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Quantum computing promises to revolutionize complex problem-solving in finance, logistics, and drug … More discovery but won’t replace classical computers for everyday business operations.

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Computers, the internet and digitization have been major driving forces of innovation over the last 50 years, but classical computing architecture has its limits.

Quantum computing is emerging as a solution to the problem of rapidly cranking up the amount of processing power we can throw at cracking particularly tricky conundrums, such as the vastly complex calculations necessary for accurately modeling the effects of medicines on humans, or predicting extreme weather events.

I’m not really here to talk about the technical differences, but just a quick primer, in case you’re not sure what I’m talking about:

While classical computers are built on binary bits that can exist in a state of on or off (one or zero), quantum computers process information as qubits, which can be zero, one or, due to the strange behavior of physics when modeled at the quantum level, both at the same time!

Difficult as this is to understand without a grounding in quantum physics, the end result is that they are capable of vastly more complex calculations than the classical computers – laptops, smartphones, workstations and data centers – we use every day.

Businesses working on tasks that could be accelerated with quantum computers have a huge opportunity in front of them. That means understanding what they’re good for in order to identify potential future use cases. So, let’s take a look.

What Quantum Computers Will Be Better At

Building machines that aren’t fixed to the rigid on/off logic is a big step towards building more accurate models of hugely complex, real, physical systems; the world around us, nature, the cosmos and the human body don’t operate in binary, after all!

This makes quantum computers superior when it comes to tackling problems involving large numbers of variables, like complex optimization problems, or computer cryptography.

These calculations are used in finance to structure investment portfolios and assess insurance risk, in logistics to determine the most efficient delivery routes, and in material science to develop new plastics and alloys.

Making better drug discoveries is also dependent on our ability to model molecules with an increasing level of fidelity. The chemical reactions and biological interactions involved at the molecular level often don’t follow the 1/0 logic.

Artificial intelligence (AI) is set to be the most transformative technology of the century, and many of the calculations used in machine learning and data analytics, such as pattern recognition, could be accelerated with quantum computing.

And another area where it’s already being predicted to have a big impact is cryptography and cybersecurity. The encryption that keeps the world’s private data safe is based on the difficulty of factoring large numbers – a task that takes classical computers an extremely long time to complete. Quantum computers, on the other hand, can crack them almost instantly, leading to fears that some methods of encryption will become obsolete and a rush to develop newer “quantum-safe” cryptography. If your business relies on keeping information secure, this is something you certainly need to be aware of now!

What Classical Computers Will Still Be Better At

Despite all the excitement around quantum computers, it’s likely that for most of us, classical computers will still be a mainstay of our day-to-day lives.

For hosting and managing email servers, running workplace and productivity software, administering databases and networks, classical computers will remain the backbone of IT infrastructure.

The increased power of quantum computing doesn’t create practical benefits when it comes to running these workloads.

Systems we have today, built around structured databases, cloud storage, and storage and retrieval of large datasets, will remain the domain of classical computers.

So, too, will the billions of low-powered commodity devices, such as the processors embedded in our cars, home appliances, civic infrastructure and industrial machinery.

Quantum computers are expensive, fragile and often need to be housed in environments where the temperature is close to absolute zero. So, for anything that involves computation on a device that sits on your desk and in your pocket, they won’t be very practical.

The Coming Quantum Revolution

To sum it up, classical computers will remain the workhorses of day-to-day business technology. Quantum computers, on the other hand, will be highly-specialized tools, designed for solving particular complex problems.

Quantum computers will not replace classical computers; they’ll work together. Thanks to AI and automation, the interface will eventually be invisible to us, with intelligent agents shifting workloads to whichever platform is the most appropriate.

If you do work in a field or industry that’s vulnerable to being disrupted by the arrival of quantum, the transformation is likely to be dramatic.

In financial services, logistics and manufacturing, as with many other industries, competition for efficiency and the cost reduction it creates is cutthroat; milliseconds matter. This means the emergence of new technology like quantum computing creates opportunities for new leaders to emerge and the status quo to be upended.

It’s time to realize that it isn’t only scientists and computer engineers who need to understand what quantum can do. Professionals, business leaders and decision-makers should be getting to grips with it, too, if they want a head-start on the competition.

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Cloud computing has evolved from basic infrastructure into a powerful engine of business innovation. Today, it enables everything from AI-powered services and e-commerce to entirely new business models and customer experiences. Despite its growth, many organizations still fall into common traps—mistakes that can be costly both in terms of money and missed opportunity. This article outlines the five most critical cloud computing mistakes businesses must avoid in 2025 and how to navigate them.

Mistake 1: Failing to Approach Cloud Strategically

Why It Happens

Too many organizations treat the cloud as simply another IT infrastructure upgrade—a place to store data or run applications. This narrow focus limits its potential and reduces ROI.

What to Do Instead

Cloud should be viewed as a strategic business enabler. Organizations that have thrived in the cloud era—from startups to global giants—have used it to build entirely new services and reimagine customer experiences. Instead of just “lifting and shifting” legacy systems, re-evaluate business models and workflows through the lens of cloud scalability, agility, and innovation.

Key Takeaway

Embed cloud into overall business strategy. Align it with innovation, customer experience, automation, and speed-to-market goals.

Mistake 2: Overlooking Security Responsibilities

Why It Happens

Many businesses assume that cloud providers like AWS, Google Cloud, or Microsoft Azure are fully responsible for all aspects of security.

What to Do Instead

While providers do offer robust security frameworks, clients are responsible for securing their own data, configurations, and access controls. Missteps—such as misconfigurations or employee negligence—can lead to major vulnerabilities.

Real-World Example

A 2023 attack on Microsoft’s Azure platform revealed how hackers could exploit one compromised account to access others across separate organizations, highlighting shared infrastructure risks.

Key Takeaway

Adopt a security-first culture. Train teams to recognize phishing attacks, implement access controls, and use AI to monitor and prevent security breaches.

Mistake 3: Treating Cloud and AI as Separate Strategies

Why It Happens

Organizations often pursue cloud computing and AI as unrelated initiatives, leading to disjointed systems and missed opportunities.

What to Do Instead

Recognize the symbiotic relationship between cloud and AI. Cloud provides the computing power, data access, and scale that AI requires. AI, in turn, optimizes cloud usage and enhances services.

Industry Examples

• Netflix combines AI analytics with cloud to personalize streaming.

• Amazon leverages AI and cloud for dynamic e-commerce experiences.

• Pfizer accelerates drug discovery using AI on cloud-based platforms.

Key Takeaway

Develop a unified cloud-and-AI strategy. Integrate teams and tools to drive smarter, data-driven decision-making and innovation.

Mistake 4: Underestimating the Cost of AI Workloads in the Cloud

Why It Happens

AI services, especially training large models or running constant inference, require significant compute power—leading to unexpected and growing costs.

What to Do Instead

Monitor usage closely, establish usage limits, and forecast cloud spending with AI-specific budgeting. Choose scalable and cost-efficient infrastructure tailored to your AI needs.

Real-World Example

Stability AI, a startup known for its image generation tool Stable Diffusion, reportedly ran into financial strain due to excessive cloud costs.

Key Takeaway

Implement cost governance policies. Regularly audit AI resource use and adopt cost-efficient cloud services or hybrid models where appropriate.

Mistake 5: Getting Locked into One Cloud Provider

Why It Happens

Vendor-specific tools, contracts, and pricing can create long-term dependency—making it hard to switch or diversify when business needs change.

What to Do Instead

Pursue a multi-cloud or hybrid cloud strategy. Build systems that are cloud-agnostic, modular, and portable across different platforms.

Supporting Data

A Gartner survey found that 95% of businesses feel they are either locked in or would face difficulty changing providers.

Key Takeaway

Use abstraction layers, open-source platforms, and containerized architecture to retain flexibility and avoid lock-in.

Conclusion

Cloud computing remains one of the most powerful assets in the modern business toolkit—but only when approached strategically and managed with care. In 2025, the organizations that succeed will be those that:

• View cloud as a business enabler, not just infrastructure

• Build a culture of security across teams

• Align AI and cloud strategies from day one

• Monitor and manage AI-related cloud costs

• Maintain flexibility with multi-cloud and vendor-neutral tools

Avoiding these common mistakes isn’t just a way to protect your bottom line—it’s how you future-proof your digital transformation efforts.

FAQs

What is the most common cloud computing mistake businesses make?

Treating the cloud solely as a technical upgrade rather than a strategic business enabler is one of the most common and costly mistakes.

Why is AI strategy important to cloud adoption?

AI and cloud work best together—AI relies on cloud infrastructure for scalability and data access, while cloud providers use AI to improve performance and user experience.

How can I prevent getting locked into a single cloud vendor?

Use modular architecture, open-source tools, and consider a multi-cloud approach. This gives you flexibility and leverage when negotiating or switching providers.

Are cloud services automatically secure?

No. While cloud providers offer strong security, users are responsible for securing their data, access points, and configurations.

How can businesses manage cloud costs for AI workloads?

Monitor resource usage, forecast costs, set budgets, and explore cost-efficient alternatives like edge computing or hybrid cloud solutions.

As artificial intelligence continues to reshape industries, a new role is emerging at the intersection of technology, strategy, and innovation: the AI Product Manager. This isn’t just a passing trend—it’s a reflection of how integral AI is becoming in the development and optimization of modern products.

To succeed in this evolving role, AI product managers must do more than understand traditional product lifecycles. They’ll need to navigate complex AI and machine learning (ML) systems, evaluate performance metrics, and ensure responsible, ethical deployment of technology. That requires a unique blend of technical acumen, data fluency, and cross-functional leadership.

Core Competencies of Future-Ready AI Product Managers

To lead in this space, product managers should develop proficiency in the following key areas:

• AI-Specific Technical Competence – Understanding how models are built, trained, tested, and deployed.

• Data Science Knowledge – Ability to interpret data, partner with data teams, and drive data-informed decisions.

• Model Performance Evaluation – Knowing how to measure, optimize, and communicate model performance.

• Ethics, Bias, and Regulation – Staying informed about legal and societal implications of AI systems.

• Education and Influence Management – Evangelizing AI within the organization and aligning diverse stakeholders around AI initiatives.

Why Every Product Manager Needs AI Skills

Just as “internet product managers” were once a niche, only to evolve into the standard model of digital product management, AI is on track to become a core element of every product manager’s toolkit.

According to Forrester, AI will become so embedded in product development that PMs who lack foundational AI knowledge may find themselves at a disadvantage. Generalist product managers won’t need to be AI engineers, but they will need to understand how to integrate AI into product features, make informed trade-offs, and iterate based on user feedback and AI performance.

How Product Leaders Can Prepare Their Teams

Leadership plays a crucial role in preparing product teams for the AI-powered future. That means more than just encouraging learning—it means building a culture that values experimentation, continuous education, and hands-on practice.

Here’s how leaders can start:

• Offer AI literacy programs tailored for non-technical professionals.

• Create hands-on experiences through internal projects, hackathons, or partnerships with AI teams.

• Provide access to online, interactive courses and workshops that blend theory with application.

• Recognize and reward team members who take the initiative to upskill.

Conclusion

AI isn’t just a buzzword—it’s rapidly becoming a foundational element of modern product strategy. As such, the AI product manager role is not only growing but evolving into a key pillar of the future workforce.

Product leaders who invest in upskilling today will set their teams up for long-term success, ensuring they’re not only keeping up with the market but helping to define it.

FAQs

Q: What skills do AI product managers need?
A: They should develop AI-specific technical knowledge, data science fluency, the ability to evaluate AI performance, a strong understanding of ethics and regulation, and the ability to educate and influence across teams.

Q: Why is AI knowledge becoming essential for all product managers?
A: AI is becoming a standard part of digital products. PMs will need to understand how to apply AI responsibly and effectively to remain competitive and meet evolving customer expectations.

Q: How can leaders support their teams’ AI/ML development?
A: Provide access to literacy courses, create hands-on learning opportunities, encourage cross-functional collaboration, and foster a culture of curiosity and continuous learning.