AI, Blockchain, and machine learning are enabling extensive intellectual, secure, and personalised interaction with every user - be it a customer or an employee.
The entire enterprise landscape has undergone innumerable revolutions over the recent years, thanks to three critical technologies - artificial intelligence, machine learning, and blockchain. Of course, the list of influential technologies is much longer, but these three miracle innovations are special because they have enabled extensive intellectual, secure, and personalised interaction with every user - be it a customer or an employee.
The most significant achievements of these technologies include:
- Simplifying even the most complex transactions
- Making data accessible anytime from anywhere across any device
- Transforming technology from a supportive role to change-driving leaders
- Empowering technology through cognitive computing
Let's look at each of the three technologies in detail to assess their contributions to the enterprise landscape.
AI: Driving disruption the smart way
The scope of AI applications within enterprises is expanding by the second! In fact, as per Gartner's predictions, by 2021, about 40 percent of newly introduced enterprise apps will have AI as their driving feature. Most of today's regular and repetitive enterprise operations are being managed by AIs because of their ability to act cognitively, independently, and rapidly. Feeding machines with pattern-based data that they can learn, interpret, and apply is what enables AI to be so brilliant. There are many examples, but our ever-popular use cases are chatbots.
Bots are the products of conversational AI technology. These conversational buddies have evolved seamlessly to work on routine transactions with ease. They provide instructions intuitively, perform pattern studies to make smart suggestions, and act as efficient assistants. The best chatbots deliver an experience (CX) in which the users cannot tell if they are interacting with a human or a computer. This experience is being delivered through messaging channels leveraging platforms like Facebook, Slack, Skype, Teams…the list goes on.
Bots include two critical components: the bot server and the NLP AI. The bot server applies logic to the user's inquiry and is also connected to the backend enterprise systems. The NLP AI supports the bot in interpreting the user's request and connecting the request to the corresponding information in the backend server.
- In the Human Capital Management (HCM) scenario, bots are the go-to experts for routine questions about pending leaves, payroll inquiries, and other queries. The chat-like personalised interface ensures a positive and quick interaction, with the added security of confidentiality. Bots also help in planning leaves, scheduling official trips, and tracking, as well as prompting, approvals. Bots are equally useful in creating an engaging, conversational, and trust-driven interface for conducting surveys.
- In aviation, part procurement is the key determiner of operational efficiency. Bots support efficient part management by maintaining a healthy part inventory, auto-triggering purchase orders, selecting the ideal vendor, and promptly handling AOG situations and technician inquiries about parts, repair status, or suggested solutions.
- In logistics, bots enable frictionless computing by proactively delivering information about consignment status.
- Bots have taken over the first line of customer service engagement in most verticals, reducing the customers’ waiting time and drastically improving customer retention.
The next wave of innovation in AI is leveraging voice messaging platforms like Google Assistant, Alexa, Cortana, and many more. With evolving applications digging deeper into the AI mine, we will definitely be seeing much more of this intelligent technology in the days to come.
Machine learning: the smart precursors of AI
Machine learning is a crucial element of AI, but this technology has created a niche for itself in the not-so-routine, yet pattern-based, activities within enterprises. Data is the core nourishment of machine learning, and it thrives on the innumerable packets of clean data that it is fed. Of course, some level of initial handholding is required to ensure that the right data is fed and interpreted so that the desired data insights are delivered. Hence, machine learning finds incredible applications in niche areas within specific verticals.
- In logistics, machine learning supports in-memory optimisation and last-mile planning, which simplifies the dreaded task of freight planning. The in-memory optimisation engine works within a flexible framework, assessing multiple real-time data before delivering the final fleet plan suggestions, including driver and vehicle availability, nature of the consignment, place of delivery, warehousing capability, route options, the condition of the vehicle, and driver performance. The engine then immediately provides smart suggestions of not just the route, but also the vehicle, driver, and the load distribution.
- In HCM, machine learning can help in studying the traits and characteristics that best performers within an organisation display. These traits are grouped into categories of position, competencies, and performance indicators. Each category is studied in detail to derive a consistent pattern of traits and characteristics of an ideal candidate for a given role.
- Biometric attendance systems use the concept of zero UI by applying facial recognition algorithms to clock in and out times of employees. This approach simplifies the entire time authentication process with zero interaction, enabling compliance and accurately clocking in and out times.
- In a workshop scenario, this facial/object recognition ML Models will help in monitoring and alerting any safety non-compliances.
- In Enterprise Asset Management (EAM), machine learning finds extensive application in the real-time monitoring of critical equipment parameters, triggering automated alerts when a combination of these parameters shift beyond the threshold value.
Blockchain: delivering security and transparency to core tech
No matter what the technology, safety and operational transparency are key concerns that stop decision-makers from embracing new technologies. Blockchain allays all these fears since it simplifies operations by making them safer and visible to all relevant stakeholders.
Blockchain minimises instances of fraud and errors, since a common ledger is shared across all stakeholders. Real-time visibility of every transaction reduces the risk of mismanagement and ensures a single version of the truth. This approach not just enhances the speed of operations but also eliminates unnecessary cost elements. With automated audit options and authentication locks limiting every scope of error, this decentralised approach has immense potential in enabling seamless supply chain operations.
With applications across all verticals, blockchain has innumerable capabilities, some of which include:
- Logistics - in terms of tracking shipments in real time, measuring specific consignment parameters in transit, recording every transaction between origin and destination, permanently storing all documentation associated with the shipment with clear permission-based access, and a single version of data that can be easily validated.
- Aviation – tracking and tracing parts availability and genuineness, which would help during critical times such as AOG situations. Some aviation parts have a life limit and that could be tracked using Blockchain applications.
- HCM – Investors are showing interest in companies dealing with Blockchain business applications that would help in removing few inefficiencies such as background checks by creating trust using proper validation mechanisms with Blockchain fundamentals.
- In LSP machines and tools marketplace, Blockchain can enable the formation of a well-integrated consortium of manufacturers, resellers, and LSPs. This can simplify the buying and selling of tools in the marketplace with minimal scope for forgery or error. Every transaction, from bids to settlements, in the consortium, is entirely transparent to every user. Performance and usage conditions can also be easily tracked through sensors mapping specific parameters.
The network of trust that the Blockchain technology creates empowers every stakeholder to contribute their best with confidence.
The confluence of 3 smart technologies
AI, machine learning, and Blockchain can catalyse spectacular business opportunities, especially when they work together. With machine learning sharpening AI skill sets, AI delivering cognitive and intellectual capabilities to the machine, and Blockchain ensuring secure and transparent computing, this technology trio can definitely work magic in terms of deploying meaningful solutions across the enterprise landscape.
We have a long way to go before we can entirely explore all the possible capabilities of these technologies. But the journey towards intellectual computing has certainly begun.
Where are you in this journey?
(Disclaimer: The views and opinions expressed in this article are those of the author and do not necessarily reflect the views of YourStory.)