Insight announcements AI Adoption Is a Top Transformation Priority for IT Leaders — but They Have a Long Way to Go
By Stan Lequin / 7 Jun 2024
By Stan Lequin / 7 Jun 2024
I also get excited when I look at what our clients are doing and see companies like Cabot’s employing AI to drive innovation for their business. With guidance from Insight and Microsoft, their generative AI-powered chatbot, called the Cabot's Project Assistant, provides automated customer support for DIY woodcare projects. Using advanced AI and cloud technologies, the chatbot can dynamically answer questions, provide recommendations and solutions, and free up the company’s help and advice teams to focus on more complex queries.
That’s progress. But change doesn’t always come easy with many organizations grappling with the complexities of AI adoption. No matter how great the hunger to use it is, AI only works if the right foundation is in place.
New research by Insight, in partnership with Foundry, an IDG company, illuminates how just about every company today is at least testing AI to some degree. Yet actually finding success with it is another story.
According to the data in our new survey, The Path to Digital Transformation: Where IT Leaders Stand in 2024, AI by far is top of mind. Our annual state-of-progress survey asked 400 senior IT decision-makers at companies of at least 1,500 employees across various industries about their planned digital transformation initiatives. 99% of enterprises plan to use generative AI in the next 12 months to drive business value.
Nearly half of technology decision-makers (47%) say optimizing their data estate for AI is their organization’s top digital transformation priority. Furthermore, leveraging gen AI to drive innovation now ranks among their top three IT objectives just two years after its emergence.
Besides AI adoption, they also cited optimizing cloud operations (43%) and strengthening cybersecurity programs (37%) as their biggest priorities.
While 92% say their organizations are at least testing AI technology, many express concerns about limitations in their IT programs to fully utilize it:
Only 36% have implemented the data infrastructure required for AI adoption.
Less than one-quarter (23%) have optimized and automated best practices for AI.
71% have yet to adopt an enterprise AI strategy.
Additionally, the majority (61%) said they must invest further in AI talent and training. They ranked gaps in technology skills and knowledge as the top challenge inhibiting overall digital transformation (44%). Budget constraints followed (43%), while a lack of optimized infrastructure to support new technologies like gen AI was third (42%).
Less than one-third (29%) have built AI Centers of Excellence, which we see as a key starting point to unifying strategic adoption and management of AI across various lines of business. Like many enterprises have learned through their cloud adoption experience, costs and shadow projects can easily get out of control if projects run independent of one another, with little governance in place to guide the management of what is acceptable.
But there are signs of progress, as 62% are in the early stages of creating an AI CoE. So, while conversational and generative AI may be the big thing right now, it’s only going to get bigger as more and more organizations take their next steps to taking advantage of the phenomenon.
Our survey spanned every facet of the digital journey, including how organizations are handling cloud/multicloud, as-a-service models, cybersecurity, platform engineering and the use of technology to address Environmental, Social and Governmental (ESG) issues.
Among the key findings:
43% reported experiencing a cybersecurity breach over the past 12 months. Just more than half of the organizations were able to recover between one (17%) and multiple days (37%). Some companies were only able to recover after 2-3 months (3%) or longer (3%).
87% of respondents reported ESG concerns have an impact on IT investment decisions, with 42% ranking the impact as significant and 45% as moderate. Top issues affected by ESG considerations include investments in technologies for remote/hybrid working, energy-efficient IT infrastructure and devices, and cloud use to reduce energy consumption.
An average of 46% of public cloud workloads will get repatriated to an on-premises or hybrid cloud environment in the next 12 months. Top drivers for these projects are improved security and compliance for sensitive data, enhanced business continuity and disaster recovery, and resolution of performance issues like latency. One third of respondents cited a need to modernize legacy workloads before returning them to the cloud as a primary reason for repatriation. Just 29% named a reduction in cloud spending as a principal motive.
96% are leveraging one or more as-a-service delivery models, led by Network as a Service (NaaS) and Storage as a Service (StaaS). 81% are using at least three, 66% at least four, 35% at least five and 10% are using at least six.
Much of this is the unsexy backbone work required to establish a strong AI program. After all, the quality of output you’ll get from something like a generative AI-powered chatbot that you want to use as front-line customer service is only as good as the data estate it draws from.
AI represents a paradigm shift in all-around work methodologies and business agility, so it’s not surprising that it is rising to the top of enterprise IT priorities. We see it with our clients daily: They’re unsure about how to unlock the potential of AI to drive new business value.
They seek our help to upgrade their infrastructure, train employees on classical and generative AI concepts and prompts, identify use cases, and jumpstart AI projects by using our Insight Lens™ platform’s data-ingestion capabilities.
Only when all the pieces of the puzzle are put in place do we see innovation from the Cabot’s of the world truly begin to take shape.