With the world endeavouring to get back on an even keel as the pandemic slowly recedes, businesses are looking at the effects of the past two years on their own organisations’ health and considering the future. While some undoubtedly will need to rethink expansion plans, the need for enhanced connectivity remains top of the agenda, and for enterprises that largely means the Internet of Things (IoT), says Rohit Maheshwari, head of strategy & products at Subex.
Despite the impact of COVID-19, a survey by Gartner issued late last year shows that 47% of organisations will increase their investment in the IoT. According to a forecast from Transforma Insights, the total number of IoT connections is expected to grow from 9.4 billion in 2020 to 27.8 billion in 2030, with government, retail and wholesale, and transportation and storage sectors representing the greatest IoT revenue opportunities.
All this opportunity, all these new connections, together with the increasing rollout of 5G networks and the emergence of new business cases, means a vast increase in the amount of data that needs managing. Investing in new technology to create smarter operations is only sensible if the outcomes of those investments can be analysed and utilised to deliver better business results.
So how does the enterprise look to extrapolate the value of great connectivity? The Gartner survey also showed that by 2023, a third of companies that have implemented IoT will also have implemented artificial intelligence (AI) in conjunction with at least one IoT project. Those are clearly the organisations who understand the value of AI in unlocking insights and automating processes. In conjunction with machine learning (ML), this is how the data tsunami can be channelled into actionable enhancements.
Yet for many businesses, there are roadblocks in the way of this digital transformation. There can be a perception that sophisticated technologies like AI and ML are the prerogative of large organisations with deep pockets and relevant skilled personnel. Arguably that was the case when the early implementations emerged, but today AI and ML techniques can be available to all.
It’s true that the barriers to adoption can be daunting for the smaller enterprise. Many of their operational processes are still manual, making extracting and analysing information a lengthy and time-consuming process, open to error and misinterpretation. Bringing together data from different sources on to legacy systems creates complications and can deliver confusing outcomes. In addition, AI and ML have emerged with a certain mystery surrounding them – technologies only capable of being utilised by the experts. A perceived lack of skill sets will deter many enterprises from even considering AI and ML as suitable for their business.
Many enterprises are struggling to implement AI because they cannot clearly see the business value the science and sophistication of the technology is commonly highlighted rather than its importance to the business plans and goals. By demystifying AI and its capabilities, a wider range of business units can become involved to create highly relevant information that can directly and positively impact strategy and operations.
Automating data preparation, model building, deployment, and analysis gives faster and more accurate actionable insights, increases productivity and efficiency, and streamlines business processes from supply chain management to customer service interactions. Any enterprise can benefit from operationalising AI with the help of an AI orchestration platform.
Gartner’s research also mentions that only half of AI projects make it from pilot into production, and those that do take an average of nine months to do so. The urgency and criticality of leveraging AI for business transformation is driving the need for the operationalisation of AI platforms. This means moving AI projects from concept to production so that AI solutions can be used to solve enterprise-wide problems. In addition, 50% of enterprises will devise AI orchestration platforms to operationalise AI. AI orchestration platforms widen and democratise AI across the entire data value chain, allowing non-experts- i.e., most people to explore the possibilities and benefit from the potential.
An AI orchestration platform also has the capability to help enterprises operationalise AI, thereby enabling improved scalability and growth. Enabling technologies such as ML and AI assist with data preparation, model building and deployment, insight generation, and insight explanation to augment how enterprises explore and analyse data. The benefits of AI are well known to businesses today, and this is attested by the fact that by 2025, 70% of enterprises will have operationalised AI architectures due to the rapid maturity of the AI orchestration platform.
Businesses can use the platform to connect disparate and live data sources, identify relationships within the data, build AI models quickly, put them into production, and share the findings in understandable formats.
A powerful AI orchestration platform, such as Subex HyperSense, should be designed to provide automated data engineering, enabling ‘citizen data scientists’ to build complex AL models even if they have no in-depth training or skills in data programming or manipulation. With pre-built analytics use cases within the platform covering marketing, finance and technology, enterprises can achieve fast, impressive results without the need for specialist skills.
It is also essential to consider the responsibility and accountability that comes with implementing AI and ML applications they must be trustworthy, ethical and free from bias. There must be full transparency, and clear highlighting of both the short- and long-term benefits and impacts of AI within a defined boundary. There must be full assurance of data privacy and model security to ensure not only a smooth digital transformation within the enterprise’s data management processes, but a trusted one.
In the same way that the IoT has effectively democratised the ability for any enterprise to implement a business case based on connectivity, no-code AI orchestration platforms have exploded the myth that the power to leverage AI is restricted only to the experts. With the right AI orchestration platform, users with no coding knowledge at all can easily aggregate data from disparate sources, turn data into insights by building, interpreting and tuning AI models, and share their findings across the enterprise in formats that can be easily understood.
The massive potential of the technology is now available across all activities of the enterprise simplifying data discovery, delivering insights more rapidly and with greater accuracy, enabling more people to extract more value from their work and deliver more value to their customers. It’s time to put the power of the no-code AI orchestration platform in the hands of the people who need it.
The author is Rohit Maheshwari, head of strategy & products at Subex.
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