The status of AI adoption within enterprises, where AI bears nearly immeasurable benefit in a variety of use cases — such as productive and hyper-efficient IT, supply chain automation, and increasingly intelligent cybersecurity ecosystems — is a mixed bag. Companies can begin to reap the benefits of a truly game-changing technology by addressing the fear, uncertainty, and doubt surrounding AI.
In a report- AI is Set to Accelerate… Is Your Organization Ready? Based on a survey done by Juniper of 700 IT professionals from around the world, 95% feel their companies would benefit from incorporating AI into daily operations, products, and services, and 88 percent want to employ AI as much as feasible. However, only 6% of C-level executives who responded to the study said their organization has implemented AI-powered solutions. In moving forward with AI, the C-suite faces a variety of hurdles, some technical and some organizational.
According to IDC’s 2021 “Worldwide Semiannual Artificial Intelligence Tracker“, the global AI market, which includes software, hardware, and services, will grow 16.4% this year to US$327.5 billion dollars, and will surpass US$500 billion by 2024. Enterprises will account for a sizable portion of that expansion. As a result, it’s apparent that widespread AI adoption within businesses isn’t a question of if, but rather of when.
Why, then, is it so difficult to implement AI and make it stick? An AI implementation strategy has a lot of moving components, and some businesses may feel overwhelmed by what appear to be multiple barriers to adoption. However, riding the AI wave does not have to be difficult. It’s a lot easier to get started with AI if organizations can ask and answer four basic questions.
Being intentional and focused
AI is just too vast and vital to be taken lightly. Companies must be very intentional about AI; they must sufficiently fund it, put some of their finest people to it, and acknowledge that the road ahead will be difficult.
CIOs have a significant role to play, but they can’t do it alone because so many of the AI issues are beyond their control. If a critical mass of two or three top executives, including the CEO, personally commit and drive the rest of the organization toward AI as a crucial piece of its future, it will help tremendously.
Addressing the data challenge
Coming to terms with all the integration problems and technological improvements required for AI-ready, cloud-based infrastructure stacks is one of the most important barriers to AI adoption.
According to IDC’s “AI StrategiesView 2020: Executive Summary”, businesses often spend around one third of their AI lifecycle time on data integration and data preparation vs. actual data science efforts, which is a big inhibitor to scaling AI adoption.”
In many ways, AI inherits the data and analytics challenges that businesses faced before the term Artificial Intelligence was coined. Layering AI on top of such difficulties can be difficult because many organizations haven’t addressed them yet.
Companies must recognize that they will require the appropriate infrastructure to centralize and expedite the effort of getting all of this data in AI-ready condition, without compromising the insight-generating data science that each function may have undertaken independently. Fortunately, technology exists to make this easier.
The impact on the people
Apart from technological concerns, businesses must ensure that they have a workforce with the necessary skills to support AI. Businesses must first address the concern that plagues everyone’s mind when it comes to AI: Will it eliminate jobs?
This is sometimes portrayed as a “either/or” situation — either machines or humans have work — but the reality is significantly more nuanced.
Many IT teams are made up of bright minds and problem solvers who are frequently drawn into the quagmire of tedious, routine tasks. Their energies can be unlocked thanks to automation. As a result, AI’s greatest benefit isn’t always limited to making IT workers’ lives easier. It’s all about unlocking the full potential of every person by eliminating monotonous chores and solving challenges that humans can’t tackle at scale.
Security and governance
For AI to be successfully deployed, cross-functional and executive involvement in the supervision of operational, reputational, and financial risk associated with AI is critical. Bias in data must be reduced if AI is to be trustworthy. Whatever a business does with AI must adhere to its own set of business and ethical guidelines. It must also adhere to an ever-increasing number of governmental regulations.
Another major concern is security, which is complicated by AI models. Source code repositories are safeguarded in typical software development. However, the data used in AI models is not part of that ecosystem. To account for the uniqueness of AI development, companies must widen their security plans and processes