$15.7 Trillion.
That’s more than the combined annual output of Japan, Germany, India, and the UK, combined. Unsurprisingly it’s also what PwC estimates that AI will contribute to the global economy by 2030. It’s no secret that the cost of intelligence has been falling steadily for years. In fact, in 2020, a third of enterprises reported that the cost of AI decreased by as much as 20% across nearly every industry.
In 1965, Gordon Moore predicted that the number of transistors on a chip would double every two years, allowing for commensurate advancements in computing power, data storage, and algorithmic efficiency. On the heels of that prediction, the near exponential growth of the cloud and the pay-as-you-go model, now means that even smaller organizations now have access to highly scalable computing infrastructure at relatively low cost. This has removed the need for large up-front investments in computing infrastructure and has made it possible for smaller organizations to compete with larger ones on an equal footing.
Moreover, the explosion of data has played a crucial role in the reduction of the cost of intelligence. With the growth of the internet and the proliferation of sensors, there is now an abundance of data available for analysis. This has allowed machine learning algorithms to be trained on large datasets, leading to improved accuracy and performance. In addition, the open-source movement has made it possible for developers to access and use large datasets for free, lowering the barriers to entry for developing intelligent systems. Finally, advances in algorithmic efficiency have also contributed to the reduction of the cost of intelligence. Researchers have developed new techniques for training and optimizing machine learning algorithms, resulting in faster and more accurate models. This has made it possible to develop intelligent systems with fewer computational resources, reducing the cost of development and deployment.
In an era where AI and ML technologies are ubiquitous, we can expect to see significant changes in how enterprises operate and innovate across industries. In fintech, for example, agile startups are using AI to deliver everything from STP for customer KYC and on-boarding to financial and budgetary insights. And in healthcare, it’s enabling small tech start-ups to predict patient symptoms via inputs from wearables, and deliver timely emergency services.
Building A Connected Enterprise Is Critical
Connected enterprises are much better positioned to take advantage of the falling cost of intelligence than their traditional counterparts. Part of the reason is that connected enterprises use digital technology to connect with customers, employees, suppliers, and partners in real-time. They also take a cloud-first approach to infrastructure, helping them easily process high volumes of data from mobile devices, social media, and other tools to streamline processes and gain insights into customer behavior. Most connected enterprises in fact, are built on three major pillars.
Amplified Human Potential: Often, connected enterprises play host to culture of innovation, agility and collaboration. The high degree of automation and end-to-end digitalization means that employees are emancipated from the tyranny of repetitive manual tasks, and have more time for creative problem-solving and higher order work. In fact, having the digital infrastructure to support innovation culture is just as important as building the culture itself.
Value Networks: Leaders within connected enterprises understand that the linear supply chain has outlived its usefulness, and are instead investing in ecosystems of technology providers, aggregators, distributors, and startups. Low-latency connections within these ecosystems, or value networks, means that every stakeholder has access to a stream of real-time information to fuel decision-making, optimize processes, and accelerate product delivery. A solid example lies in how auto insurers have partnered with manufactures and telematics companies to launch pay-as-you-drive models, where policyholders are charged a lower premium if they consistently exhibit safe driving behaviour. At the same time, the information collected by the onboard telematics helps emergency responders quickly track the scene of an accident, while feeding critical data back to manufacturers so they can optimize safety components.
Cognitive Operations: In today’s age, a ‘culture of innovation’ is only as good as the data that feeds it. Connected enterprises are more decentralized and flexible that traditional organizations, with distributed teams and a focus on results rather than process. Agile methodologies, AI-driven processes that require very little human intervention, and a high-degree of internal connectivity are hallmarks of successful connected enterprises. This means that data flows seamlessly across the entire system, and stakeholders can instantly access information that is critical to their work without the bottlenecks that siloed operations often create.
What Does The Real-World Impact Entail?
With a presence in over 20 countries across Asia, the Middle East, and Africa, a fast-growing FMCG company was looking to cement its position across multiple geographies. However, despite its success, the company struggled to tap into its full regional sales potential due to the fragmented retail landscape in emerging markets. Specifically, the company found it challenging to gain visibility into demand and increase its market share, due to a heavy reliance on manual processes. In terms of a solution, using an AI-powered platform to automate operations and map out critical operational data was the first step. The next involved creating a dashboard for their sales representatives and territory managers that helped them map geo-penetration, identify territory gaps, and build a strategy for effective outlet coverage. In just a few months, they saw a 15% increase in value per size, 15% improvement in sales rep productivity, and a 50% jump in ECO.
Similarly, when the pandemic was in full swing, a CPG company tracked the spread of COVID across different neighbourhoods, and fed that information into an AI platform to predict which retail locations would be hit hardest by stock-outs. By using these insights, coupled with a digitally connected distributor network, they were able to restock their products in a matter of a couple of days, while shelves lay bare of competitor brands.
The Ethics And Agility Of Intelligence
These stories illustrate how small organizations that embrace the AI tools and talents available to them, are able to create an impact that larger, more traditional enterprises would struggle to replicate. To stay resilient in a world where every organization has access to cutting-edge intelligence and analysis tools, turning into a connected enterprise is clearly essential.
But besides creating more economic value, a clear opportunity for enterprises to stand out amongst their peers is to commit to the ethical use of AI. Not only does this translate into using the technology to further environmental and social agendas, but it means ensuring that their AI models are culturally sensitive, unbiased toward minority perspectives, and are used in compliance with privacy regulations. As AI becomes further entrenched into enterprise operations, workforce displacement is also a key concern – one that leaders can address via upskilling programs and effective change management.
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