Evaluating Gartner’s 2025 Technology Predictions: A Mid-Year Reality Check
When Gartner released its annual list of top technology predictions for 2025, some felt familiar, others ambitious. At the time, we picked three that struck closest to home: Agentic AI, energy-efficient computing, and neurological enhancement. These weren’t just headline trends. They aligned with what we see daily in embedded systems, medical software, and AI-enabled platforms.
So, we made a note to revisit them halfway through the year, not to chase hype, but to ask a more grounded question: have these technologies started reshaping how systems are built, deployed, and funded?
To get perspective beyond the press releases, we spoke with senior engineers from PerformaCode – people with decades of hands-on work in embedded software development, microcontroller-based systems, robotics, AI-powered diagnostics, and medical device firmware for ICU-grade platforms. We also reviewed current forecasts, adoption data, and what’s changed since those trends were first published in late 2024.
The result is part validation, part reality check, and part reminder that behind every “transformative” trend is a long list of practical constraints.
Agentic AI: the age of autonomous agents
Our AI expert described Agentic AI as “a big step forward for business efficiency.” And while it might sound futuristic, enterprise adoption is already underway. Deloitte predicted that by 2025, 25% of companies using generative AI would be piloting agent-based systems.
Midway through the year, that projection still appears to be on track. A July 2025 Capgemini report confirms that fewer than 25% of organizations have launched agentic AI pilots. Meanwhile, IT Pro notes that around 14% are in early implementation, and only about 2% have reached full deployment. A McKinsey forecast reinforces this trajectory, with 25% of AI adopters planning to launch autonomous agents before the end of the year. Although full-scale deployment remains rare, the trend toward enterprise-agentic AI appears to be real.
Of course, building these systems is another story.
Explainability: “Let’s face it: black box AI isn’t good enough in 2025. If a system can’t explain itself, especially in healthcare or finance, it’s a dealbreaker. Ethical and transparent design is non-negotiable. This applies especially to regulated industries where explainable AI is critical.”
Data Requirements: “Most companies are drowning in data they can’t use. The problem isn’t a lack of data – it’s a lack of the right systems to process and make sense of it. We’ve seen this repeatedly in AI integration projects.”
Costs: “We can’t ignore the elephant in the room: AI development is expensive. Not every business can afford to experiment with high-risk, high-reward systems. Custom AI solutions often require upfront investment before ROI becomes measurable.”
Energy-efficient computing: sustainability meets scalability
One of our embedded systems engineers summed it up this way: “Energy efficiency isn’t a bonus anymore. It’s a constraint.” And halfway through 2025, that constraint is already shaping architecture and budgets.
By 2030, U.S. data centers are projected to consume between 4.6% and 9.1% of the nation’s electricity load, up from roughly 4% in 2023, according to the Electric Power Research Institute. But recent data suggests we’re reaching that range sooner than expected. Bloom Energy’s 2025 Data Center Power Report shows 30% of sites will run entirely on onsite power by 2030 to avoid grid overload, and Goldman Sachs forecasts a 165% increase in global data center power consumption by the end of the decade.
This isn’t just a data-center story. From industrial controllers to AI-enabled wearables, energy constraints are being pushed down into firmware and embedded software layers. Optimization is fundamental. As one of our engineers observed, the real bottlenecks are rarely in the big numbers, they hide in the daily trade-offs:
Algorithm Optimization: “Most software today is unnecessarily complex and resource-heavy. Addressing this means focusing on leaner, more efficient code – think power-aware algorithms and embedded systems optimization that eliminate waste and boost performance.”
Testing Tools: “Current energy profiling tools are outdated and often unreliable. Developers need accurate power-profiling and simulation frameworks to predict real-world consumption without guesswork.”
Legacy Integration: “Replacing or updating legacy systems is a huge challenge. Many companies feel stuck because modernizing legacy firmware and hardware platforms carries high costs and risks.”
Neurological enhancement: redefining human potential
Our medical systems lead described neurological enhancement as “the most exciting—and most complicated—trend on the list.” In 2025, that complexity is no longer theoretical. From therapeutic devices to experimental consumer tools, brain–computer interface (BCI) systems are steadily moving from the lab into product roadmaps.
Non-invasive technologies like transcranial direct current stimulation (tDCS) and transcranial magnetic stimulation (TMS) are already in limited use for both clinical and recreational applications. tDCS, for example, is marketed to enhance language acquisition, memory, and cognitive performance. A 2022 meta-analysis reported measurable working memory gains in older adults, but the method remains unapproved by the FDA, and most claims are still under active clinical review.
The BCI market itself is growing fast. Precedence Research estimates the global BCI industry will reach USD 2.94 billion in 2025, and expand to USD 12.4 billion by 2034, with a compound annual growth rate near 18%.
From a systems perspective, that growth brings more than signal processing challenges. Our engineers called out three core concerns:
UX Design: “You’re not designing screens. You’re designing believable feedback between a person and their neural signals. If it doesn’t feel intuitive, it won’t be usable—especially when the user can’t always describe what ‘intuitive’ means.”
Data Security: “Brain data is fundamentally different. You can’t revoke it, reset it, or fully anonymize it. There’s no technical silver bullet: just risk reduction through encryption, access control, and data minimization. The rest are still frameworks and good intentions.”
Collaboration Across Fields: “You need neuroscience, embedded engineering, UI, and compliance teams working in sync. If any one of those lags, the whole system fails – either technically or ethically. They usually operate in silos, so building these bridges will be a major hurdle.”
The close up
History shows that technological shifts rarely arrive on time, but once they do, adoption tends to accelerate. By mid-2025, Agentic AI, energy-efficient computing, and neurological enhancement are no longer speculative—these trends are unfolding now, reshaping systems by resolving challenges in explainability, efficiency, and cross-disciplinary integration.
Recent industry forecasts confirm the momentum in each:
- Agentic AI: Gartner predicts that by 2028, at least 15% of day-to-day work decisions will be made autonomously by agentic AI systems, rising from virtually none in 2024
- Energy Efficiency: The global green data center market grew to USD 81 billion in 2023 and is expected to reach USD 162.40 billion by 2029, driven by demand for sustainable infrastructure (by TechSci Research report).
- Neurological Enhancement: The brain–computer interface (BCI) market is forecast to expand from USD 23 billion in 2023 to USD 8.36 billion by 2032, at a compound annual growth rate of nearly 16% (SnS Insider).
These figures don’t represent distant futures. They reflect active transformation. The future is not just coming. It’s being engineered right now and right here.