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&nbsp;Harvey Nash&rsquo;s CIO Voices brings together technology leaders to explore the trends, challenges, and opportunities shaping the future of enterprise technology. This month&rsquo;s discussion tackled a topic that many organizations are actively navigating but few have fully mastered: the journey from traditional business intelligence to AI-driven decision-making.
To unpack what that transition really looks like, we spoke with Tad Rhodes, Founder of TownVue, Sanjay Bhutiani, Founder of Dreaming Tree AI, and Craig Leland, Market Director at Harvey Nash. Their perspectives revealed a common theme throughout the discussion: while AI may dominate the headlines, successful machine learning initiatives are rarely about the models themselves. They are about data, architecture, organizational design, and perhaps most importantly, people.
The conversation raised an important question: what does it actually take to operationalize machine learning at scale?
The answer, it turns out, is far more complex than simply adding AI to an existing technology stack.
The Shift from Reporting to Discovery
For years, business intelligence has served as the foundation for enterprise decision-making. Dashboards, reports, and analytics platforms have helped leaders understand what happened, why it happened, and where performance could improve.
But AI introduces a fundamentally different dynamic.
According to Tad Rhodes, the distinction comes down to the difference between presenting information and uncovering insights.
"BI is really about presenting data. It tells you what already happened. AI is more about interrogating it, surfacing the questions you didn't even know you should have been asking."
That observation captures one of the most significant shifts organizations are experiencing today. Traditional BI assumes that humans will interpret information and decide what actions to take. AI changes that relationship by accelerating analysis, identifying patterns automatically, and uncovering opportunities that may never have surfaced through conventional reporting.
Sanjay Bhutiani sees this transition through a slightly different lens. In his view, many organizations believe they are moving toward AI-driven decision-making when, in reality, they are simply layering AI tools onto existing BI environments.
"The honest answer is that most organizations are not really making the transition."
He argues that AI compresses the decision-making loop and fundamentally changes how organizations process information. Systems designed to support executive dashboards are not necessarily designed to support machine learning workflows.
Taken together, their perspectives reveal an important truth. The move from BI to AI is not simply a technology upgrade. It represents a shift from reporting what is known to discovering what is not yet visible.
Why AI Adoption Is More Human Than Technical
One of the strongest themes throughout the discussion was that technology itself is rarely the biggest barrier to AI adoption.
The real challenge is organizational change.
For Rhodes, adoption begins with demonstrating value at an individual level. He described what happens when employees see AI solve a meaningful problem for the first time.
"It's the moment an employee watches AI answer a tough question well, and their eyes of understanding open."
That moment of discovery often changes the conversation entirely. Skepticism gives way to curiosity, and curiosity becomes experimentation. Over time, those small moments of adoption spread across teams and departments.
Bhutiani agrees that culture ultimately determines success, but he believes the challenge runs deeper than individual enthusiasm. Organizations must rethink how they structure teams, measure performance, and reward innovation.
He argues that many enterprises still evaluate technology organizations based on traditional metrics such as uptime, ticket resolution, and project delivery. Yet none of those measurements capture the value AI is intended to create.
Instead, organizations need to focus on outcomes such as decisions accelerated, capacity unlocked, and time recaptured.
Perhaps most importantly, employees must be rewarded for changing how work gets done.
As Bhutiani noted, people are unlikely to redesign their workflows around AI if their performance reviews continue to reward the old way of working.
The technology may be new, but the challenge is a familiar one. Organizations that successfully operationalize AI are often the ones that successfully operationalize change.
The Foundation Beneath Every Successful AI Initiative
If there was one area where contributors aligned most strongly, it was on the importance of data quality, governance, and architecture.
In fact, both Rhodes and Bhutiani argued that these elements ultimately determine the ceiling of what AI can achieve.
Rhodes offered a pragmatic perspective, noting that organizations often become trapped between two extremes. On one side is the pursuit of perfectly clean data. On the other is the reality of messy, incomplete, and inconsistent information.
"Perfectly clean data is a fallacy, but you obviously can't run on a swamp either."
His point is an important one. Successful organizations do not wait until their data is perfect before moving forward. Instead, they focus on creating data that is trustworthy enough to support meaningful decisions.
Bhutiani reinforced this idea from a governance standpoint.
"An organization with mediocre data running sophisticated models will get mediocre outputs, confidently delivered."
It is a warning many leaders need to hear. AI systems can make weak data appear convincing. The sophistication of the model does not compensate for weaknesses in the underlying information.
Governance plays an equally important role. Without clear ownership, auditability, and accountability, machine learning initiatives can quickly create operational and compliance risks that become difficult to manage later.
The organizations succeeding with AI at scale are not treating governance as a final checkpoint. They are treating it as an integral part of the process from the very beginning.
Data Architecture: The Hidden Constraint on AI
While discussions about AI often focus on models, copilots, and automation, our contributors repeatedly returned to a less glamorous topic: data architecture.
Both Rhodes and Bhutiani described architecture as the hidden factor that determines whether AI initiatives accelerate or stall.
Rhodes referred to it as the "silent ceiling."
Organizations often discover this limitation only after investing heavily in AI tools. Data exists across multiple systems, records cannot be connected, and critical information remains trapped in silos.
A customer in a CRM system may appear differently in billing, support, and operational platforms. Without a consistent way to connect those identities, AI lacks the context necessary to generate meaningful insights.
Bhutiani echoed this concern.
"I have watched organizations invest seven or eight figures in AI tooling on top of data architecture that fundamentally cannot support what they are asking it to do."
The result is often predictable. The technology functions correctly, but the outputs remain unreliable because the underlying architecture cannot deliver complete, timely, and connected information.
Three architectural principles emerged repeatedly throughout the discussion:
<ul style="margin-top: 0in;" type="disc">
<li class="MsoNormal" style="mso-list: l0 level1 lfo1; tab-stops: list .5in;">Unified rather than siloed data.</li>
<li class="MsoNormal" style="mso-list: l0 level1 lfo1; tab-stops: list .5in;">Real-time accessibility rather than delayed batch processing.</li>
<li class="MsoNormal" style="mso-list: l0 level1 lfo1; tab-stops: list .5in;">Structures that allow AI systems to query and reason across information seamlessly.</li>
</ul>
The lesson is clear. AI does not reward organizations that simply possess large amounts of data. It rewards organizations whose data can actually work together.
Centralizing AI Without Slowing Innovation
As AI adoption expands, many leaders face an important question: should AI capabilities be centralized or distributed across the business?
Interestingly, both contributors challenged the framing of the question itself.
For Rhodes, the goal should not be determining who owns AI. The goal should be enabling teams to solve problems.
"Ask, collect, structure, expose."
His approach focuses on understanding what teams need, building the right foundation, and making information accessible without creating unnecessary friction.
He also offered an important observation about so-called shadow AI. Employees often turn to AI tools independently because they are trying to solve real business problems. Rather than viewing this as a threat, organizations should view it as a signal that unmet needs exist.
Bhutiani approached the issue from an enterprise governance perspective.
His recommendation is straightforward: centralize governance, infrastructure, and shared capabilities. Distribute application and innovation.
"The organizations that get this wrong centralize everything and slow themselves down, or distribute everything and accumulate hidden risk."
That balance may ultimately become one of the defining operating models of the AI era. The most effective organizations will likely maintain centralized standards while empowering individual teams to apply AI in ways that create domain-specific value.
Building Teams for the AI Era
The discussion concluded with one of the most pressing questions facing technology leaders today: what does great AI talent actually look like?
The answer depends largely on who you ask.
For Rhodes, curiosity may be the most important hiring characteristic of all.
While technical fundamentals remain important, he believes AI is dramatically accelerating how quickly people can acquire new skills. As a result, individuals who are adaptable, curious, and eager to learn often outperform those relying solely on existing expertise.
"A curious person with the fundamentals can pick up domain context fast and let AI handle the rest."
Bhutiani takes a different but equally compelling view. He places greater emphasis on domain expertise, arguing that technical skills can often be developed faster than deep business understanding.
An engineer who lacks business context may build technically impressive solutions that fail to solve meaningful problems. Conversely, a domain expert who develops technical capabilities can often create solutions that directly impact business outcomes.
The most successful teams, he argues, combine senior technical architects with domain experts who are capable of growing into hybrid roles over time.
Craig Leland added a practical hiring perspective that many organizations would benefit from adopting.
Rather than starting with job descriptions, he encourages organizations to begin by defining outcomes.
"The most common advice I provide to clients is first start by defining the outcomes sought after and not just roles or responsibilities."
He also highlighted a growing challenge in the market. Organizations unfamiliar with AI hiring often add excessive interview stages in an attempt to reduce hiring risk. Unfortunately, this frequently drives away the very candidates they hope to attract.
A clear evaluation process, well-defined objectives, and disciplined decision-making often produce better outcomes than lengthy hiring cycles.
The Real Work of Operationalizing AI
After listening to Rhodes, Bhutiani, and Leland, one conclusion stands out above all others.
Operationalizing machine learning is not primarily a technology challenge.
The organizations creating meaningful value from AI are investing just as heavily in architecture, governance, operating models, talent, and culture as they are in algorithms and tools.
Machine learning succeeds when data is trusted. It succeeds when systems are connected. It succeeds when employees understand how to use it. And it succeeds when organizations align incentives, processes, and people around new ways of working.
The transition from BI to AI is not about replacing dashboards with models. It is about building an environment where intelligence can move from observation to action.
For many organizations, that journey is only beginning.
A huge thank you to Tad Rhodes, Sanjay Bhutiani, and Craig Leland for sharing their perspectives. Their insights continue to make CIO Voices a valuable forum for technology leaders navigating the future of enterprise innovation.

From BI to AI: Operationalizing Machine Learning

As artificial intelligence continues to reshape the hiring landscape, standing out in a competitive job market has never been more challenging or important. In the US tech sector, employers are increasingly relying on Applicant Tracking Systems (ATS) and AI-driven screening tools to manage high volumes of applications.
While these technologies streamline recruitment, they also raise the bar for job seekers. A well-crafted resume must appeal not only to hiring managers but also to algorithms.
So, what does a great resume look like in 2026? We spoke with Ketzia Woodard, Senior Technical Recruiter, to find out.
1. Keep your resume simple and ATS-friendly
With AI playing a significant role in the hiring process, simplicity is key. Overly designed resumes with complex graphics, columns, and unconventional fonts can confuse screening systems and reduce your chances of progressing.
&ldquo;Right now, because of market changes and the volatility created by AI, I see a lot of jazzy resumes with extra formatting,&rdquo; says Woodard. &ldquo;One of the most important things to keep in mind is to keep it simple.&rdquo;
Use clear headings, standard fonts, and a straightforward layout to ensure your resume is readable by both technology and recruiters.
Top tips:
<ul style="margin-top: 0in;" type="disc">
<li class="MsoNormal" style="mso-list: l1 level1 lfo3; tab-stops: list .5in;">Use standard fonts such as Arial, Calibri, or Times New Roman.</li>
<li class="MsoNormal" style="mso-list: l1 level1 lfo3; tab-stops: list .5in;">Avoid graphics, images, and text boxes.</li>
<li class="MsoNormal" style="mso-list: l1 level1 lfo3; tab-stops: list .5in;">Stick to clear section headings like Experience, Skills, and Education. </li>
<li class="MsoNormal" style="mso-list: l1 level1 lfo3; tab-stops: list .5in;">Submit your resume as a Word document to ensure the recruiter has easy access to edit and copy. </li>
</ul>
2. Showcase recognizable experience, skills, and measurable impact
Well-known organizations can help signal credibility and professional rigor, and hiring managers may use brand recognition as a quick reference point when assessing candidates.
However, brand names aren&rsquo;t the only way to demonstrate value. In-demand technical skills, platforms, and measurable achievements can be just as powerful, especially if you&rsquo;ve worked with widely used tools like Salesforce or delivered meaningful results in smaller or less recognizable organizations.
Leading with a mix of recognizable companies (where applicable), core skills, and clear outcomes helps hiring managers quickly understand the scale, complexity, and relevance of your experience.
This approach also supports ATS optimization, as many systems are designed to identify both employer names and specific skills or technologies.
Your resume should focus on results, not administrative or structural details. Hiring managers are far more interested in what you achieved than how you were contracted or paid.
Whether you worked as a consultant, contractor, or through an LLC, emphasize the client or organization you supported and the value you delivered. Recognizable brands can act as powerful social proof, but they should sit alongside clear evidence of impact.
Remember, hiring managers typically spend very little time reviewing each resume, so it&rsquo;s important to make those moments count by highlighting measurable outcomes and real-world results.
Top tips:
<ul style="margin-top: 0in;" type="disc">
<li class="MsoNormal" style="mso-list: l3 level1 lfo1; tab-stops: list .5in;">Highlight well-known companies where relevant, but don&rsquo;t rely on them alone. </li>
<li class="MsoNormal" style="mso-list: l3 level1 lfo1; tab-stops: list .5in;">Call out key technical skills and platforms (e.g., Salesforce, AWS, Python) prominently. </li>
<li class="MsoNormal" style="mso-list: l3 level1 lfo1; tab-stops: list .5in;">Lead with the client or company name when possible. </li>
<li class="MsoNormal" style="mso-list: l3 level1 lfo1; tab-stops: list .5in;">Clearly indicate contract roles with titles such as Senior Project Manager (Contract). </li>
<li class="MsoNormal" style="mso-list: l3 level1 lfo1; tab-stops: list .5in;">Quantify achievements with metrics and results. </li>
<li class="MsoNormal" style="mso-list: l3 level1 lfo1; tab-stops: list .5in;">Showcase the scale of your responsibilities, such as budgets, teams, or revenue impact.</li>
</ul>
4. Adopt a skills-first approach &nbsp;&nbsp;&nbsp;&nbsp;
We find it's best to present your skills and experience to support shaping the best first impression in the hiring process. Presenting your credentials strategically ensures your skills remain the focus. 
We recommend that applicants remove all potential distractions, such as eliminating their graduation dates and early career shifts. After several years in the workforce, what you&rsquo;ve accomplished matters far more than when you earned your degree.
This approach helps position you as current, competitive, and aligned with today&rsquo;s market.
Top tips:
<ul style="margin-top: 0in;" type="disc">
<li class="MsoNormal" style="mso-list: l2 level1 lfo2; tab-stops: list .5in;">Omit graduation dates unless you are a recent graduate. </li>
<li class="MsoNormal" style="mso-list: l2 level1 lfo2; tab-stops: list .5in;">Highlight recent certifications and technical skills.</li>
<li class="MsoNormal" style="mso-list: l2 level1 lfo2; tab-stops: list .5in;">Focus on achievements from the past 5-10 years. </li>
<li class="MsoNormal" style="mso-list: l2 level1 lfo2; tab-stops: list .5in;">Ensure your resume reflects modern tools and methodologies. </li>
</ul>
5. Curate your experience for relevance
A resume is not a career history, it&rsquo;s a strategic marketing document. Every line should demonstrate your ability to solve a specific problem for an employer.
Tailor your content to align with the role you&rsquo;re pursuing, prioritizing recent and relevant experience. Removing outdated technologies and redundant information ensures your strengths remain front and center.
As Woodard&rsquo;s insights reinforce, clarity and relevance are essential in a fast-evolving, AI-driven job market.
Top tips:
<ul style="margin-top: 0in;" type="disc">
<li class="MsoNormal" style="mso-list: l0 level1 lfo4; tab-stops: list .5in;">Prioritize relevant experience over trying to tailor your resume for every application.</li>
<li class="MsoNormal" style="mso-list: l0 level1 lfo4; tab-stops: list .5in;">Remove outdated skills and technologies. </li>
<li class="MsoNormal" style="mso-list: l0 level1 lfo4; tab-stops: list .5in;">Highlight modern tools and methodologies. </li>
<li class="MsoNormal" style="mso-list: l0 level1 lfo4; tab-stops: list .5in;">Summarize older roles to show career progression without clutter. </li>
</ul>
Stand out and secure your next opportunity
AI is transforming recruitment, but it hasn&rsquo;t replaced the human element. A standout resume in 2026 strikes the right balance between technological optimization and authentic storytelling.
By keeping your resume simple, highlighting credible experience, and focusing on measurable impact, you can position yourself for success in the US tech hiring market.
In an age of algorithms, clarity, relevance, and results remain your greatest competitive advantage.
<span lang="EN-GB" style="font-size: 12.0pt; line-height: 115%; font-family: 'Aptos',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: Aptos; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: Arial; mso-bidi-theme-font: minor-bidi; mso-ansi-language: EN-GB; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;">Ready to take the next step? <a href="https://careers.harveynashusa.com/jobsearch/?_gl=1*1jbpdxp*_ga*NzQ3OTIxMzEzLjE3NTk5MzY3ODc.*_ga_LTK3MRX9GL*czE3NzYyNDYxNTQkbzQxJGcwJHQxNzc2MjQ2MTU0JGo2MCRsMCRoMA..">Explore our current jobs</a> and discover opportunities that match your skills and ambitions.

Top tips to make your resume stand out in the US tech job market

-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; U.S. tech workers the most likely to work four or even five days in the office
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; But Americans are the happiest tech professionals globally and the most likely to have received a promotion and pay rise of 10% of more
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; High levels of retention compared to global counterparts
Although twice as many tech professionals in the U.S. are mandated to attend the office four/five days a week compared to any other country (34% vs 17%), a new global study finds that they are the happiest with their role amongst the countries surveyed, and are the most likely to have received a pay rise of 10% or more (38% vs 25%) and/or a promotion (24% vs 22%) in the last year.
The ability to work from home appears to matter less to U.S. tech professionals than those in other countries, with only 38% considering it important compared to 52% globally, and only 34% saying they wouldn&rsquo;t consider a role that didn&rsquo;t have some degree of remote working compared to the global figure of 50%.
The Harvey Nash Tech Talent &amp; Salary Report, that surveyed over 3,600 technology professionals globally (629 in the U.S.), also found that high levels of promotion and financial reward in the U.S. tech industry are driving improved retention levels, as U.S. tech professionals are more likely to have been in role 3-5 years than those in any other country (26% vs 20%).&nbsp;
There are other favorable factors supporting job satisfaction across the U.S. tech sector: more U.S. professionals report that they have seen reduced workloads than in any other country (21% vs 13%) and they generally feel less under-resourced than their global counterparts. Tech professionals based in the U.S. also feel the most supported with both their physical (37% vs 34%) and mental wellbeing (40% vs 35%).
Jason Pyle, Global COO &amp; President of Harvey Nash USA &amp; Canada said:
&ldquo;At a time when much of the conversation around the tech labor market has been pessimistic, our U.S. data tells a far more encouraging story. Many tech professionals are feeling valued, rewarded and supported at work &ndash; and that&rsquo;s translating into higher levels of job satisfaction and retention.
What stands out is that U.S. organizations are successfully linking investment in people with business outcomes. Competitive pay rises, promotions and a stronger focus on wellbeing are clearly resonating, even in more office‑based environments. For employers, this reinforces an important point: engagement and reward still matter more than any single policy on where work gets done.
However, this shouldn&rsquo;t lead to complacency. The most sought‑after, niche skills remain highly mobile, and expectations continue to rise. Organizations that want to attract and retain specialist tech talent need to continue to evolve their employee proposition to compete.&rdquo;
Across the 629 U.S technologists surveyed, The Harvey Nash Tech Talent Report also found the following:
Push and pulls factors for technologists:
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Push factors - For over three quarters (43%) of technologists in the U.S., pay retains its top position as the primary reason for considering leaving their role, with the culture of their organization second (35%) and career progression not far behind (33%).
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; The most important pull factors for men and women - Female technologists in the U.S. are slightly more concerned with factors such as paid time off, flexible working hours and retirement benefits than their male peers. In turn, men are more interested in opportunities for career progression and the nature of the projects.
Reskilling and upskilling in AI:
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; AI is taking my job &ndash; More U.S. respondents than in any other country feel that their role is under threat from AI (49% vs 43%).
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Experimenting in AI &ndash; Almost two thirds (64%) report being given access to AI tools and platforms, and 34% are given dedicated time to experiment and learn.
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Waiting for training or left to self-learn &ndash; U.S. organizations are the least likely to provide employees with internal training programs (51% vs 69% globally), and a quarter of technologists (24%) are expected to either self-learn or are waiting on formal training.&nbsp;
Good tech leadership:
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Great leaders - When it comes to defining what makes a great tech leader, more than half (54%) of respondents say that great communication remains key, and 43% value their leader&rsquo;s ability to create a positive culture within the team.
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Deep understanding of tech &ndash; Technologists in the U.S. also continue to value their leaders having a deep understanding of technology, with almost half (46%) making this one of their top three leadership traits. 1 in 8 feel that a lack of technology understanding amongst leaders is a huge barrier to delivering their tech goals, with a quarter (25%) rating it as a significant issue. IT Strategy also appeared on the top list of leadership qualities for the first time in several years of reporting &ndash; a sign that technologists may be concerned about the need for clear strategic and business case direction in the age of AI.&nbsp;
The Inclusive workplace:
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Tech sector not doing enough - While just under a half (48%) think the tech sector is doing enough to support female participation in technology, almost one third of female respondents (30%) actively disagree, wanting to see more concrete action &ndash; much higher than amongst men where only a fifth (19%) feel this way.
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Decrease in focus and investment in DEI &ndash; Although almost all (80%) think their organization does enough to support diversity as a whole in the workplace, around 2 in 10 technologists report a decrease in focus and investment in DEI over the last two years.
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Sense of purpose vs. DEI - Almost all (87%) tech professionals in the U.S. stated that an organization&rsquo;s sense of purpose is important when selecting a new role, but only 61% think their approach to DEI holds the same weight. This rises to 71% when the respondent identifies as any ethnicity other than white; 46% of white males agree.&nbsp;
Simon Crichton, CEO of Harvey Nash concluded:
&ldquo;With technology moving so fast, and AI beginning to change the game, technology leaders have many plates to spin. Tech professionals are looking to their leaders with an expectation of clear strategic direction, fair reward and a supportive environment in which they can build fulfilling careers. A standout feature this year is tech team members&rsquo; concern that IT strategy should be clear. There are undercurrents of worry about the impact of AI &ndash; even if it also presents career opportunities. The best technology leaders are those that integrate the development and deployment of AI into a coherent overall strategy that continues to have the skills and abilities of tech professionals at its heart.&rdquo;&nbsp;
-ENDS-
About the report
The <a href="https://www.harveynashusa.com/research-whitepapers/tech-talent-and-salary-report-2026">Harvey Nash Global Tech Talent &amp; Salary Report </a>is based on a survey of over 3,646 technology professionals globally (including 1,394 in the UK and 629 in the US). The survey took place between 4th November 2025 and 26th January 2026. This report is part of a suite of reports and surveys that Harvey Nash publish annually, including its highly respected Digital Leadership Report, which was launched in 1998 and is the world&rsquo;s largest and longest running survey of senior technology leaders.
&nbsp;
To request a full copy of the results, please visit <a href="https://www.harveynashusa.com/research-whitepapers/tech-talent-and-salary-report-2026">https://www.harveynashusa.com/research-whitepapers/tech-talent-and-salary-report-2026.</a>
About Harvey Nash
Harvey Nash is a specialist global technology recruitment firm that connects the world's most innovative companies with the technology talent they need to succeed. For over 35 years, Harvey Nash has been a pioneer and leading voice in the global technology space, having long term strategic partnerships with blue chip customers. With offices across multiple continents, including North America, the UK. Europe and Asia, Harvey Nash experienced in partnering with organizations on their specialized technology talent requirements including Cyber, DevOps, AI &amp; Automation, Data, Cloud and Software Engineering.
For further information visit <a href="https://www.harveynashusa.com/">https://www.harveynashusa.com/</a>
Media Contacts: Michelle Thomas
Harvey Nash
<a href="mailto:michelle.thomas@harveynash.com">michelle.thomas@harveynash.com</a>
+44 (20) 7333 2677

In-office culture rewards U.S. tech professionals

<h1 class="MsoNormal">How to protect skills pipelines in the age of AI</h1>
AI is scaling, but&nbsp;skills pipelines are under pressure, and digital leaders&nbsp;are&nbsp;feeling&nbsp;both realities at once. Technology teams are&nbsp;delivering&nbsp;faster with AI embedded across engineering,&nbsp;testing&nbsp;and delivery. Yet underneath that acceleration, new capability risks are emerging.&nbsp;
As AI takes on more foundational work, the pathways technologists traditionally used to learn, practice, and progress are being reshaped. If workforce models are not redesigned alongside technology adoption, organizations risk creating a future where productivity rises but deep technical capability thins out.&nbsp;
This tension sits at the heart of our&nbsp;<a href="https://www.harveynashusa.com/tech-flix-the-ai-paradox" target="_blank" rel="noopener">recent Tech Flix film, &lsquo;The AI Skills Paradox&rsquo;: AI is Scaling, Skills are Not&rsquo;,</a> which explores the widening gap between the rapid rise of AI and the skills needed to harness it. It brings together perspectives from industry,&nbsp;education&nbsp;and government to examine how organizations can prepare their people for the future of technology.&nbsp;
For a frontline operational view, we also spoke with Nash Squared CIO Ankur Anand, whose perspective reflects how these shifts are playing out inside technology teams today.&nbsp;
<h1 class="MsoNormal">The digital talent paradox inside tech teams</h1>
AI adoption across technology functions has accelerated rapidly, but workforce readiness is moving at a different pace.&nbsp;
The&nbsp;2025&nbsp;<a href="https://www.harveynashusa.com/research-whitepapers/digital-leadership-report-2025">Nash Squared/Harvey Nash Digital Leadership Report&nbsp;</a>shows that demand for AI capability continues to surge, with AI and machine learning&nbsp;remaining&nbsp;among the fastest-growing skills areas globally. Yet access to talent&nbsp;remains&nbsp;constrained, with digital leaders consistently reporting skills shortages in critical technology disciplines.&nbsp;
This creates a structural paradox.&nbsp;
Organizations are scaling AI delivery while facing persistent gaps in the very skills required to implement, govern and scale it effectively.&nbsp;
Our recent Tech Flix film&nbsp;reinforces this divide, highlighting how AI&nbsp;is advancing faster than workforce preparedness, creating pressure on leaders to rethink how skills are built, not just how technology is deployed.&nbsp;
<h1 class="MsoNormal">The real risk to junior and mid-level roles&nbsp;</h1>
Much of the external narrative focuses on AI threatening entry-level roles. Inside tech teams, the structural pressure&nbsp;(how the&nbsp;shape&nbsp;of a tech team&nbsp;is shifting)&nbsp;is more nuanced.&nbsp;
Development copilots, automation&nbsp;tooling&nbsp;and internal knowledge systems now allow junior engineers to complete tasks that previously&nbsp;required&nbsp;several years of experience.&nbsp;
As&nbsp;CIO of Nash Squared,&nbsp;Ankur explains, early-career productivity is rising sharply. Junior engineers can interpret requirements, generate&nbsp;code&nbsp;and build solutions far earlier in their careers than before.&nbsp;
But the structural impact falls most heavily on the mid-experience layer.&nbsp;
&ldquo;It&rsquo;s creating more risk for the people with mid-level experience&nbsp;compared to the more&nbsp;senior and&nbsp;experienced people&nbsp;as well as the juniors,&rdquo; he notes.&nbsp;
Work traditionally owned by engineers with two to five years of experience is being compressed. It is absorbed upward through AI-augmented senior oversight and downward through AI-enabled junior execution.&nbsp;
This does not remove technology roles, but it does reshape&nbsp;career&nbsp;pathways.&nbsp;
Without intervention, organizations risk narrowing the bridge between entry-level exposure and senior accountability.&nbsp;
<h1 class="MsoNormal">Productivity is rising&nbsp;&amp;&nbsp;experience is evolving&nbsp;</h1>
While risk exists, AI is also transforming what early-career technologists can achieve.&nbsp;
Ankur points to initiatives delivered by engineers with less than a year of experience, including platforms launched within months. These were AI-enabled from&nbsp;inception&nbsp;and did not follow traditional development learning curves.&nbsp;
&ldquo;The impact of AI on fresh talent is&nbsp;very high. Their productivity is now&nbsp;almost as&nbsp;good as people with three to five years of experience.&rdquo;&nbsp;
Access to automation, coding copilots and internal data environments allows early-career engineers to contribute meaningful outputs faster than ever before.&nbsp;
The challenge for&nbsp;digital&nbsp;leaders is ensuring that accelerated output still translates into deep&nbsp;expertise&nbsp;over time.&nbsp;
<h1 class="MsoNormal">How technical skills development is evolving&nbsp;</h1>
AI&rsquo;s impact is not uniform across the technology landscape.&nbsp;
In modern product and platform environments, AI is embedded across delivery, accelerating coding,&nbsp;testing&nbsp;and documentation. Engineers are building AI-enabled solutions from day one, working in automation-rich ecosystems where delivery speed is significantly enhanced.&nbsp;
As Ankur notes,&nbsp;when reflecting on recent initiatives, many programmes today are designed around AI from&nbsp;inception&nbsp;rather than layered in later.&nbsp;
But the picture shifts in high-accountability, experience-led environments.&nbsp;
In domains requiring deep&nbsp;expertise, risk ownership and judgment, human capability&nbsp;remains&nbsp;central.&nbsp;
As&nbsp;Ankur&nbsp;explains,&nbsp;&ldquo;Where you have high-skilled jobs with more experience and manual&nbsp;decision-making&nbsp;required, you&nbsp;can&rsquo;t&nbsp;rely on junior or entry-level talent&nbsp;to take those&nbsp;judgment&nbsp;calls.&nbsp;But&nbsp;you may augment AI for experienced people so they can&nbsp;make&nbsp;faster decisions.&rdquo;&nbsp;
This creates two distinct capability pathways:&nbsp;
<ul style="margin-top: 0in;" type="disc">
<li class="MsoNormal" style="mso-list: l1 level1 lfo1; tab-stops: list .5in;">AI-enabled engineering environments where automation drives productivity&nbsp;</li>
</ul>
<ul style="margin-top: 0in;" type="disc">
<li class="MsoNormal" style="mso-list: l5 level1 lfo2; tab-stops: list .5in;">Experience-led environments where AI augments but does not replace human judgment&nbsp;</li>
</ul>
Digital leaders must build strength across both.&nbsp;
<h1 class="MsoNormal">Governance is now a delivery priority&nbsp;</h1>
As AI accelerates output, governance becomes inseparable from capability.&nbsp;
&ldquo;AI without governance is equally a big risk and can have unexpected consequences,&rdquo; Ankur warns.&nbsp;
AI-generated code can introduce vulnerabilities if deployed without architectural understanding. Security design, data&nbsp;privacy&nbsp;and resilience frameworks are not inherently embedded in AI outputs.&nbsp;
Rapidly developed applications may&nbsp;function but&nbsp;fail under scrutiny or scale.&nbsp;
For digital leaders, this reinforces four operational priorities:&nbsp;
<ul style="margin-top: 0in;" type="disc">
<li class="MsoNormal" style="mso-list: l3 level1 lfo3; tab-stops: list .5in;">Security-first engineering principles&nbsp;</li>
</ul>
<ul style="margin-top: 0in;" type="disc">
<li class="MsoNormal" style="mso-list: l0 level1 lfo4; tab-stops: list .5in;">Responsible AI training&nbsp;</li>
</ul>
<ul style="margin-top: 0in;" type="disc">
<li class="MsoNormal" style="mso-list: l4 level1 lfo5; tab-stops: list .5in;">Human review layers&nbsp;</li>
</ul>
<ul style="margin-top: 0in;" type="disc">
<li class="MsoNormal" style="mso-list: l2 level1 lfo6; tab-stops: list .5in;">Structured governance frameworks&nbsp;</li>
</ul>
Speed must be balanced with safeguard design.&nbsp;
<h1 class="MsoNormal">Five leadership moves to protect the skills pipeline&nbsp;</h1>
Protecting capability does not require slowing AI adoption. It requires designing workforce models that evolve with it.&nbsp;
Drawing on insights from&nbsp;our recent&nbsp;<a href="https://www.harveynashusa.com/tech-flix-the-ai-paradox" target="_blank" rel="noopener">Tech Flix film</a>,&nbsp;<a href="https://www.harveynashusa.com/research-whitepapers/digital-leadership-report-2025" target="_blank" rel="noopener">the&nbsp;2025&nbsp;Digital Leadership Report</a>&nbsp;and Ankur&rsquo;s frontline perspective, five priorities stand out.&nbsp;
<h3 class="MsoNormal">1. Redesign early-career roles&nbsp;</h3>
AI enables juniors to deliver faster, but learning must remain intentional. Exposure to architecture, testing and decision-making must sit alongside AI-enabled execution.&nbsp;
<h3 class="MsoNormal">2. Accelerate mid-level progression&nbsp;</h3>
As delivery work redistributes, mid-career technologists must be supported to move into higher-value domains such as security,&nbsp;platforms&nbsp;and governance.&nbsp;
<h3 class="MsoNormal">3. Embed governance into engineering workflows&nbsp;</h3>
Governance cannot sit outside delivery. Secure design, AI&nbsp;oversight&nbsp;and risk accountability must be built into day-to-day development.&nbsp;
<h3 class="MsoNormal">4. Build dual capability pathways&nbsp;</h3>
Leaders must invest in both AI-enabled product skills and AI-augmented legacy&nbsp;expertise&nbsp;to sustain transformation.&nbsp;
<h3 class="MsoNormal">5. Design blended operating models&nbsp;</h3>
Future tech teams will combine AI-enabled early talent, experienced engineers, governance&nbsp;capability&nbsp;and platform leadership rather than flattening structures entirely.&nbsp;
<h1 class="MsoNormal">The capability question facing digital leaders&nbsp;</h1>
AI is already reshaping how technology teams&nbsp;operate.&nbsp;It&rsquo;s&nbsp;accelerating junior contribution,&nbsp;redistributing mid-level work, and&nbsp;augmenting senior oversight.&nbsp;
But long-term capability will not build itself.&nbsp;
As Ankur emphasizes, the priority now is workforce design. Leaders must ensure accelerated productivity today still produces the deep technical expertise organizations will depend on tomorrow.

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