New research shows that nearly all IT leaders (93%) agree that “compared to five years ago, there is a greater expectation that IT leaders in my organization will minimize time to sales for AI-driven IT infrastructure.”
Business leaders are excited about the possibilities that AI can bring to their market shares and bottom lines. And more than ever, they’re leaning on their IT teams to deliver these AI-driven improvements.
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So you’re ready to walk into a manager’s office and explain what investments they should approve to make things happen while trying to manage expectations, explain why things might be moving slower than expected, and detail why implementing AI is more than just flipping a switch ?
That’s the challenge at the heart of Flexential’s latest research report, which reflects the views of 350 IT executives at organizations with annual revenues in excess of $100 million. Respondents are relatively optimistic about their AI plans, but acknowledge that AI cannot be scaled from the cloud at the push of a button.
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Planning for infrastructure and skills is needed, along with appropriate investment. Many data centers are not ready to handle the workload of AI, not to mention the additional security and privacy risks that come with AI.
But it’s not a case of IT leaders not being as excited about AI as their bosses—they are. Nearly three-quarters (73%) say they are excited about AI initiatives in their organization, and nearly half (49%) say they feel inspired. Only a minority of IT leaders report negative feelings such as nervousness (16%) or being overwhelmed (12%).
However, enthusiasm among IT leaders has not translated into full confidence in their organizations’ ability to execute on AI plans, the survey authors said. Just over a third of respondents (36%) described their organizations’ AI maturity as nascent or emerging, “suggesting that they may be catching up in terms of building their AI capabilities,” the authors said.
In addition, nearly half (46%) express some degree of doubt about their organizations’ ability to implement AI plans. Leveraging cloud services isn’t always the easiest route either, with 60% of organizations reportedly offloading AI workloads from the public cloud in the past 12 months, with 42% citing data privacy and security concerns. Another 38% said the main concern was improving general application performance.
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There is a great deal of elbow grease that must go into developing reliable and secure AI capabilities. Key priorities moving forward include the following:
- Increasing infrastructure investment to match larger AI-based workloads – 59%
- Investing in stronger cyber defenses for AI applications – 54%
- Custom development of AI applications and solutions – 52%
- Improving data center sustainability (e.g. carbon footprint) – 52%
- Hiring talent with AI experience and skills – 50%
The most common measures taken to address AI infrastructure gaps include offloading 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%) ).
AI pressure also increases skill requirements. More than half of respondents (53%) say they have difficulty finding people to take over the management of specialized computing infrastructure, such as high-density computing. Another 47% need more people to manage advanced network technologies such as SDN or NFV. 39 percent are looking for more data scientists or data engineers to help with their AI efforts. Only 9% report no staffing issues at this time.
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As mentioned above, business leaders are leaning heavily on their technology organizations to advance their organizations’ AI efforts. “AI is a boardroom conversation, and IT leaders are under increased scrutiny,” the survey authors said.
“AI investment is a top-down initiative in most organizations. More than half of respondents (53%) said the C-suite is one of the top three drivers for AI adoption, and nearly half (46%) named the board as the driving force.”
C-suite and board attention “could be a double-edged sword,” the survey authors added. “It means more support and probably more resources for AI initiatives, but also more scrutiny of AI-related investments.”