Overcoming Challenges in Leveraging AI for Significant Commercial Gains - Insights From ZDNet

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New research shows nearly all IT leaders (93%) agree that “compared to five years ago, there’s a greater expectation that IT leaders in my organization minimize time-to-revenue for AI-driven IT infrastructure.”

Business leaders are excited about the possibilities AI can deliver to their market shares and bottom lines. And they are leaning more heavily than ever on their IT teams to secure these AI-led boosts.

Also: 5 ways CIOs can manage the business demand for generative AI

So, are you ready to walk into an executive’s office and explain what investments they should be approving to make things happen while trying to manage expectations, explain why things may progress slower than expected, and detail why implementing AI is more than simply flipping a switch?

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That’s the challenge underlying Flexential’s latest survey report , reflecting the views of 350 IT leaders at organizations with more than $100 million in annual revenue. Respondents are relatively optimistic about their AI plans but recognize that AI can’t be scaled up from the cloud at the touch of a key.

Also: How your business can best exploit AI: Tell your board these 4 things

Infrastructure and skills planning , along with appropriate investments, are needed. Many data centers aren’t ready to handle AI loads, not to mention the added security and privacy risks that come with AI.

However, it’s not a case of IT leaders not being as enthusiastic as their bosses about AI – they are. Nearly three-quarters (73%) say they’re excited about AI initiatives in their organization, and almost half (49%) say they feel inspired. Only a minority of IT leaders cite negative feelings like nervousness (16%) or being overwhelmed (12%).

However, enthusiasm among IT leaders hasn’t translated into full-fledged confidence in their organizations’ ability to execute AI plans , the survey’s authors reported. Just over a third of respondents (36%) flagged their organizations’ AI maturity as nascent or emerging, “indicating they may be playing catch-up when it comes to building out their AI capabilities,” the authors stated.

In addition, close to half (46%) express some level of doubt in their organizations’ ability to execute AI roadmaps. Tapping into cloud services isn’t always the simplest route, either – 60% of organizations have reportedly pulled an AI workload back from public cloud over the past 12 months, with 42% citing data privacy and security concerns. Another 38% said the main issue was improving general application performance.

Also: AI-powered ‘narrative attacks’ a growing threat: 3 defense strategies for business leaders

There’s a great deal of elbow grease that needs to go into developing reliable and secure AI capabilities. Top priorities for moving forward include the following:

Increasing infrastructure investments to account for more AI-driven workloads - 59%
Investing in stronger cybersecurity protections for AI applications - 54%
Developing AI applications and solutions in-house - 52%
Improving data center sustainability (e.g. carbon footprint) - 52%
Hiring talent with AI experience and skills - 50%

The most prevalent actions taken to address AI infrastructure shortfalls include offloading workloads to 5G or IoT networks , cited by 54% of respondents, using third-party colocation data centers to process data closer to the edge of the network (51%), and using network function virtualization (45%).

The push for AI is upending skills requirements as well. More than half of respondents (53%) report having difficulties finding individuals who can assume management of specialized computing infrastructure, such as high-density computing. Another 47% need more people to manage advanced networking technologies, such as SDN or NFV. Thirty-nine percent seek more data scientists or data engineers to assist with their AI efforts. Only 9% report no staffing issues at this time.

Also: When’s the right time to invest in AI? 4 ways to help you decide

As mentioned above, business leaders are leaning heavily on their technology organizations to advance their organizations’ AI efforts. “AI is a board-level conversation, and IT leaders are under increased scrutiny,” the survey’s authors stated.

“AI investments are a top-down initiative at most organizations. Over half of respondents (53%) said the C-suite was one of the top three driving forces behind AI adoption, and almost half (46%) identified the board as a driving force.”

C-suite and board attention “could prove a double-edged sword,” the survey’s authors added. “It means more support, and likely more resources, for AI initiatives, but more scrutiny on AI-related investments as well.”