After around six years of relatively flat growth in AI adoption, the enterprise AI landscape experienced a notable shift between 2024 and 2025. AI is now deployed by 78% of surveyed organizations in at least one business function, marking a substantial increase from 55% reported the year before. Furthermore, for the first time, a majority of these organizations report using AI across multiple business areas, signaling a move beyond isolated initiatives toward more widespread, value-driven integration.

While the entry of GenAI has contributed to this change, there are other factors too. Pilot projects have proved AI’s potential and the pressure is on to deliver measurable outcomes in high-impact areas such as total experiences, operations and risk management. Unlike a few years ago, enterprises now know where AI can move the needle, which means ambiguity can be replaced with well-defined, verticalized applications. Growing data readiness is also playing a central role, with all the investments in building data lakes, standardizing pipelines and improving governance starting to pay dividends.

Finally, the need to outcompete is stronger than ever, with early AI adopters shaping market dynamics for the late majority and laggards. The imperative is clear: scale or risk being left behind. However, making the move from experimentation to scale can be tricky.

Challenges in scaling AI

Performance requires powerful hardware. Combined with software licenses and ongoing maintenance, this can often lead to high costs. Power consumption is also a major challenge, as AI workloads continue to drive up data center power demand and current estimates point to a 165% increase by 2030. Adding to the obstacles, the complexity of integration with existing enterprise systems and the need for thorough testing result in slow deployments. The answer lies in striking the right balance across raw performance, operational cost, energy consumption and time-to-market.

This is where silicon-to-software collaboration can make all the difference.

The power of ecosystem collaboration

As enterprises move AI from labs into live operations, successful deployment will require a robust tech stack that underpins flexible strategies for diverse use cases and evolving market demands. Silicon-to-software integration is essential for realizing this vision, as it directly addresses the key roadblocks that enterprises are facing right now in their pursuit of scaling AI.

Performance: Instead of piecing together disparate components, joint design of chip features and software drivers ensures predictable end‑to‑end throughput.

Efficiency: Embedding power‑profiling in silicon, in combination with orchestration software, allows dynamic voltage and frequency scaling, along with workload‑aware scheduling, significantly reducing power consumption.

Cost: Pre‑validated reference architectures and integrated software frameworks help organizations avoid custom configurations and unexpected downtime, reducing the overall cost of running large-scale AI.

Speed: Co‑released and co‑supported by partners, turnkey bundles of silicon, firmware, drivers and deployment pipelines compress integration and testing cycles from months to weeks, accelerating enterprise rollouts.

Joining forces to co-engineer for success

Tackling today’s enterprise challenges requires more than advances in hardware or software alone. It demands close collaboration between technology leaders, each bringing unique expertise and perspectives. By working together, these partnerships co-create integrated solutions that are precisely aligned with real-world business needs, driving faster innovation while managing costs and sustainability.

One example of this approach is our strategic alliance with AMD, which aims to deliver future‑ready solutions tackling today’s most significant enterprise pressures, including innovation velocity, cost control and sustainability targets. At the heart of this partnership are joint AI Labs for rapid prototyping and proof‑of‑concept testing, ensuring shorter feedback loops and reduced deployment risk. Our training and reskilling programs equip teams to leverage advanced AI tools. At the same time, our focus on end-to-end co‑design from silicon to application paves the way for consistent performance, greater efficiency and streamlined manageability across industry-specific use cases.

By deploying AMD Ryzen™ PRO-powered endpoints with on‑device AI inferencing, manufacturers can access predictive maintenance, anomaly detection and immediate quality‑control feedback right on the shop floor. This boosts productivity and decision‑making while reducing the risk of downtime.

The high core density and energy efficiency of AMD EPYC‑based infrastructure can enable financial institutions to reduce operational costs and consolidate trading platforms, risk model simulations and large‑scale analytics workloads. Additionally, built-in security features can help banks and insurers safeguard sensitive customer data and ensure compliance with regulatory requirements.

In retail, AMD Instinct™ accelerators paired with EPYC™ servers can train recommendation engines, demand forecasting models and computer vision systems at scale, delivering personalized shopping experiences and real‑time inventory optimization.

Together, we are creating a digital ecosystem where AMD’s leading-edge hardware is deeply integrated with our cloud, AI and systems‑integration expertise, resulting in solutions that help enterprises keep pace with emerging market expectations.

The path forward is ecosystem-first

Forrester predicts that AI platform budgets will triple over the next decade. As the market shifts toward increasingly compute-heavy and data-driven operations, enterprises that place future-ready AI infrastructure at the core of their strategy will emerge as frontrunners. We are already starting to see the trends that will shape the new business world, as innovations in photonics and chiplet interconnects promise to cut down latency and energy use, enabling scalable, domain-specific accelerator networks. Modular compute architectures will allow IT teams to compose CPU, GPU, memory and networking resources on demand, eliminating the need for over-provisioning. By optimizing the use of public cloud, private data centers and edge nodes, hybrid and edge-first models will ensure that workloads run where they make the most sense, helping organizations manage costs, sovereignty and variable latency requirements.

Turning these trends into tangible, enterprise-grade capabilities will require sustained co-engineering across silicon, firmware, software stacks and deployment pipelines. Done right, this ecosystem-first approach will help businesses build the flexible, efficient and secure AI foundations they need to succeed amid growing competition.

Discover how HCLTech and AMD build the AI infrastructure that enterprises need to lead the future.