides predictive modelling. In simpler terms, it can help forecast important business outcomes like what a particular customer is likely to buy, can identify fraud patterns, analyse warranty data in products, automate ad targeting, provide insurers with real-time insights, etc.
Now, the question arises: How do cognitive insights differ from traditional analytics? There are, basically, three main distinctions. To start off, they are way more detailed and data-intensive. Secondly, their prediction models are trained on organised and updated data sets. Finally, the more data these models encounter, the more they evolve and become intelligent.
General Electric, for example, has used this set of technologies to eliminate redundancies and renegotiate supplier contracts. The firm uses cognitive insights to run an analysis of its expenditure patterns and identify trends for improvement. The company saved more than $80 million in the first year itself.
Similarly, Deloitte’s auditing practice is leveraging cognitive insight to extract terms from contracts. This enables increasingly efficient and vast audits without requiring excess manpower.
3. Cognitive engagement
This type of AI refers to programming projects that engage employees and customers on a number of topics, at scale. They do this by leveraging natural language processing algorithms, chatbots, etc.
We discussed earlier how the service needs of consumers have multiplied and grown in complexity over time. Similarly, the need for internal business communication has grown manifold with initiatives like reskilling, employee feedback, human resources management programs, etc. gaining great momentum.
This variety of functions cannot be profitably, or even conveniently, accommodated by human associates. Therefore, cognitive engagement serves as a superior alternative.
However, as of today, companies are hesitant about this technology, owing to its relative immaturity.
Facebook, for instance, discovered that its Messenger chatbots couldn’t answer quite a few service requests without eventual human intervention.
Hence, a lot of companies are restricting their use to specific domains and platforms. This will completely change once cognitive engagement is out of its teething stage though.
Adopting artificial intelligence for your business
If you’ve been wondering whether you should go the AI way or not, ponder no more. There’s no time like the present to dive into the digital era with finesse.
Here are a few simple steps you should take when adopting artificial intelligence for your firm:
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Identify the problems for which you need AI:
The first question you need to ask yourself is: What exactly is it that you are looking for? This consideration is more important than you’d think, since it’ll shape the scope you choose for your version of AI integration and usage. Check if adding AI capabilities to your existing products and services will truly be of benefit.
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Bring in experts to get things started:
It’s important to note that artificial intelligence hasn’t been thoroughly tested for a number of case scenarios. This also translates into a shortage of skilled and informed personnel who actually know the domain well.
Therefore, you need to get the right set of people on board to start off.
Do a smaller pilot study that will be temporary and controlled. You can gain a lot of integral insights from this simple exercise.
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Plan for data storage:
Any artificial intelligence program requires access to near-unlimited, structured, and clean data. Only then can it work to its full potential.
This brings with itself the challenge of properly storing all the data, especially in a swiftly retrievable manner. Therefore, this planning is essential in your implementation process.
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Incorporate AI completely:
Man versus machine has been a long-time debate, wherein people feel that heavy automation can replace human efforts and, thereby, run workers out of their jobs.
While it’s true that AI can make some roles redundant, the truth is that it actually leads to greater output and hence, a higher need for employment overall.
AI and human efforts should complement each other to optimise holistic operations. Additionally, if you integrate AI in a thorough and transparent manner, the positive spin-offs will be immense and sustainable.
Conclusion
Artificial Intelligence is transforming the world as we know it. It has a superbly wide canvas of potential applications, while its true capabilities are yet to be fully realised. A large number of businesses have been leveraging AI’s potential already to gain massive efficiencies, eliminate redundancy and introduce new elements of dynamism overall into the way they function.
It’s indisputable that coupled with modern and advanced communication platforms, AI can grant steady growth and utter dominance to a business operating in any market and industry.
While there are some issues and debates concerning this technology still, like that of AI ethics. However, we must keep asking the right questions and remain informed to head towards a future that features more ethical AI applications. Because whether we like it or not, this technology is here to stay.