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Cloud computing has transformed from basic infrastructure into a strategic business capability, yet … More most organizations continue to make costly errors.

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In the past decade, cloud computing has become foundational to the way the world does business. It’s given rise to entirely new categories of services, products and entire business models. From global e-commerce to the cloud AI tools used by millions of small businesses, cloud computing makes it all possible.

This is why the most successful businesses look beyond the cloud as simple IT infrastructure and embrace it as a new strategic capability.

It’s easy to make mistakes, though – and in my work with companies of all shapes and sizes, I see it happen all the time.

When it comes to cloud strategy, I see certain mistakes again and again. So here I’ll overview what I believe are the most common pitfalls businesses will stumble into over the next 12 months, and give some tips on avoiding them.

1. Not Approaching Cloud Strategically

Cloud isn’t just data centers or online storage. If it’s treated as a purely technical exercise in centralizing access to tools and data, we’ll miss out on some of the biggest opportunities.

Garage startups have grown into global titans in the cloud era by using it to create and deliver new products and services that wouldn’t have been possible before. From streaming music, movies and games to work and productivity tools delivered via subscription, to virtual communities and worlds with their own economies and currencies. This involves not simply “lifting and shifting” legacy systems onto shiny new servers but rethinking entire business models. Embedding cloud into broader business strategy and understanding how it enables automation and innovation is key to avoiding this trap in 2025.

2. Overlooking Security

A mistake I frequently see companies make is assuming their cloud provider will take care of all their security needs for them. After all, companies like Google, Amazon or Microsoft must be pretty hard to hack, right? Unfortunately, misconfigurations on the client side or falling victim to one of the 60 percent of corporate data breaches committed by insiders can quickly render all of their sophisticated defenses useless. And cloud architecture itself can also create vulnerabilities, as demonstrated by an attack on Microsoft’s Azure service in 2023. After compromising one Azure business account through social engineering or phishing, hackers were able to “sidestep” into adjacent accounts owned by different businesses and plunder their data, too.

Avoiding the risks associated with cloud involves building a “security-first” culture where best practice is embedded across the organization, from training on how to spot phishing attempts to deploying AI counter-measures against infrastructure attacks.

3. Failing To Align Cloud And AI Strategy

Cloud and AI enjoy a symbiotic relationship. Cloud computing gives AI algorithms scalability, deployability, and access to all the data they need. AI helps cloud services run efficiently, optimize the use of power throughout its vast data centers, and personalize the delivery of service to fit customer needs. It’s frustrating to see many businesses still treating them as siloed technologies, limiting the value of both.

Netflix combined AI viewer analytics with cloud streaming, creating a new business model. Amazon did the same with e-commerce, and pharmaceutical giants like Pfizer have built AI-based drug discovery platforms in the cloud. Bringing these groundbreaking technologies together is critical to reaping the benefits of both.

Failing to take this approach can result in fragmented initiatives that don’t effectively capitalize on the potential of cloud or AI, expensive failures and loss of trust in digital transformation.

4. Not Keeping A Lid On AI Costs

Cloud computing isn’t cheap, and this is doubly true if you’re running AI workloads, with the high resource overheads associated with training and inference. Failing to control costs can easily lead to runaway bills, particularly given the 24/7, always-on nature of most AI services. Stability AI was seen as a huge success among AI companies thanks to pioneering image generation with its Stable Diffusion models. However, it ran into publicized financial difficulties due to the huge bills it was running up with cloud providers. Many other businesses risk similar problems on a smaller scale in 2025, particularly those ramping up AI operations without sufficient budgeting in place.

5. Becoming Locked In

According to a Gartner poll, 95% of businesses say they are currently “locked in” to their existing cloud provider, or that changing providers would be challenging.

Becoming so heavily reliant on any provider’s service that your business depends on them is a bad move. With cloud computing, it could limit your ability to adopt new technologies and pivot to new strategies when opportunities appear.

Avoiding becoming locked into a vendor’s pricing, roadmap, and usage policies is a key reason that many businesses choose to adopt multi-cloud strategies. This can provide the agility needed to shift workloads across different providers as strategic requirements change. Embracing open-source technology, modular architecture, abstraction layers, and virtualization can also help avoid damaging vendor lock-in.

This year, cloud computing will be a more powerful tool than ever before when it comes to driving growth and innovation. For many organizations, it will be integral to their success (or failure) with AI. Working to avoid the errors covered here in your business strategy should be a priority for anyone who doesn’t want to turn opportunity into costly failure.

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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.

Lagging In HR AI And Automation

There are lots of great ways companies can use AI within HR to drive improvements in EX. Did you know, for example, that 54% of respondents to one survey said they had given up on applying for a job they wanted due to poor communication from the employer?

Other opportunities include providing personalized onboarding, reducing administrative work by automating repetitive tasks, engagement tracking and improving many aspects of performance management.

Over-Automating Employee Experience

On the other hand, AI still presents a huge number of challenges, particularly when it’s mixed with humans! And while many companies will make the error of under-investing, just as many will, unfortunately, end up using it in ways that are potentially damaging.

Failing To Offer Personal Development Opportunities

This is critical for both retaining existing employees and attracting new talent. Technology is quickly reshaping industries, but workforces need trained and skilled employees to take advantage of this. Offering career progression planning, upskilling and retraining aimed at empowering them to use technology helps people feel they are investing in their own futures by sticking with a business.

Failing To Measure EX ROI

Investing in EX initiatives without a clear plan or milestones in place for measuring success risks wasting money without delivering tangible benefits.

Neglecting Employee Mental Health And Wellness

Workplace stress and burnout are at an all-time high. In fact, the World Health Organization reports that the US economy loses $1 trillion every year thanks to lost productivity caused by depression and anxiety.

Final Thoughts

Employees are a company’s most important resource, and neglecting EX in 2025 means they will quickly start looking elsewhere. This can be a disaster when business success is more dependent than ever on attracting and retaining the right people!

Conclusion

The message I want to get across is that every business should take a strategic approach to EX, taking care to understand how success or failure will impact goals and overall performance. Invest in staff through training, professional development and wellbeing initiatives, and they will pay you back with loyalty, growth and business success!

FAQs

• What is employee experience (EX)?
  • EX is the sum of all experiences an employee has in a company, including their interactions with colleagues, supervisors, and the organization itself.
• Why is EX important?
  • EX is important because it can directly impact employee productivity, retention, and overall job satisfaction.
• What are some common pitfalls companies make when it comes to EX?
  • Some common pitfalls include lagging in HR AI and automation, over-automating employee experience, failing to offer personal development opportunities, failing to measure EX ROI, and neglecting employee mental health and wellness.
• How can companies improve EX?
  • Companies can improve EX by providing personalized onboarding, reducing administrative work, offering career progression planning, and prioritizing employee mental health and wellness.