Thinking outside the bots: future-proofing tech leaders

The modern data platform

While much has been made of the transition to the cloud, many private equity-backed businesses have not yet developed their underlying data architecture sufficiently – and are still relying on siloed, rigid datasets, which are being replicated in cloud infrastructure. According to James Cross, CEO of Colibri Digital, not only does this increase costs, it also thwarts an organisation’s ability to adopt emerging technologies.

Instead, data lakehouse infrastructures, which apply metadata layers over unstructured data lakes, enable flexible and rapid adoption of new technologies – such as the overlay of LLMs to drive practical, valuable applications of GenAI. CTOs considering modern data infrastructure do, however, need to beware of its potential pitfalls. It is easy to get caught up trying to deliver a comprehensive, all-encompassing modern platform and let “perfect be the enemy of good”. Instead, businesses should start by addressing a small number of areas with known pain points, develop solutions with valuable use cases, and quickly gain organisational buy-in.

AI on the prize: reasons to be optimistic

Since AI was first proposed in the 1950s, it has gone through various cycles of excitement and disillusionment. While significant progress has been made, AI is essentially in a pre-Alpha state, with significant performance gaps and a broad lack of agreement on the ‘right’ applications. “The latest swings in mood are reminiscent of the early days of the internet in the 1990s,” said Jonathan Morris, CTO-in-residence at Bowmark, referring to the rapid shifts in public sentiment and speculative excitement around AI technologies.

Across the AI landscape, fresh challenges are emerging due to expectations not being met, fading consumer trust, a proliferation of IP-light applications (e.g. GPT wrappers) and uncertainty associated with the ownership of the underlying data. There are also substantial cost requirements for organisations to become cutting edge. Companies are building vast cross-functional platform teams to bring together specialist capabilities like DataOps, DevOps, architecture, data science and full-stack development. Alongside this, the costs to train the most powerful models are increasing, approximately doubling every nine months.

There are, however, reasons to be positive. For the right use cases, AI is increasingly demonstrating its ‘endless patience’, particularly in assistive roles such as tutoring, code review and document collaboration. Remarkable tools like Cursor AI (accelerating software development) and Perplexity (search engine) are emerging, while the cost curve is beginning to mature in some organisations. Despite the hype, AI remains deeply engaging and is constantly generating new ideas and insights.

The next wave of offshoring

Offshoring is evolving, with companies shifting their focus from cost savings to risk, value and innovation. In addition to the rise of nearshoring – where businesses outsource to geographically closer locations – remote-first and distributed teams are becoming more common, as companies integrate global talent into their operations using digital collaboration tools.

There is also increased offshoring of specialised work like software development and AI research, as countries such as India and Poland expand their pool of high-skilled talent. Automation and AI are replacing traditional offshore roles, such as call centres and first-line support, forcing providers to offer more advanced services. Meanwhile, IP protection, data security and compliance are being prioritised, with companies seeking partners that adhere to global privacy regulations. According to Steve Lydford of Codurance, offshoring today is more about strategic, well-managed partnerships than cost reduction.

Cybersecurity

CTOs are facing an increasingly challenging cybersecurity environment, with Ransomware-as-a-Service (RaaS) empowering attackers with limited technical expertise to launch devastating strikes. We heard from participants who have seen first-hand the risk of sophisticated threats, with attackers using advanced techniques to attempt access. Additionally, AI-powered attacks are emerging, allowing cybercriminals to automate and scale their efforts more efficiently.

Cybersecurity is evolving to counter these threats. The Zero Trust model, which requires strict verification for all users and devices, is now essential for securing remote work environments. AI-driven cybersecurity tools are also helping organisations detect and respond to threats faster by analysing large datasets for suspicious patterns. Finally, extended detection and response (XDR) systems provide a holistic view of threats across networks, endpoints and servers, improving detection.

Conclusion

The forum showcased the range of innovations transforming the tech sector, reinforcing the need for leaders to prioritise and maintain focus. The rapid rise of AI, automation and advanced cyber threats requires CTOs to adopt a proactive, forward-thinking approach to technology leadership. By staying ahead of these developments, CTOs can both protect their organisations and harness emerging technologies to drive sustainable competitive advantage.