Quantum Proofs of Concept
Moving from Quantum Awareness to Embedded Workflows
One of the key principles of Quantum’s Business over the years has been “Do Your Own Research”. Take no statement at face value. Check the details. Don’t take the advertisers at their word. Look to multiple advisors. Consider those of cautious analysts like Marin Ivezic and Jack Krupansky, as well as the balanced industry experts actively pursuing quantum tech like Jaime Gomez Garcia and Philip Intallura.
For any new technology, credibility is key. Investments of time, money, and other resources are at risk. Prioritizing a new technology within an organizations’ IT budget can considered speculative. In fact, whenever I speak to business leaders they challenge me on the value of quantum technologies, both today and into the future. So let’s start with where are today.
Image source: Brian Lenahan/ Midjourney
Where Are We in 2026?
McKinsey’s Quantum Technology Monitor 2026 (released late April) delivers a clear signal: quantum computing has reached a commercial tipping point. Over 300 organizations worldwide—including Airbus, JPMorgan Chase, Boehringer Ingelheim, E.ON, and Liberty Mutual—are now actively collaborating with quantum technology vendors. (AT&T just announced deeper dives with D-Wave). According to McKinsey, quantum computing companies generated more than $1 billion in revenue in 2025, with projections reaching as high as $4.4 billion by 2028. The broader economic value unlocked by quantum could hit $1.3–2.7 trillion globally by 2035.
“I think that in 2028 or 2029, you’ll see it have a measurable impact on our top line and bottom line,…By the end of the 2030s, we are now pretty convinced this is a trillion dollars of value.” - IBM CEO Arvind Krishna
$1 trillion while the internal quantum technology market itself is expected to grow to $60–100 billion in the same timeframe (quantum computing accounting for the majority). This is no longer a story of pure research or speculative pilots.
First movers are embedding quantum into end-to-end workflows—hybrid classical-quantum-AI systems that tackle specific subproblems inside existing business processes. Roughly 72% of activity sits with privately owned entities despite government interventions through roadmaps, guidelines and mandates. One-third of the analyzed companies allocated more than $10 million to quantum in 2025 (7% spent over $50 million), primarily on use-case development and integration.
The question for business leaders is no longer “Should we explore quantum?” It is “How do we move from awareness and isolated pilots to repeatable, embedded value—and which problem classes offer the most realistic near-term returns?”
Sector Prioritization: Where the Value Concentrates
McKinsey and industry activity point to four priority sectors:
Chemicals and life sciences / pharmaceuticals: Molecular and materials simulation remains the strongest near-term use case. Companies are using quantum to model interactions that classical computers approximate poorly, accelerating targeted experimentation and reducing physical lab cycles.
Financial services: Portfolio optimization, risk-scenario generation (including edge-case correlations classical models miss), cryptography migration, and fraud-related anomaly detection.
Travel, transport, and logistics: Combinatorial optimization problems—routing, scheduling, network design—where even modest improvements compound into large operational savings.
Broader industrial and energy applications: Supply-chain and process optimization that sit at the intersection of the above.
These sectors share a common trait: they already generate large volumes of structured data and face combinatorial or simulation bottlenecks that hybrid approaches can attack incrementally.
Problem Classes Closest to Practical Value Today
Not every quantum algorithm is equally mature. The classes showing the most traction in 2026 are:
Chemistry and materials simulation — Modeling molecular interactions, reaction pathways, and material properties. This is where many of the 300+ companies (especially pharma and chemicals players) are concentrating early spend. Hybrid quantum-classical methods already deliver useful approximations on current hardware for smaller systems.
Optimization — Portfolio construction, vehicle routing, production scheduling, and logistics network design. Quantum annealing and gate-model variational algorithms are being tested inside classical optimization loops. Gains are often modest today but highly measurable.
Risk modeling, scenario analysis, and fraud detection — Capturing complex correlations and rare-event tails in finance and insurance, or detecting subtle patterns in transaction data. These map well to hybrid quantum machine-learning approaches and can be framed as subproblems within existing risk engines.
Logistics and supply-chain optimization — A practical subset of the broader optimization class, with clear KPIs (cost, time, emissions, resilience) that make ROI easier to track.
These are not yet “quantum advantage” in the strict scientific sense for large industrial instances, but they are already producing directional insights and measurable efficiency lifts when embedded hybrid-style.
Realistic Near-Term ROI Expectations
ROI remains difficult to quantify precisely because most systems are still noisy and limited in scale.
Expect:
Short-term (2026–2028): Learning ROI and capability building more than pure financial returns. Successful programs measure success by improved model accuracy on subproblems, reduced classical compute time for hybrid workflows, faster insight generation, and internal talent development.
Medium-term: Incremental operational improvements—single-digit percentage gains in optimization or simulation fidelity that compound across large portfolios or supply chains.
Longer-term: Step-change value once fault-tolerant systems mature, but the companies that wait will face higher talent costs and weaker intellectual-property positions.
McKinsey is explicit: companies that act now gain competitive edge and help define industry standards. The cost of inaction is rising as talent scarcity and industry consolidation accelerate.
“Wait and see” will cost you dearly. This added complexity is exactly why migration is a highly complex, multi-year programme: discovery, vendor dependencies, testing, governance. The standards picture will keep shifting - build agility into the migration itself, or relive the whole programme when the next break lands.” - Philip Intallura
How Companies Should Structure POCs Right Now
A disciplined POC approach in 2026 looks different from the exploratory experiments of 2022–2024:
Start with a hybrid framing — Identify a high-value subproblem that can sit inside an existing classical (or AI) workflow rather than attempting to replace an entire process.
Ensure data readiness first — Quantum algorithms are sensitive to data quality and format. Clean, well-structured datasets are a prerequisite; many pilots fail here.
Choose measurable business KPIs — Accuracy lift, runtime reduction, cost savings, or risk-metric improvement—not qubit counts or algorithm novelty.
Use quantum-as-a-service / cloud access — Avoid heavy capital outlays. Leverage vendor platforms (or national/academic testbeds) to iterate quickly.
Build a small, cross-functional core team — Business domain experts + classical data scientists + one or two quantum-aware specialists. External partners accelerate learning but should not own the IP.
Design for repeatability and governance — From day one, plan how a successful POC graduates into production: integration points, monitoring, security (including post-quantum considerations), and change management.
Time-box and stage-gate — 3–6 month sprints with clear go/no-go criteria. Parallelize 2–3 use cases across different problem classes to diversify learning.
Capture intellectual property and process knowledge — Document what works, what does not, and how hybrid orchestration is best managed. This becomes a durable advantage.
The hybrid path—quantum tackling the hardest subproblems while classical and AI systems handle the rest—is the credible bridge to near-term value. First movers are already testing these setups in operational environments.
The Bottom Line for Leaders
McKinsey’s 300+ company cohort is not a distant future signal—it is a present-day competitive reality. The organizations embedding quantum into workflows today are building institutional knowledge, securing talent, and shaping the standards that will define the next decade. If your firm is still at the awareness or single-pilot stage, the window to catch up without paying a significant premium is narrowing. Prioritize chemistry/simulation, optimization, risk/fraud, or logistics use cases; structure tight, hybrid, KPI-driven POCs; and treat quantum capability as a strategic asset rather than an R&D experiment. The commercial tipping point has arrived. The question is whether your organization will help define the new workflows—or adapt to those defined by others.
What quantum use case is your organization testing in 2026? Reply or comment—I read every one.
Brian Lenahan is founder and chair of the Quantum Strategy Institute, author of seven Amazon published books on quantum technologies and artificial intelligence and a Substack Top 50 Rising in Technology. Brian’s focus on the practical side of technology ensures you will get the guidance and inspiration you need to gain value from quantum now and into the future. Brian does not purport to be an expert in each field or subfield for which he provides science communication.
Brian’s books are available on Amazon. Quantum Strategy for Business course is available on the QURECA platform.
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