The post-LLM explosion of AI applications in everyday business life means that companies need governance to reel in excesses and limit risks, and strategy to make the most of what AI offers.
What works for governance and strategy is changing quickly, however. Here’s what the experts at All Covered think will be key to getting AI right in 2026.
By now, most companies know that they need to do the right thing when it comes to AI governance, but in 2026, we expect that companies will increasingly need credible proof of AI governance actions. In other words, yes, your AI governance needs to be effective, but it needs to be verifiable too.
Regulators, boards, and your enterprise customers increasingly expect evidence that AI is managed with the same rigor as financial reporting or cybersecurity. That includes inventorying AI use cases, classifying use case risk, and assigning clear ownership for each system. Audit standards are slowly emerging, ISO/IEC 42001 is a good example.
That means moving from static AI governance policies to processes that are documented, including logs and approvals that show how AI use is controlled in practice. Companies with existing, functional AI governance processes may need to tweak with traceability in mind. Frameworks such as the NIST AI Risk Management Framework can be a great place to start.
The World Economic Forum Cybersecurity Outlook published in January 2026 found that 87% of surveyed leaders felt that AI-related vulnerabilities will be the fasted-growing cybersecurity risk.
At All Covered we think that, in 2026, we’ll see the AI data security challenge growing in two directions:
IBM reports that 13% of companies reported an AI-related data security breach and it’s unlikely that 2026 will see a reversal of that trend.
The best way forward? Organizations should treat AI data governance and security as a primary design constraint for every AI use case, not a bolt‑on control added later. That includes enforcing zero‑trust access to data, effective monitoring, and DLP policies specifically tuned for AI traffic.
AI agents are evolving from experimental tools into a routine part of how work gets done, embedded inside the systems people already use rather than accessed as separate chatbots.
Enterprise trend reports describe digital workers, AI agents that can independently execute multi‑step processes across multiple applications, acting as virtual team members rather than one‑off utilities.
Instead of asking a bot a single question, employees delegate goals, and an agent coordinates the underlying tasks. Some forecasts suggest that a meaningful share of day‑to‑day work decisions will be made autonomously by agentic AI within a few years.
So, when thinking about AI governance strategy, cybersecurity leaders need to consider agent governance (what they’re allowed to do), workforce design (how human and digital workers share responsibility), and skills (teaching employees to manage and supervise agents effectively).
When platform owners like Microsoft started rolling out everywhere-AI such as Copilot it looked like a novelty, but it’s increasingly the default way people interact with business software. In part because it’s become almost impossible to avoid the integrated touchpoints that nudge us towards built-in AI. That goes for CRM, ERP, and line‑of‑business SaaS too.
By early 2025, Microsoft 365 Copilot had been adopted or piloted by a majority of Fortune 500 companies. SaaS trend analyses predict that by 2026, most major SaaS categories will ship with embedded co‑pilots and AI‑native experiences as standard.
Users are increasingly comfortable using “obvious AI”, because AI is now surfaced as a sidebar or chat pane inside everything from email to documents and spreadsheets.
So, in 2026, organizations will need to introduce fencing around the tools available: including which copilots they support, and applying standardized AI data governance and security policies.
AI’s environmental footprint is starting to show up in how stakeholders judge a company’s climate strategy. Projections point to tenfold growth in AI‑related power consumption by 2030, across the AI stack, from mining materials for hardware, to building and cooling data centers.
A significant share of executives are already re‑examining climate targets because AI‑driven growth in data center usage is pushing emissions in the wrong direction.
Sustainability groups now argue that environmental impact needs to sit inside AI governance, not next to it, with AI use evaluated for energy, water, and lifecycle impacts as part of the approval and review processes.
The explosion of AI use isn’t the first time that a new technology required reigning in, and it’s not the first time that novel technology mandated a strategic approach.
Just like the broad adoption of the internet and later, cloud migration, business AI adoption requires new (and familiar) strategic and governance efforts.
At All Covered we have decades of technology strategy and compliance experience. We can help your business make the most out of artificial intelligence through AI strategy consulting while balancing AI use with the governance and compliance that reassures your stakeholders.
Find out now how you can maintain strong controls and make the most out of AI, in partnership with All Covered.