The organizations pulling ahead have made energy security, materials resilience and operational flexibility a strategic priority. Here is how IBM helps.
The conditions
reshaping growth
Energy, materials and market access power every growth plan. All three are under more strain than at any point in the last two decades. The technologies organizations are counting on to drive the next wave of growth are accelerating that strain.
Constraint, handled well, is a competitive advantage
Companies that treat energy, materials and market access as design questions are outperforming the ones that don’t. IBM’s Institute for Business Value found that companies who build sustainability into how they run, not just how they report, are 52% more likely to beat peers on profitability and grow revenue 16% faster. The ones pulling ahead aren’t waiting for conditions to improve. They’re building around them now.
64%
of organizations are deploying generative AI
1/3
have made meaningful progress managing AI’s environmental impact
Efficiency and trust built from the chip up
Building around these constraints takes infrastructure that can hold energy, materials, and market access together — not solve them one at a time. IBM works across the whole AI stack, from chip-level research through systems, cloud, models, and applications, taking on all three simultaneously.
Solving one constraint in isolation risks creating new exposure in the others — optimizing energy without addressing materials risk, or tightening compliance without governing AI, trades one vulnerability for another. IBM’s integrated position is designed to close all three at once, backed by trust and governance credentials that matter most where the cost of a mistake is highest.
IBM designs its own processors and accelerators for IBM Z, LinuxONE, and IBM Power. Purpose-built systems where energy efficiency is a design requirement. IBM Z has increased total capacity per kilowatt by more than 100 times since its first generation. Source: IBM Redbooks. IBM z17 Technical Introduction.
Misplaced workloads waste energy, inflate cloud spend, and underperform. IBM's hybrid by design approach puts the right workloads on the right systems — on-premises, cloud, or edge — and keeps them there. IBM Turbonomic and Red Hat OpenShift (link resides outside of ibm.com) automate those placement decisions continuously, optimizing performance, cost and energy consumption simultaneously without manual intervention.
IBM is ranked first in the Stanford AI Foundation Model Transparency Index (link resides outside of ibm.com) with a score of 95, up from tenth place the year before. IBM is named a Leader in the IDC MarketScape for Unified AI Governance Platforms, the Forrester Wave for AI Governance Solutions, and the Gartner Magic Quadrant for Governance, Risk and Compliance Tools.
IBM's approach to Responsible Technology is guided by enterprise-wide governance that helps embed trust, transparency, and accountability across how IBM designs, develops and deploys AI. IBM Consulting's AI governance practice brings that discipline to client engagements, helping organizations meet the demands of the most regulated industries in the world.
Energy access
Energy demand is putting pressure on supply. AI infrastructure, electrification, and manufacturing are all pulling from the same grid at the same time, and most utilities cannot expand fast enough to keep up with all three demands running at once.
Organizations that redesign how they consume energy, rather than just how much they buy, are turning that pressure into a lasting edge. An IDC study sponsored by IBM found that nine surveyed companies reduced unplanned downtime by an average of 47% after adopting IBM Maximo. IBM Research is pushing further at the grid frontier.
Materials access
Materials regulation is accelerating on every front. PFAS restrictions, critical minerals sourcing rules, and supply chain transparency mandates are all expanding across major markets at once, and most sourcing teams are still tracking it by hand, in spreadsheets.
When supply chain data flows through one connected system, procurement teams see risk before it becomes a shortage. AI speeds up materials analysis, sharpens supplier risk scoring, and monitors continuously, without someone chasing spreadsheets every quarter.
Market access
Market access now depends on regulatory readiness. Reporting frameworks covering energy, emissions, water, and supply chain transparency are expanding across major markets, and AI governance has become a procurement requirement in regulated industries.
Organizations that generate compliance output as a byproduct of running the business are spending less, moving faster, and reaching markets that others cannot. IBM offers a layered approach to compliance and AI governance, from model monitoring through to sovereign deployment.
Already proven at scale
IBM applies the same discipline to its own operations that it builds for clients, using every layer of the stack to secure energy, materials, and market access at a global scale. IBM does not sell what it has not already run. The results are documented across energy management, emissions reporting, and AI-powered client operations.
Quantum computing and the next class of problems
Solving today’s constraints is only part of the work. IBM is also exploring what comes after — where quantum computing changes what’s possible, starting with the problems classical scale can’t solve.
From complex variables to practical breakthroughs
Quantum computing does not replace classical infrastructure. IBM's quantum-centric supercomputing approach combines quantum computing with powerful high-performance computing and AI infrastructure. Chemistry, materials science, and complex optimization are among the most promising domains approaching quantum advantage — the point at which quantum computing is faster, more accurate, or more cost-effective than classical methods alone.
Quantum's connection to the three constraints discussed above is under active exploration. Exploring questions like:
- How do you design a catalyst that makes fertilizer production less energy intensive?
- How do you model chemistry well enough to build batteries that last longer and store more energy?
- How do you improve a grid with thousands of variables shifting in real time?
These are areas IBM Quantum is exploring with clients and partners across energy, manufacturing, and automotive. The Quantum Computing for Sustainability Working Group, led by IBM researchers and experts in industry and academia across the globe, is pursuing the same open questions.
