How do you think AI will disrupt business across industries? Add to my list: 1. Content creation 2. Photos and video production 3. Basic coding and debugging 4. Strategic analysis to be highly complimented 

In my opinion, the most straightforward use cases that can be accelerated via AI will be those which have deterministic outcomes and are supplied with comprehensive data sets and context. Examples are segmentation, categorisation, rules-based prioritisation. These are easy to verify and to fine-tune models.

Companies that hold custom/niche information that they can distill into a model and add GPT on top will hold unique seller advantages. Example, thought leaders in specific industries can accelerate and diversify their impact and reach by having proprietary data more user-friendly via a GPT interface and also agents. It will be the new SaaS experience route.

Personally though, I am experimenting on using coding AI for migration projects (e.g. migrating from Angular to ReactJS) using stateful (context expansion) and stateless (for horizontal scaling) multi-step approach. 

Here are my thoughts:

1. Content creation → Fast, scalable text generation. Humans focus on strategy and tone.
2. Photo & video production → Instant, customized visuals without studio costs.
3. Basic coding & debugging → AI handles routine code; developers focus on architecture and complex issues.
4. Strategic analysis → AI surfaces insights; people make the judgment calls.

Start building internal AI literacy across teams now — the companies that train employees to work effectively with AI (prompting, validation, oversight) will be far more competitive in the next 2–3 years.

AI is poised to transform industries where tasks are well-defined and follow predictable patterns. For instance, L1 software support roles—which involve reviewing incident logs, performing initial diagnostics, and creating tickets—are increasingly being automated through AI-driven log analysis and ticketing systems. Similarly, roles like FinOps, which focus on analyzing cloud costs and recommending optimization strategies, are also being reshaped. In such domains, where decision-making is based on structured data and repeatable logic, AI can not only replicate but often enhance human performance.

5. 1st level (customer) support.
6. Competitive intelligence and GTM analysis
7. Revenue Operations

Oh, and looking at some stocks, there is also a chance AI will replace third-party analysts and their objective takes as well.

AI is not just a disruptor; it is a systemic shift on the scale of what email was to traditional mail. However, much like email, the security implications of AI adoption are often underestimated or retrofitted after deployment, and we are already seeing history repeat itself.

1. AI Will Expose the Same Gaps We Faced with Email – Email fundamentally altered communication speed and business velocity at scale, but its rapid proliferation outpaced our ability to secure it. Phishing, spoofing, misdelivery, and data exfiltration via email remain top concerns decades later.

2. "Leapfrogging with AI" will lead to disruption and waste - Many organizations are embracing AI, hoping to leapfrog process gaps or workforce shortages. While this enthusiasm is understandable, AI is not a shortcut for missing foundational maturity.

3. The Unexpected Win: A Resurgence of Data Governance and DLP Awareness - Despite the risks, the AI movement has sparked a long-overdue renaissance in data governance. Security practitioners have struggled to elevate the importance of data classification, lifecycle management, and enterprise-wide DLP programs for years.

AI is not merely another tool in the innovation toolkit; it is a force multiplier that will challenge assumptions, expose weaknesses, and elevate strategic priorities. However, just as with email, AI's success depends on how we use it and how we secure it. The organizations that pair AI innovation with intentional, enterprise-wide security and governance strategies will lead in performance and resilience.