Building a future-ready technology strategy: 3 mistakes to avoid

Amidst big industry shifts across AI, cybersecurity, and cloud, it’s hard enough predicting what the future holds, much less how to prepare for it. 

Faye Ellis, Principal Training Architect at Pluralsight and AWS Community Hero, explains how to overcome common challenges to stay ahead and build a future-ready technology strategy.

Uncover all of Faye’s insights, including the trends and skills shaping the future of tech. Watch the on-demand webinar.

“The next phase of AI is . . . focusing more on governance, ethics, and return on investment,” says Faye, “with cloud serving as the key enabler, playing a central role in AI adoption and digital transformation.”

But to see ROI with AI and future technology, organizations need to overcome three common challenges.

Organizations have faced mounting pressure to use AI in any capacity. 

Unfortunately, that’s led many to adopt AI before distinguishing meaningful use cases from hype. Now, as leaders look to drive value with AI, they’re struggling to move from experimentation to production.

“Many organizations are investing heavily [in AI] and receiving little to no return on investment,” said Faye. “Because sometimes what works as a proof of concept may not actually be cost-efficient enough to gain you a real return on investment once you ramp it up into production.”

Learn more about how to prepare for the future of generative AI.

Lack of AI ROI usually isn’t caused by the technology itself, but how organizations apply it. 

“Generally, failed AI initiatives are associated with strategy failures, [such as] not having the right problem, the right data, or the right underlying foundation in terms of core capabilities,” explains Faye.

Before investing in AI and scaling it throughout your organization, identify the problem you want to solve with it. 

64% of leaders say their organization has the infrastructure to comply with and adjust to potential regulatory changes on AI going forward. The real challenge is balancing security, compliance, and ethical responsibility with speed and innovation.

“Avoid over-regulation, which can lead to shadow AI, or under-regulation that creates legal and reputational risk and really undermines trust,” says Faye.

Human oversight is critical to implementing effective AI governance best practices that don't sacrifice speed. Invest in AI governance experts and SMEs who can guide this transformation. If you don’t have the budget for that, upskill existing professionals in governance or IT roles.

“[Don’t set] expectations that are not grounded in reality, like believing that AI can actually replace human expertise or underestimating the human effort that's required to actually verify the AI output. There's still plenty of work for humans to do, maybe more so than ever, and we're not getting decommissioned just yet,” says Faye.

47% of organizations have abandoned projects due to IT skills gaps, and rising hiring costs impede organizations from sourcing skilled external talent. But hiring isn’t your only option.

“What we really find is that upskilling is a lot cheaper and faster than hiring externally,” shares Faye. “But it does require a real commitment.”

Uncover the real cost of hiring for IT roles and why upskilling is better for the future of tech.

Successfully building, implementing, and scaling AI requires more than AI skills. It also requires a strong foundation in cloud, data, and security.

“I’m talking about running AI pilots without cloud maturity under your belt or under-investing in data quality, security, or infrastructure,” says Faye. “Without those things in place, pilots just don't reach production.”

A future-ready technology strategy should focus on two things: business goals and upskilling.

“In my mind, it's really a people strategy,” says Faye. “Successful companies have a strategic plan focused on customer outcomes. . . . Organizations who want to stay on top also need a comprehensive plan for upskilling.”

Strategic planning for technology should include the following steps:

• Define the business goals and problems you want to solve using technology. Use this as a north star for technical decisions, like which cloud provider or which service to use for a particular project.

• Create a plan for continuous upskilling. Treat learning as an ongoing operating model, not a one-off event. Make sure skill development is aligned to real business goals and outcomes.

• Prioritize internal talent development and retention. Create internal mobility and career pathways so people feel valued and in control of their careers.

• Retain institutional knowledge. As roles continue to evolve, leverage mentorships, succession planning, and clear documentation processes to reduce the loss of critical knowledge.

“If you're a leader, a mentor, or a senior engineer, my challenge to you is to make it your mission to identify your own superstars. Think about who in your organization is your next data analysis expert, Python genius, or security specialist. Create development objectives and mentorship programs, and introduce protected learning time,” says Faye.

“One thing we've seen with some of our customers who have been successful is to incentivize people to really develop the expertise that's needed within the organization over the next few years.”

Uncover tips to incentivize upskilling and build a culture of learning—get the Tech Upskilling Playbook.

“AI changes everything,” says Faye. “Organizations with strong foundations will really be the ones that benefit the most. I'm talking about cloud, data, and security, as well as governance . . . and the human-centric skills that really set us apart from machines.”

Uncover all of the tech trends you need to know to build a future-ready strategy and remain competitive in 2026 and beyond. Download the 2026 Tech Forecast and catch all of Faye’s insights in the on-demand webinar.