$15.7 trillion
That’s more than the annual output of Japan, Germany, India and Great Britain combined. Not surprisingly, PwC estimates that AI will power the global economy by 2030. It’s no secret that the value of intelligence has been steadily declining for years. In fact, in 2020, one-third of enterprises reported that the value of AI has decreased by up to 20% in almost all industries.
In 1965, Gordon Moore predicted that the number of transistors on a chip would double every two years, allowing for commensurate advances in computing power, data storage, and algorithmic efficiency. As a result of that prediction, the exponential growth of the cloud and the pay-as-you-go model now means that even smaller organizations now have access to very large-scale computing infrastructure at a relatively low cost. This eliminated the need for large upfront investments in computing infrastructure and enabled smaller organizations to compete on an equal footing with larger ones.
Moreover, the explosion of data has played a crucial role in reducing the value 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 data sets, leading to improved accuracy and performance. In addition, the open source movement has enabled developers to freely access and use large data sets, lowering barriers to entry for the development of intelligent systems. Finally, advances in algorithmic efficiency have also helped reduce 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 computing resources, reducing development and deployment costs.
In an era where AI and ML technologies are ubiquitous, we can expect to see significant changes in how businesses operate and innovate across industries. For example, agile startups in fintech are using AI for everything from STP for customer KYC and immigration to financial and budgeting insights. And in healthcare, it enables small tech startups to predict patient symptoms using data from wearable devices and deliver timely emergency services.
Building a connected business is essential
Connected businesses are in a much better position to take advantage of the falling price of intelligence than their traditional counterparts. That’s because connected businesses use digital technologies to connect with customers, employees, suppliers and partners in real time. They also take a cloud-first approach to infrastructure, helping them easily process large volumes of data from mobile devices, social media and other tools to streamline processes and gain insights into customer behavior. Most affiliate businesses are actually built on three main pillars:
Enhanced human potential. Often connected enterprises host a culture of innovation, agility and collaboration. A high degree of automation and eventual digitization means that employees are freed from the tyranny of repetitive manual tasks and have more time for creative problem-solving and high-quality work. In fact, having the digital infrastructure to support a culture of innovation is as important as the culture itself.
Value networks. Connected enterprise leaders are realizing that the linear supply chain has outlived its usefulness and are instead investing in ecosystems of technology providers, aggregators, distributors and start-ups. Low-latency connections within these ecosystems, or value networks, mean that every stakeholder has access to a real-time flow of information to make decisions, optimize processes, and accelerate product delivery. A prime example is how auto insurers are partnering with manufacturers and telcos to launch pay-as-you-go models where policyholders are charged a lower premium if they consistently drive safely. At the same time, the information collected by the aircraft’s teleconnections helps emergency responders quickly track down the crash site, while providing critical data to manufacturers so they can optimize safety components.
Cognitive activities. In today’s era, a “culture of innovation” is only as good as the data that fuels it. Connected enterprises are more decentralized and flexible than traditional organizations, with distributed teams and a focus on outcomes rather than process. Agile methodologies, AI-based processes that require very little human intervention, and a high degree of internal connectivity are hallmarks of successful connected enterprises. This means data flows seamlessly throughout the system, and stakeholders can instantly access information critical to their work without the barriers often created by congested operations.
What does real-world impact entail?
With a presence in more than 20 countries in Asia, the Middle East and Africa, the fast-growing FMCG company sought to strengthen its position in 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. In particular, the company found it difficult to gain visibility into demand and increase its market share due to heavy reliance on manual processes. In terms of the solution, using an AI-powered platform to automate operations and map critical operational data was the first step. Next involved creating a dashboard for their sales reps and territory managers that helped them map global penetration, identify territory gaps and build a strategy for effective outlet coverage. In just a few months, they saw a 15% increase in cost per size, a 15% improvement in sales rep productivity, and a 50% increase in ECO.
Similarly, when the pandemic was in full swing, the CPG company tracked the spread of COVID in different neighborhoods and fed that information into an AI platform to predict which retail locations would be hit hardest by stockouts. Using these insights combined with a network of digitally connected distributors, they were able to restock their products within days, while competing brands were off the shelves.
The ethics of reason and agility
These stories show how small organizations that embrace the AI ​​tools and talent available to them can create an impact that larger, more traditional enterprises would struggle to replicate. To stay agile in a world where every organization has access to the latest intelligence and analytics tools, transforming into a connected enterprise is clearly essential.
But in addition to creating more economic value, a clear opportunity for businesses to stand out among their peers is to adhere to the ethical use of AI. This not only translates to using the technology to further environmental and social agendas, but also means ensuring that their AI models are culturally sensitive, unbiased towards minority views and used in accordance with privacy regulations. As AI becomes more entrenched in how businesses operate, workforce mobility is also a major concern; one that leaders can address through promotion programs and effective change management.