Industry executives and experts share their predictions for 2025. Read them in this 17th annual VMblog.com series exclusive.
By Srikara Rao, Chief Technology Officer –
Cloud, Cyber Security Services, R Systems
Artificial intelligence is transforming
enterprises, reshaping how software is developed, tested, and deployed. It is
enhancing productivity, streamlining workflows, and enabling innovation at
scale, ultimately improving the way engineering teams create digital products.
However, the shift toward AI-powered engineering also has challenges that must
be addressed in order to leverage it to its fullest potential.
Accelerated Growth in AI
Capabilities
In software engineering, AI advancement
could lead to AI models reaching near-perfect accuracy for tasks such as code
generation, debugging, and system optimization. In 2025, engineers can expect
AI to play a role in driving product development, leading to faster product
development cycles, improved software reliability, and an overall enhancement
in the quality of digital products.
For example, within financial institutions,
banks could use it to help process loans or transfer money. In the past, using
AI for these types of cases was difficult, but generative AI has now become
sophisticated enough that it is able to discern if a person is eligible for a
loan. It can link multiple parts of the person’s identity and then say “What is
their eligibility?” and then “Do they have a credit score within our loan
parameters?” and then “Are they regularly receiving some sort of income?”. These
questions added up by generative AI can give a banker enough information to
make an informed decision about whether or not they’d grant the loan. There
are, of course, still measures that humans should be taking to reach this
conclusion, but it helps speed up the initial process.
As AI models continue to become smarter
and reach higher levels of accuracy, they will enable engineers to tackle more
complex, strategic challenges, delivering more innovative solutions.
Widespread Use of AI
Agents and Integration of AI in Knowledge Work
One of the most significant ways AI will
impact digital product engineering is through the integration of AI agents into
knowledge work. These agents will be able to automate routine tasks, such as
managing software testing workflows, monitoring system performance, and
handling project management tasks. As these agents become more sophisticated,
they will take on more responsibility in the development process, allowing
humans to focus on more high-level work.
AI agents will also manage repetitive
tasks with speed and efficiency, from ensuring seamless software deployments to
handling code reviews. This automation will reduce human error, streamline
workflows, and foster a more innovative and productive environment.
Escalating Investment in
AI Infrastructure
To support the increasing demand for
AI-powered systems, companies are ramping up their investment in AI
infrastructure. According to Bain & Co., the market for AI-related products will
hit as much as $990 billion in 2027. Companies such as Meta and AWS will
allocate over $10 billion toward AI infrastructure development. As AI continues
to scale, its infrastructure needs will grow more complex.
For product engineering teams, the
significant investment in AI infrastructure will mean greater access to
advanced tools and resources, allowing for faster and more reliable product
development. However, the high costs associated with AI infrastructure present
challenges in terms of resource allocation and the need to keep pace with
rapidly advancing technologies.
Key Challenges to
Overcome in 2025
While AI has many potential benefits,
engineering teams will face several challenges as they integrate these
technologies into their workflows.
1. Balancing Innovation with Security and Privacy
As products become more AI-driven and
data-centric, balancing innovation with security and privacy will be
increasingly complex. Ensuring robust security measures and compliance with
privacy regulations like GDPR or HIPAA will be paramount, especially as AI
systems handle more sensitive data. Engineering teams will need to design
secure systems that protect user privacy without sacrificing the innovative
capabilities that AI brings.
2. Managing Complexity and Integration
Engineers will need to continuously learn
and adapt to new AI tools, techniques, and best practices as technological
advancements surge.
Integrating AI capabilities with existing
systems and workflows can be difficult, particularly in legacy environments –
the finance and healthcare industries have been known to be laggards, for
example. Because of this, ensuring that AI tools can interact seamlessly with
existing software infrastructure without compromising product stability or
performance will require careful planning and implementation. Teams will need
to balance the complexity of new AI systems with the need to maintain the
integrity of established workflows.
3. Talent Acquisition and Upskilling
As AI continues to grow in prominence,
the demand for skilled professionals with the right mix of AI, data science,
and engineering skills can be difficult to come by. Digital product engineering
teams will need to invest in talent acquisition and upskilling to ensure that
they have the expertise needed to harness the full potential of AI.
AI’s impact on the enterprise is set to
be transformative. While the benefits are clear-accelerating development
cycles, improving product quality, and fostering innovation-the challenges that
come with integrating AI into existing workflows are significant.
In 2025, by balancing innovation with
security, managing integration complexity, and keeping pace with technological
advancements, engineering teams can navigate the evolving landscape of AI and
create products that are both cutting-edge and responsible.
