Due to the increasing involvement of state players in automation warfare, when AI-driven automation is on its way to becoming a war weapon, what will it mean for an enterprise to stay competitive for survival?
Artificial intelligence is redefining the very meaning of being an enterprise. The rapidly advancing artificial intelligence (AI) capability is on its way to revolutionizing every aspect of an enterprise. The ability to access data has leveled the playing field and brought every enterprise a unique possibility of progress. What needs to be seen is in this level playing field, which enterprises will be able to compete and lay a new foundation for fundamental transformation and which ones will decline.
Disclosure: I am the CEO of Risk Group LLC.
Risk Group discusses “The Future of Enterprise AI” with Ankur Dinesh Garg, chief of artificial intelligence at Hotify Inc., board member and chief of artificial intelligence at Sonasoft, a board member at Iamwire, advisor to many companies and member at Forbes Technology Council based in the United States.
Purpose of Enterprise AI
Enterprises across industries are undergoing a profound and lasting shift in the relative balance of AI adoption. AI application will offer each enterprise as many opportunities as it does challenges. While access to technology, data, and information is common to all enterprises, what is not common is how each enterprise uses that information—and for what reason. While AI has given enterprises across industries and nations the same starting point in access to AI technology, it is crucial to understand the parameters that will define their individual and collective success.
There are many variables in each enterprise ecosystem that will determine whether an enterprise will be able to use the data and information from its ecosystem to develop AI, automate, and transform to succeed. Ankur Garg expands on this notion on Risk Roundup: “All the enterprises are running the race of AI, and who is going to win largely depends on many crucial elements. For example, how accurately enterprise leaders can articulate the problem that they are facing, and the business impact and the value associated with the problem.”
As the state of AI deployment accelerates, it is difficult to grasp what staying competitive means for an enterprise’s survival. It is an understatement that enterprises across nations are expected to face extraordinary challenges and changes in the coming years, with automation driven growth as the only constant in those changes. As a result, it is vital to understand what does AI-driven growth means for enterprises.
The emerging trends in AI-driven automation reflect significant shifts of players and actions in the AI sphere that reveal the reconfigurations of ideas, interests, influence, and investments in the AI domain of enterprise adoption and transformation. Enterprises are beginning to understand the consequences of the evolving artificial intelligence-driven automation ecosystem far beyond narrow artificial intelligence, crossing economic, commerce, education, governance, and trade supply chains. While the relationship between enterprises and automation is complicated, and at times indirect, the force and pace of AI-driven automation change expected in the coming years will present each enterprise challenges and opportunities for its: products, services, processes, operations, and supply chains. From what it seems, the AI applications of tomorrow will be hybrid systems composed of several components and reliant on many different data sets, methodologies, and models.
The growing layers of cyberspace are connecting humans and machines across cyberspace, aquaspace, geospace, and space (CAGS). It is not only the human users that are getting connected, but the growing number of internet of things (IoT) devices are also getting active and operational with the rollout of 5G. Individually and collectively, the ever-increasing connectivity of man and machines, living and non-living, is creating enormous amounts of data and is driving the rapid expansion of AI across enterprises.
However, so far, there was not enough processing power for enterprises to implement ideal AI techniques. While the AI-driven automation emerged a few years ago, it is only now maturing as cloud computing, and massively parallel processing systems advance AI implementation further. As a result, AI-driven automation adoption is now progressing further as an essential trend.
There are many functional parts of enterprises that are already benefiting from the AI transformation. From R&D projects, customer service, finance, accounting, and IT, there are rapid shifts from experimental to applied AI technology across enterprises. There is no doubt that each enterprise will benefit from intelligent decision making to streamlined supply chains, customer relations to recruitment practices. At the same time, AI-driven automation is on its way to becoming a war weapon, as shown by the increasing involvement of state players in automation warfare. This is aimed at crippling AI competition and is progressing rapidly despite the growing complexities and challenges.
As Enterprise AI demand grows, so does the rise of AI-as-a-service. Moreover, AI-driven automation, data analytics, and low-code platforms are converging as AI fundamentally shifts the competitive landscape. New organizational capabilities are becoming critical, and so is the need to effectively manage the growing security risks of dual-use of AI.
When common sense tasks become more straightforward for computers to process, AI-driven intelligent applications and robots will become extremely useful in enterprise operations and supply chains. While a limited understanding of use cases — what problems can be solved using AI, where to apply AI, what data sets to use, how to get credible data and skilled resources — still slows down AI adoption, company culture also plays a vital role in AI adoption strategies and is proving to be a barrier to AI adoption.
Enterprise Digital Data Infrastructure
While enterprises are taking advantage of AI and are beginning to harness these technologies and benefits, the AI growth for any industry is driven and shaped by several variables and external factors, many of which can be amplified or influenced by data choices made at the enterprise or industry level. So, how will availability, affordability, accessibility, and integrity of data impact potential AI growth for enterprises across nations?
As seen, many enterprises lack the necessary digital data infrastructure. The lack of digital support, in turn, discourages opportunities and innovations in AI, making it challenging to address enterprise needs adequately — leaving each of its enterprises with outdated data, information, and intelligence. Moreover, the credibility of the data sets also is an emerging concern. That brings us to two important questions: how are enterprises addressing digital data infrastructure challenges? What are the different data types that are important for enterprises?
While enterprises are currently using AI in areas for which they already have some data and analytics in place, many meaningful data partnerships are emerging. The emerging integrated structured data and text, when available to train AI systems, will bring necessary progress in enterprise AI. It will be interesting to see how this new data-driven world reality brings each enterprise across industries, both opportunities, and risks.
The potential of Enterprise AI can transform the enterprise ecosystem in many ways. From decision making to supply chain intelligence and tracking capabilities to the automation of business processes, AI can change the entire enterprise ecosystem across CAGS. The time is now to understand its risks and rewards.
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