Multiple surveys and studies by consulting firms have confirmed that a majority of C-suite executives are resistant to use artificial intelligence (AI) in high-level decision making. While skepticism might be sound, early adopters have utilized enterprise AI to disrupt and alter their industry ecosystems in significant ways, from the development of new business models to the reimagination of supply chains. Against the backdrop of an accelerated digital transformation and expanding digital ecosystem, their impressive growth poses an important predicament:
Even as an imperfect technology, can C-suite executives afford to dismiss the utilization of enterprise AI?
At the heart of their rationale, enterprise AI remains an unproven technology, given that its results are built on inadequate data and can therefore be unreliable. While machine and deep learning models have helped AI evolve at break-neck speed, the lengthy process of data structuring, data inaccuracies and partial data feeding corrupt any final conclusions. These concerns are echoed by industry experts and international organizations who say that biased and incomplete data sets contribute to a number of risks including bias, false positives and other negative traits, as previously reported by MBN.
Even supposing an acceptable margin of error, any remaining confidence is diminished by a lack of both transparency and explainability. This is unacceptable to business leaders who carry the weight of multimillion-dollar decisions, enterprise continuity and people’s livelihoods. Nevertheless, as demonstrated by the digital transformation that came close on the heels of the global COVID-19-pandemic, hesitancy and aversion to incomplete innovations such as AI can also be detrimental.
Over the past two years, the digital economy expanded exponentially, consuming infrastructures, businesses processes and industries, thereby forcing companies to think in terms of digital first. In such a landscape, as demonstrated by the three previous industrial revolutions, early adopters of new technologies ultimately position themselves to secure their long permanence in the economy that is yet to fully emerge. At the forefront of the market are born-digital technology companies that are harnessing AI to completely rethink the way that business works to grow rapidly, while non-users fight to maintain market share despite AI.
The companies that have put AI at the core of their business have become market disruptors through the discovery of all-new business processes and commercial propositions that are often overlooked by the human eye. Such examples include automaker Tesla, which uses AI-powered systems and collaboration to continuously improve its manufacturing process and develop products at a groundbreaking speed. Continual end-point data analysis has effectively inverted the traditional value chain to start with consumers at the center, building products and services based on data patterns recognized by AI.
John Deere has become more than an agricultural manufacturer with AI, expanding its business model to offer smart-technology based services to boost farmers’ profitability. Its cloud-enabled JDLink System and complementary John Deere Operations Center will give farmers the ability to control, manage and monitor all their fixed capital assets in real time. From here, farmers will have the option to complement their data with ecosystem expertise services meant to discern what crop is best suited, as well as where and even when to plant. This holistic commercial approach in turn has the potential to change the emphasis of John Deere’s business model from manufacturing to profit-sharing agreements with farmers.
Mexican companies are not far behind in this respect, with a recent IBM study finding that 57 percent of businesses across various industries are already implementing and utilizing AI. Ambitious companies such as Ternium, a mining and manufacturing conglomerate, used AI to go beyond detecting system failures toward predictive maintenance, correcting anomalies even before they occur. Reported and observed results produced by AI have led companies to accelerate strategic ambitions to adopt AI technologies, a ripe market capturing the attention of institutes, companies and startups.
This includes enterprise AI software company C3 AI, which recently expanded to Guadalajara, Mexico, with a stated interest in fomenting the accelerated creation and production of AI applications across industrial sectors. With early entry, the company hopes to capture a rapidly growing global market projected to balloon from US$342 billion in 2021 to US$500 billion by 2024, according to the International Data Corporation. To support its ambitions the company is partnering with Tecnológico de Monterrey, advising the development of AI research programs as a means of sourcing talent and influencing the state’s thriving technology ecosystem.
“C3 AI is the type of visionary business that will push forward the whole Guadalajara tech ecosystem,” said Enrique Cortés Rello, Director of the Artificial Intelligence Hub, Tecnológico de Monterrey. “We expect our students and researchers to benefit from what we believe will be a very fruitful and deep interaction with C3 AI,” reads the press release.
This concerted effort points to the interest and prospective growth of Mexico’s AI enterprise market, a tendency Mexican AI startups hope will help them capture venture capital funding. Enterprise applications are diverse, attacking relevant market avenues from Logistics to Health, services and solutions that have raised millions in capital. Standing out among its peers is Nowports, a Mexican logistics startup that utilizes AI, Big Data and IoT, an advanced business model that allowed them to raise US$60 million in its Series B investment round.
Altogether, while AI remains an incomplete technology, applications are already proving to successfully undo traditional business processes and business models that have allowed their users to become market leaders. In other words, non-users will likely see themselves without little recourse than to explore AI applications in the near future, otherwise they risk becoming obsolete in a rapidly evolving digital economy. However, latecomers could possibly benefit from piggybacking off of proven AI technologies and services, while considering risk management as a means of minimizing the potential risks that may arise