As AI continues to reshape the technology landscape, the pressure on engineering and technology leaders is increasing rapidly. Teams are being asked to move faster, deliver more with fewer resources, modernise platforms, integrate AI responsibly and rethink what high performance looks like in a technology organisation. These themes sat at the centre of NearTech Search’s recent Manchester CTO roundtable, where technology leaders came together to discuss the realities of scaling modern tech teams, platform engineering, AI adoption and the changing expectations placed on leadership teams. What became immediately clear throughout the discussion was that the conversation around scaling has fundamentally changed. The focus is no longer purely on hiring growth. Instead, organisations are thinking more carefully about operational efficiency, AI enablement, engineering effectiveness and how to build adaptable teams capable of navigating continuous technological change. Below are the key themes and insights that were touched on during the event. Scaling Is No Longer Defined by Headcount Growth One of the strongest themes from the evening was that many businesses are actively trying to slow hiring growth rather than accelerate it. For years, scaling technology teams often meant increasing engineering headcount as quickly as possible. However, the discussion highlighted how priorities are changing. Technology leaders are now far more focused on improving output, reducing delivery friction and building smaller teams that can operate more effectively. Several attendees discussed how investor expectations and commercial pressures have pushed businesses to think differently about growth. Rather than simply asking how quickly teams can hire, organisations are increasingly focused on how they can achieve more with the teams already in place. One of the most repeated ideas throughout the discussion was the importance of scaling operationally rather than structurally. Leaders spoke about using AI tooling to remove repetitive workloads, improving platform capabilities and enabling engineers to work across broader parts of the stack more independently. A particularly striking comment from the discussion captured this perfectly: “How do we deliver more with the same team?” This mindset is now influencing everything from hiring strategy to platform investment and engineering team design. Key Takeaway: High-growth technology businesses are increasingly prioritising productivity, operational efficiency and smarter tooling over rapid headcount expansion. AI Is Changing What Strong Technical Talent Looks Like Another major area of discussion centred around AI and how it is already reshaping engineering teams. The conversation moved well beyond whether AI will impact technology roles. The consensus in the room was that the impact is already happening. Interestingly, the discussion was not centred around replacing engineers. Instead, attendees focused on how AI is changing the shape of technical capability and what businesses now value most in engineering teams. Several leaders discussed how specialist roles are becoming broader, with adaptability and full-stack capability becoming increasingly valuable. The ability to learn quickly, apply judgement and understand wider business context was repeatedly highlighted as more important than narrow technical expertise alone. One CTO explained that their organisation is increasingly hiring for attitude over aptitude. The reasoning behind this was simple, technical skills can be taught. Curiosity and adaptability cannot. Several attendees also discussed how AI tooling is helping developers deliver faster, prototype more independently and reduce repetitive development tasks. However, there was strong agreement that the best engineers are still those who understand business context, apply sound judgement, solve problems effectively and recognise when AI-generated outputs are incorrect. One of the clearest conclusions from the evening was that AI amplifies strong engineers far more than it replaces them. Key Takeaway: Businesses are increasingly prioritising adaptable, commercially aware engineers who can work effectively alongside AI rather than relying purely on narrow technical specialisation. The Bigger Challenge Is Cultural, Not Technical One of the most interesting discussions throughout the evening focused less on the technology itself and more on the human response to it. Several CTOs spoke openly about the emotional and cultural challenges surrounding AI adoption within engineering teams. Attendees discussed engineers feeling uncertain about how their roles may change, concerned that years of specialist expertise could become less valuable, or hesitant about integrating AI into their workflows. This was particularly noticeable amongst highly experienced professionals who had spent years mastering specific technologies or disciplines. One attendee summarised the sentiment clearly: “Some people feel like this technology is devaluing the years they invested into their craft.” The businesses seeing the strongest progress were not necessarily those with the most advanced tooling. Instead, they were the organisations creating environments where experimentation felt safe, learning was encouraged, and conversations around AI remained transparent. There was strong agreement that organisations cannot simply mandate AI adoption. Leaders must actively support teams through the process and create cultures where continuous learning becomes part of day-to-day operations. Key Takeaway: Successful AI adoption is becoming as much a leadership and culture challenge as it is a technology challenge. Platform Teams Are Becoming Strategic Enablers Platform engineering and DevOps functions were another major focus throughout the discussion. What became clear is that platform teams are no longer viewed purely as infrastructure support functions. Instead, they are increasingly becoming strategic enablers across the wider engineering organisation. Technology leaders discussed how platform teams are helping businesses scale more effectively by improving developer experience, building reusable tooling, introducing governance guardrails, and enabling teams to move faster without compromising quality or security. One CTO described platform engineering as: “A force multiplier for the organisation.” Conversations throughout the evening touched on internal AI tooling, automated testing, governance layers, secure AI gateways, and AI-assisted migrations. Importantly, the focus was not purely on cost reduction. Instead, businesses are concentrating on how platform functions can remove operational friction and create more efficient delivery environments across multiple teams. Key Takeaway: Platform and DevOps teams are increasingly becoming central to enabling scalability, developer productivity, and faster delivery across engineering organisations. Expectations on Technology Leaders Have Increased Significantly Another clear takeaway from the roundtable was that the expectations placed on CTOs and technology leaders have expanded dramatically. Today’s technology leaders are not only expected to oversee engineering delivery. They are also responsible for understanding emerging AI tooling, driving experimentation, educating the wider business, assessing commercial impact, and balancing governance with innovation. One attendee captured this by saying: “The CTO role feels reinvigorated again.” There was a strong feeling throughout the room that technology leadership has moved back to the centre of business strategy. However, with that comes increasing pressure. Leaders are now expected to answer difficult questions around AI strategy, automation priorities, delivery speed, and long-term organisational direction. Several attendees also referenced growing levels of “change fatigue” across organisations as teams attempt to keep pace with the speed of technological advancement. Key Takeaway: Technology leaders are increasingly expected to combine technical expertise with commercial thinking, organisational leadership, and AI strategy. The Businesses Moving Fastest Are Creating Cultures of Experimentation One of the final themes throughout the evening was that the businesses progressing fastest are not necessarily those with the biggest budgets or largest engineering teams. Instead, they are the organisations creating environments where experimentation is encouraged, learning is shared openly, and teams feel empowered to test and adapt quickly. Attendees discussed internal AI workshops, shared prompting sessions, developer-led learning initiatives, and cross-functional experimentation as examples of what this looks like in practice. In contrast, businesses struggling most with adoption were often those where AI usage remained hidden, governance lacked clarity, or teams feared making mistakes. One of the clearest takeaways from the discussion was that AI adoption is becoming just as much a culture challenge as it is a technology challenge. Key Takeaway: The organisations adopting AI most successfully are creating cultures that encourage experimentation, continuous learning, and transparent collaboration. Final Thoughts The Manchester CTO roundtable highlighted just how quickly expectations on technology teams are changing. Scaling modern engineering organisations is no longer purely about hiring growth. It is about building adaptable teams, improving operational efficiency, enabling experimentation, and helping engineers work more effectively through stronger platforms and smarter tooling. At the same time, technology leaders are balancing increasing commercial pressure, organisational uncertainty, governance requirements, and the rapid pace of AI adoption. What became clear throughout the evening is that the organisations progressing fastest are not necessarily the ones with the largest teams or biggest budgets. They are the businesses creating cultures that support learning, experimentation, adaptability, and continuous improvement. At NearTech Search, we continue to work closely with technology businesses navigating these challenges, helping them build high-performing teams across engineering, platform, DevOps, AI, and technology leadership functions. Looking to scale your technology team or strengthen your platform and AI capability? Get in touch with us to explore how we can support your growth journey.

Artificial Intelligence (AI) and Machine Learning (ML) are no longer future-facing investments or isolated innovation projects. They have become core to how modern technology businesses build products, make decisions, and scale efficiently. As organisations move from experimentation into full operational integration, the question is no longer whether to adopt AI, but how quickly they can embed it into their core teams. This change has placed AI and ML talent at the centre of hiring strategies, with demand accelerating as companies compete to turn data into insight, automation into efficiency, and intelligence into a measurable competitive advantage. For high-growth tech businesses, the ability to attract and retain this talent is no longer a differentiator. It is a requirement for staying competitive in an increasingly data-driven market. This blog explores why AI/ML talent is now at the core of high-growth tech teams, and what this means for companies looking to scale effectively in 2026 and beyond. From Experimentation to Operational Dependence The role of AI within organisations has changed dramatically. What was once confined to pilot projects or innovation labs is now embedded into core business functions, from product development and customer experience to finance and operations. According to EY , AI is no longer about isolated use cases. It is about integrating shared intelligence across organisations, where human capability and machine capability work together to improve performance. In a similar vein, Qlik highlights that AI is no longer sitting in a pilot phase. It is already shaping how businesses operate at scale. This creates a very clear hiring need. Businesses now require AI/ML professionals who can build, deploy, optimise, and maintain systems in live environments. Without that capability in-house, many AI initiatives remain stuck at the proof-of-concept stage, never delivering the commercial impact leaders expect. For high-growth companies, this makes AI/ML talent a business-critical function rather than an experimental one. Powering Hyper-Personalisation and Product Differentiation In crowded markets, differentiation increasingly comes down to how effectively a company can use data. AI and ML allow businesses to deliver more personalised user experiences, smarter recommendations, predictive services, and products that improve continuously over time. As explored in this Medium article on AI tools and customer data analytics , AI-driven analytics are changing the way organisations understand customer behaviour. Businesses can now anticipate customer needs, identify patterns more quickly, and create more relevant and bespoke user journeys. This is especially valuable in sectors such as SaaS, fintech, and digital platforms, where customer expectations continue to rise, and differentiation is closely tied to user experience. Similarly, Business Weekly notes that startups embedding AI from day one can gain a significant strategic edge by building smarter, more responsive products that scale alongside user demand. This is not simply about efficiency. It is about creating products that feel more useful, more intelligent, and more adaptable than competing solutions. To do this effectively, businesses need AI/ML professionals who can move beyond theory and create practical, commercial applications. These professionals sit at the intersection of product, engineering, data, and business strategy, making them central to how modern tech teams innovate and compete. Closing the Talent, Data and Integration Gap One of the biggest barriers to successful AI adoption is not access to tools. It is the gap between data availability, technical infrastructure, and the talent needed to connect them. Many organisations already hold huge volumes of valuable data, but without the right expertise they struggle to structure it, govern it, and apply it effectively. According to Motherson Technology , competitive advantage is increasingly driven by proprietary data and the ability to continuously train and refine models against it. That means value does not come from AI alone. It comes from the combination of data, systems, and talent. Qlik also reinforces the importance of trusted, contextual data in making AI work effectively. In practice, this means businesses need people who understand not just models, but the wider ecosystem around them, including data quality, pipelines, infrastructure, governance, and business context. This is why AI/ML talent is becoming so important. High-growth companies do not just need technical specialists who can train models. They need professionals who can bridge the gap between raw data, integrated systems, and real business application. That broader capability is what turns AI from a promising idea into an operational advantage. Enabling Faster and Smarter Decision-Making AI is also reshaping how organisations make decisions. In high-growth environments, speed matters, but speed without accuracy can create costly mistakes, leading to poor perception and lack of credibility on an already saturated market. AI and ML help teams move faster by processing large datasets, identifying trends, forecasting outcomes, and surfacing recommendations in real time. As highlighted by RocketSource , AI can support faster and better-informed business decisions by revealing patterns and opportunities before they affect performance. MS Dynamics World similarly points to AI’s growing influence in finance and operational planning, where the technology is being used to improve efficiency, accuracy, and strategic insight. This shift toward decision intelligence has major implications for hiring. AI/ML professionals are no longer only supporting technical teams behind the scenes. They are increasingly enabling commercial, operational, and leadership teams to make better decisions across the business. In high-growth tech companies, that makes AI/ML capability central not only to product development, but to the wider organisation’s ability to respond quickly and strategically. Managing Complexity, Scale and Risk The more embedded AI becomes, the more important it is to manage the complexity that comes with it. As businesses scale AI systems across products and internal operations, they must also think carefully about governance, risk, explainability, reliability, and compliance. According to Iternal AI , one of the most transformative developments is the growth of agentic AI, where systems can act with greater autonomy and execute tasks more independently. While this opens up huge opportunities for productivity and scale, it also introduces new layers of complexity. This is why responsible AI is becoming an essential part of the conversation. TechCircle argues that responsible AI must be built into enterprise systems from the beginning, not added later as a correction. Businesses need professionals who understand not only model performance, but also governance, fairness, explainability, and long-term trust. For high-growth tech teams, AI/ML talent is therefore not just about innovation. It is also about safe scale. The right people help businesses grow AI capability without creating unnecessary operational or reputational risk. Shifting Talent Models From Headcount to Capability Perhaps the biggest long-term shift is how organisations now think about talent itself. Growth is no longer simply about adding more people. Increasingly, it is about building teams with the right capabilities to generate more output, better insight, and stronger adaptability. Salt highlights that the next phase of scaling is not about adding AI features superficially, but about embedding the capability to use AI in meaningful ways across the organisation. McKinsey similarly points to a changing talent landscape in which AI is redefining what high-value capability looks like. This means hiring strategies are shifting. Businesses are increasingly prioritising: AI and ML expertise strong data literacy cross-functional problem-solving the ability to work effectively with intelligent systems commercial understanding alongside technical knowledge In practical terms, this leads to leaner, more capable teams where AI/ML professionals have an outsized impact. They do not just contribute individual output. They help raise the productivity, intelligence, and adaptability of the wider team. That is what makes them central to high-growth businesses. Prioritising Real-World Impact: From Theoretical Knowledge to Proven ROI As demand for AI and ML talent grows, so too does the expectation around what “good” looks like. It is no longer enough for candidates to demonstrate theoretical understanding of models, algorithms, or frameworks. High-growth tech businesses are increasingly prioritising individuals who can show clear evidence of impact, specifically, their ability to take models from concept to production and deliver measurable business outcomes. In practice, this means hiring teams are looking for: Experience deploying models into live environments Evidence of models that have been maintained, scaled, or optimised over time Clear examples of how AI initiatives have driven revenue, efficiency, or user growth The ability to translate technical work into commercial value As highlighted by EY and Qlik , the real competitive advantage of AI lies not in building models, but in applying them effectively within a business context. Organisations that succeed are those that move beyond experimentation and focus on ROI-driven implementation. This change is also changing how candidates are assessed. Rather than focusing purely on academic knowledge or theoretical problem-solving, hiring processes are increasingly centred on real-world case studies, production experience, and tangible outcomes. For high-growth teams, this distinction is critical. AI/ML professionals who can demonstrate proven impact reduce risk, accelerate delivery, and contribute to faster, more confident decision-making. Ultimately, the most valuable talent is not defined by what they know, but by what they have successfully delivered. Final Thoughts IAI and Machine Learning talent is no longer a niche hiring priority. It is becoming a core part of how ambitious technology businesses build products, make decisions, manage complexity, and scale effectively. As businesses move from experimentation to operational reliance, the value of AI/ML talent becomes even clearer. These professionals help turn data into advantage, embed intelligence into products and workflows, and create the conditions for smarter, faster, and more resilient growth. For high-growth tech teams, this is no longer about future-proofing. It is about current competitiveness. At NearTech, we partner with ambitious technology businesses to identify and secure the talent shaping the future of AI, ML, data, and product innovation. Looking to build or scale your AI capability? Get in touch with us to find the talent that can help your business grow with confidence.