Executive involvement in enterprise artificial intelligence (AI) initiatives is growing rapidly and more emphasis is being placed on high-quality training data.
Both C-suite ownership of AI and budgets over US$500,000 nearly doubled in 2020 due to the Covid-19 pandemic serving as a catalyst for accelerated AI initiatives.
A key lesson learned from the pandemic is that businesses need to be ready for anything that requires a high level of business agility. It’s Darwinism at its finest as businesses that can adapt to market trends faster than their competition can become market leaders and maintain that position. Those that can’t do this will fade into obscurity with many going away.
But how do business leaders even know what decisions to make? There is a massive amount of data to be analysed and people can’t process the information fast enough to find those key insights that drive business change.
Machines can work at infinitely higher speed and the pressure on the C-suite has never been higher. Now the execs are turning to AI to help them make the best decisions in as short a time as possible.
These findings come from Appen Limited’s annual State of AI and Machine Learning Report, which surveyed 374 business and technology decision-makers between April and May of this year. The report measured the state of AI for enterprises with more than 1,000 full-time employees and commercial organisations with fewer than 1,000 full-time employees.
C-suite involvement in AI takes a massive jump
The report uncovered an important change. The C-suite is now more engaged in AI initiatives than ever before, with a whopping 71 per cent of organisations reporting executive involvement. In comparison, only 39 per cent of executives owned AI initiatives in 2019.
CTOs made up 42 per cent of the 71 per cent of C-suite AI ownership, which partially explains why AI budgets are increasing in 2020. Covid-19 may be temporary but don’t expect AI to be. I believe it will be the biggest driver of business change since the rise of the Internet.
Executives see AI as invaluable to their business success. This is true for companies of all sizes across different industries. For 27 per cent of survey respondents, enterprise AI budgets have exceeded $1 million, while 10 per cent said their AI budget is more than $5 million. These numbers are expected to continue rising steeply as businesses adopt AI on a global scale.
C-level interest brings a focus on risk and ethics
With increased C-suite visibility, businesses are focusing more on risk management, governance, and ethics as key aspects of rolling out AI initiatives globally or to their full user base.
As companies start using AI to supplement human capabilities, responsible use of AI must be part of the process to ensure fairness, privacy, transparency, and security and avoid inappropriate uses of data.
Although businesses believe responsible AI is important to their success, only 25 per cent view unbiased AI as mission-critical. Half of the respondents either don’t see it as a major issue or are just starting to think about it. The findings indicate businesses must take a more proactive approach toward responsible AI. Simply having accurate data or algorithms isn’t enough. AI governance with clear ethical standards is also necessary.
Data management gets in the way of quality AI
Another big challenge for businesses is data management. Three out of four companies surveyed by Appen said they update their AI models at least quarterly. For 40 per cent of the respondents, lack of data or data management are the leading roadblocks to effectively utilising AI in the enterprise.
Most respondents (93 per cent) expressed the need for high-quality training data, as businesses continue to deal with more data types and complex data compared to previous years. The report revealed many businesses are still behind on AI adoption, especially when it comes to training data.
For this reason, company leaders are going beyond in-house resources and turning to third-party providers to help carry out AI deployments.
Data management and quality comes up in almost every AI discussion I have with business leaders. Companies have massive amounts of data — more than ever before and it continues to grow exponentially but much of the data resides in silos and is in a myriad of different formats. In data sciences, there’s an axiom that states “good data leads to good insights.”
The reverse holds true too, as bad data will lead to bad insights and partial data will lead to partial insights. Getting a handle on data and data quality needs to be as important as the AI initiatives themselves.
With AI, cloud is the way
In 2020, four times as many business and technology decision-makers reported using cloud machine learning providers, including Microsoft Azure (49 per cent), Google Cloud (36 per cent), IBM Watson (31 per cent), AWS (25 per cent), and Salesforce Einstein (17 per cent).
Each of these providers saw double-digit adoption of cloud machine learning tools in 2020 versus 2019, attributing the surge to businesses looking for solutions that can scale as their AI initiatives grow in complexity.
Despite dealing with a global pandemic, the majority (70 per cent) of businesses surveyed during those months did not expect Covid-19 to negatively impact AI strategies. In fact, nearly half of businesses have fast-tracked their AI strategies, with 20 per cent reporting significant acceleration. Only nine per cent of businesses anticipated substantial delays.
Increased C-suite investment in enterprise AI is an indication that businesses are choosing to spend money on key initiatives even in a time of crisis.
Clearly, these companies view AI-driven agility as the key to both short term survival and long-term leadership. With the right tools and strategies in place, businesses can access clean, high-quality, ethical data to successfully implement AI across the enterprise.