Presented by Outshift by Cisco
The inaugural Cisco AI Readiness Index found that in the past six months, a massive 97% of senior business leaders feel increased pressure to deploy AI-powered technologies. Most of that urgency is coming from the top, along with the Board of Directors. Unfortunately, the ambitious demands most often exceed the internal ability to make it happen. A full 86% of companies are not prepared to leverage AI to its fullest potential, no matter how badly they want to harness it – and how high-stakes an effective AI strategy has become.
The biggest issue is that the sudden democratization of generative AI has left companies in the lurch, in terms of talent, knowledge gaps and insufficient compute, says Shubha Pant, vice president, AI/ML at Outshift by Cisco. With the emergence of gen AI, the potential and the surface area for AI transformation has increased exponentially.
The biggest issue is that the sudden democratization of generative AI has left companies in the lurch, in terms of talent, knowledge gaps and insufficient compute.
“Previously, AI usage and innovation was concentrated within specific teams and products in the organization, particularly those that were technologically advanced,” she explains. “However, gen AI has completely altered this paradigm. Now new tools, platforms, and techniques are surfacing every day, which anyone within the organization can leverage to enhance their productivity and build new experiences,. The opportunities are vast and it’s an exciting time.”
However, many leaders are still in the process of identifying what this shift means for their businesses, trying to understand how this will reshape their operations, strategies and overall business landscape.
“It’s not just about integrating AI into our systems,” Pant says. “It’s about fostering an AI-ready culture, one where everyone in the organization feels confident and capable of using these tools to their advantage. That’s the real game-changer.”
The Cisco AI Readiness Index
“In the face of the rapid AI evolution, leaders should stay abreast of the latest developments and trends,” Pant says. “AI is a powerful tool, but it’s up to them to harness its potential effectively.”
However, Cisco’s recent research highlights a profound gap between the accelerating pace of AI development and how prepared organizations are to adopt it. The Index, which surveyed over 8,000 global companies, revealed critical gaps across key business pillars and infrastructures – and half of those surveyed admit serious concerns around the business impact if they fail to act within the next year.
Half of those surveyed admit serious concerns around the business impact if they fail to act within the next year.
While a full 95% of respondents say they already have a highly-defined AI strategy, 41% are lacking defined metrics for measuring impact, while only 45% have a long-term funding plan for their AI strategy.
The Index investigates AI readiness across six key pillars:
- Strategy
- Infrastructure
- Data
- Governance
- Talent
- Culture.
It also categorizes organizational readiness into four levels:
- Pacesetters (fully prepared): 14% of companies
- Chasers (moderately prepared): 34% of companies
- Followers (limited preparedness): 48% of companies
- Laggards (unprepared): 4% of companies
Pant shares an inside look at the Cisco Readiness Index to highlight the most critical vulnerabilities, as well as strategies that are crucial to organizational success in adopting AI, both in the immediate present and as the technology quickly evolves.
Why talent and culture are first in line
Generative AI doesn’t replace human intelligence and creativity, but augments it, truly democratizing innovation – if it’s universally embraced in the business. Without the top-down support of organizational leaders, it becomes an expensive experiment that won’t reach its full potential. Gen AI is also too complex, with too many moving parts, to be successfully integrated into an organization without human expertise and oversight.
Right now, 29% of respondents feel they’re very well-resourced and close to half say they’re moderately well-resourced, while 24% believe they’re under-resourced or unsure. The primary reason cited for under-resourcing is the difficulty in finding qualified AI professionals in the market.
Without the top-down support of organizational leaders, it becomes an expensive experiment that won’t reach its full potential.
But 72% say their employees have sufficient skills to use AI tools competently in their day-to-day roles – but not to leverage the full potential of these through more advanced practices. Because of this, 90% are investing in training for employees in this area. This isn’t just great for the business – it’s also a morale-lifter for employees who fear they’ll be replaced as the scope of some jobs will undoubtedly be impacted. But the study found that there’s still a big gap in AI receptiveness between leadership and middle management, and the employees who are facing big changes to their day-to-day work lives.
“It’s critical to foster a culture where no matter which function people belong to, they’re able to quickly adapt, learn, change and leverage new AI tools,” Pant says. “That will require some effort, both from the top down and the bottom up. This can be done by hiring some experts to infuse the knowledge and training their current staff across the organization. And considering the pace of innovation, continuous training and learning are needed.”
Turning strategy into action
In the Strategy category, about a third of business leaders were classified as Pacesetters, which is also the highest number of Pacesetters in of any of the six pillars. In these companies, management teams and boards are investing a great deal of time and effort in building a roadmap for AI success.
Many scored highly because of their well-defined deployment strategies, clear ownership, impact measurement processes and a steady stream of funding, and have even started to deploy AI across the org, notably in infrastructure and cybersecurity. But what’s particularly surprising in these findings is the significant emphasis on strategy, Pant says.
While impressive strategies are a great start, they need to be supported by real investments.
“My hope is that this will translate into tangible investments, rather than just empty discourse – a true commitment to AI, with a top-down reinforcement in place, rather than just a peripheral project,” she explains. “Looking ahead, I believe a considerable portion of next year’s budgets should be allocated towards this area, because while impressive strategies are a great start, they need to be supported by real investments. It will be interesting to see if the focus on strategy we’re observing now will materialize into real products in the coming year.”
The new infrastructure challenge
With powerful new technology comes the demand for exponentially powerful infrastructure, which includes high-performance CPUs and GPUs for improved processing and performance, AI-focused software platforms, automation tools, high-bandwidth ethernet, data storage and management solutions as well as heightened cybersecurity.
The study found that 95% of respondents are aware that complex workloads are cresting their IT horizon, but this is today’s shopping list, not tomorrow’s nice-to-have. In the immediate term, the focus ought to be on fostering awareness, enhancing understanding and establishing a solid foundation.
“We may not be in a position to instantly extract the business value, but the most crucial aspect is to lay the groundwork for your long-term plan – primarily, invest in people and infrastructure, which is the key to achieving sustainability over the long run,” Pant says. “This includes capacity, scalability, continuous learning and robust governance, all of which are vital for the large-scale utilization of generative AI.”
Why data (and governance) are as important as oxygen
Once you’ve got your infrastructure in place, the next step is the vast amount of data the organization has, and how that data flows, what governance tools and technology exist, and if the right data can be applied to the right business needs.
“The right talent brings the necessary expertise and oversight, but it’s equally important for organizations to focus on creating high-quality, diverse and reliable data, which serves as the bedrock for AI,” Pant says. “This data must be pertinent, dependable and easily accessible. Moreover, the availability of such data can be built incrementally, use case by use case, as the integration of AI into your business progresses.”
However, very few companies have central data management policies and data curation processes. In fact, the study found that 81% of respondents are facing data silos within their organizations. This is a critical issue to solve, along with the need for data cleaning, quality checks, security, regulatory compliance and processing skills.
Very few companies have central data management policies and data curation processes.
Many organizations haven’t gotten their arms around the new paradigm in data governance and regulatory frameworks now that AI has moved from deterministic to generative. There’s no standard in place yet – and since generative AI is so far a moving target, making startling leaps and powering unexpected innovation at a speed that hasn’t been matched previously, it will take some time to settle on universal guidelines.
Leaping into the generative AI fray
Ambition can’t outstrip readiness for long; 61% of respondents are aware they have one year or less to implement their AI strategy before incurring significant negative business impacts from falling behind. But these six critical AI readiness pillars can act as an organization’s roadmap in the journey from AI Laggard to Pacesetter, no matter the size of the business – and it’s crucial to keep an eye on the prize and plan for a marathon.
Organizations need to focus on certain immediate aspects such as infrastructure, talent and pilot projects.
“Undeniably, organizations need to focus on certain immediate aspects such as infrastructure, talent and pilot projects,” Pant says. “However, it’s important to keep long-term objectives at the forefront and keep thinking big. A strategic plan that’s both grounded in reality and realistic about the near-term possibilities, yet is flexible enough to adapt as the technology progresses, ultimately ensures a company’s ability to capitalize on emerging opportunities.”
You can start by getting the lay of the land with the Cisco AI Readiness Assessment, which offers benchmarks for your market, your region and your AI goals, and then download the full report for free.
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