The last three years have been defined by the rapid emergence of generative AI, prompting both intense interest and important questions about what comes next. ClearPoint Acting Chief Client Success Officer Alan McMurtry sat down with CEO Bain Hollister and Client & Engagement Director Mary-anne Stuart-William to explore the macro trends shaping the technology sector and influencing global technology leaders as they look toward 2026.
The conversation surfaced four strategic trends that technology leaders should be paying close attention to as the year unfolds.
For the past two years, boards have asked a familiar question: What is our AI strategy? In 2026, rather than treating AI as a separate initiative, organisations should be embedding it directly into how the business operates.
“It’s no longer about asking, ‘What is your AI strategy?’” says Mary-anne. “The real question is, what is your business strategy to operate as an AI-native organisation?”
This signals a shift from incremental optimisation toward fundamentally re-architecting value. Instead of simply doing the same things faster, organisations can now revisit initiatives that were once dismissed as too complex, costly or resource-intensive.
The discussion encouraged leaders to look beyond automation and re-imagine what is possible. As Mary-anne notes, transformation is becoming less complex than it once was because of the intelligence now available to support it — allowing organisations to move quickly past technical barriers and focus directly on value realisation.
Amplification is the word Bain uses to frame how he sees 2026 unfolding. “We’ve used the word amplification a lot recently, and I think that will continue into this year,” he says. “This movement is a great amplifier of the things businesses do.”
When data is well organised and workflows are clearly understood, AI can dramatically accelerate outcomes. “If everything is tidy from a data perspective and your workflows are well understood — particularly in a software engineering environment — AI as an amplifier works really well,” Bain says.
The inverse is also true. Where systems are fragmented or practices have been neglected, AI has a tendency to expose and magnify those weaknesses. “It’s going to amplify the good and the bad,” Bain notes, suggesting that 2026 will reward organisations that have invested in strong foundations.
That same effect is reflected in how teams operate and scale their capacity, particularly in software engineering. For decades, the industry has faced a persistent supply-and-demand imbalance. “Globally, we’ve never had a surplus of software engineers,” says Bain Hollister. “There has been unmet demand for software for decades — and AI may be the key to unlocking that constraint.”
For engineering leaders, code generation is only the beginning. The larger opportunity lies in applying AI across the entire software development lifecycle — from idea formation and design through to software quality, maintainability, security, and support.
While writing code is highly visible, it represents only around 30% of a software team’s time. Understanding AI’s impact across collaboration, maintainability and team capability is now essential to delivering sustained business value. Applied thoughtfully, AI shifts organisations from a scarcity mindset to one of expanded capacity — allowing demand to be met in ways that were previously unattainable.
This same principle applies to existing infrastructure. Rather than defaulting to large-scale replacement programmes, organisations are increasingly using intelligent tooling to assess, triage and improve existing environments — modernising what already exists and reducing the risk associated with long-running transformation efforts.
As generic large language models become universally accessible, proprietary data emerges as the true strategic advantage.
While the underlying models are shared, each organisation’s data — its insights, history and intellectual property — remains unique. Mary-anne argues this is the fuel for meaningful differentiation, enabling hyper-personalisation and new forms of value creation.
The challenge for 2026 is building a virtuous cycle: using proprietary data to generate insight, feeding those insights back into systems, and continuously strengthening decision-making. Achieving this requires sovereignty over data and clarity around how it is governed, protected and used as a competitive asset.
Despite the scale of this technological shift, the outlook for New Zealand remains optimistic. The country’s “Number 8 Wire” mindset — a practical ingenuity for solving complex problems — positions organisations well to experiment, adapt and learn quickly.
However, realising this potential demands leadership courage. “CEOs can afford to take a few bets,” Bain says. “It’s complex, and not everything will work — but the things that do can deliver significant upside.”
At ClearPoint, this mindset has already delivered tangible benefits. By deploying internal AI agents to support operational work, the business has saved around 20 hours per month on compliance activities alone — time redirected toward higher-value, client-centred work.
As automation increases, the human role continues to evolve — from operator to “agent boss.” In an environment saturated with information, leaders become trusted sources of judgement and direction. The most successful organisations will be those that automate the heavy lifting while empowering people to focus on discernment, creativity and connection.
2026 is not about having all the answers, but is about having the courage to start. “It’s better to lean into building,” Bain says. “Try things, fail fast and learn together.”