Industry executives and experts share their predictions for 2025. Read them in this 17th annual VMblog.com series exclusive.
By Al
Farmer, Senior Business Development Lead of Artificial Intelligence and Data
for Digital Velocity at CDW
What to expect when it comes to AI in the new year
As we head into 2025, efficiency
and productivity gains are on every leader’s mind. Many see AI as the key to unlocking
both initiatives, but there are plenty of barriers to overcome before long-term
benefits are realized. With AI implementation in
full swing for many, we can expect organizations to re-evaluate their
strategies and place greater emphasis on leveraging tools that drive
cost-savings, better equip
workers for success and democratize IT.
Reevaluating AI Strategy: Budget and Data Requirements
Artificial
intelligence has quickly positioned itself as a revolutionary application in nearly every industry. However, despite 68% of
organizations using AI according to the 2024 CDW Cloud Report, many still attribute
cost, security and data quality as key factors
causing slowdowns in their implementation efforts.
In the coming
year, generative AI tools will shift from “nice-to-have” to
“need-to-have” as more leaders justify initial costs to stakeholders and
realize the competitive disadvantages they may face
without AI capabilities. For many, this will be the first full fiscal year that
incorporates AI investments into budgets. As funding increases, organizations will
expand which use cases are taken to scale, leading
to more opportunities for streamlined operations and savings.
Also, many
organizations now understand that for AI to be leveraged effectively, data due
diligence must be completed upfront. Data is paramount for
all language models: in order for a chatbot to answer a question correctly, it
must have access to a pristine governed data set containing the answers when
using AI on private company data. This requires a heightened focus on data hygiene
and in some cases, a complete reset to ensure all information is properly categorized
and tagged ahead of time, and personally identifiable information (PII) and
sensitive data are accounted for accordingly.
To address
the challenges of AI implementation, companies will look to bring on the right
partners or create positions dedicated to developing the necessary
infrastructure for standardizing AI motions. The responsible parties will oversee and guide leadership alignment, use case
ideation, data preparation, cost management and data governance. As partners
and cloud providers bring AI to the front of offerings, heightened
orchestration will translate to more complex agentic workflows, bringing
opportunities for organizations to explore the value of Small Language Models
(SLMs).
Pivoting
from Reactive to Proactive: Equipping Workers for Success
The next
iteration of use cases for generative AI will focus on equipping IT workers
with the tools to work more efficiently. With more attention paid to AI as a
way to increase productivity, there is an anticipated shift away from reactive
measures and instead toward proactive strategies.
For example,
an LLM can be trained to read errors in a Kubernetes stack and identify
solutions that translate complex IT errors into plain English. This saves time
upfront, by proactively formatting messaging in easily digestible language that
also provides a general direction for IT teams to work from. Not only does this
streamline efficiency, but it also expedites the error detection process.
As a result,
workers will spend less time translating the code, and more time focusing their
efforts on addressing the problem at hand. This means less strain on the
workers and more time moving the business forward.
Democratizing
IT to Address Skills Gaps
In addition
to reducing strain on workers, 2025 is likely to usher in an increased interest
in democratizing IT as organizations continue to battle skills gaps and
staffing shortages.
31% of organizations cited a lack of AI expertise
on-staff as a key hindrance to their AI approach. Although a lack of technical
expertise may present an obstacle when it comes to adopting AI, the technology
can also be used to address long-term challenges.
Generative
AI’s ability to translate complex coding errors into plain language lowers
barriers to coding and app development. This opens doors for workers with
nontraditional backgrounds to fill holes in staffing and
also brings opportunities for existing employees in non-technical
departments to work more closely on app development, leading to enhanced
collaboration and empowering the workforce to be more hands-on with IT.
Overcoming
Challenges to AI Success in 2025
In the months
ahead, organizations will foster a technical and cultural shift to make room
for a new era of digital transformation fueled by generative AI. As more
dollars are allocated to prioritize technology, more challenges will arise,
from data hygiene concerns to staffing shortages propelling IT democratization.
To address these challenges AI will also be part of the solution, driving ROI
through enhanced productivity and cost savings.
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ABOUT
THE AUTHOR
Al
Farmer, Senior Business Development Lead of Artificial Intelligence and Data
for Digital Velocity at CDW
Al Farmer
is a seasoned professional with extensive experience in business development
and technology leadership. Currently serving as the Senior Business Development
Lead of Artificial Intelligence and Data for Digital Velocity at CDW, Al Farmer
also co-owns multiple ventures, including Caregiving Network, ROI Genius, and
Alternative Spaces. Al Farmer earned a Bachelor of Science degree in Computer
Science from the University of New Hampshire, where studies took place from
1990 to 1993.
