Regulation has quickly moved from a dry, backroom topic to front-page news, especially as technology continues to quickly reshape our world. With the UKs Technology Secretary Peter Kyle announcing plans to legislate AI risks this year, and similar being proposed for the US and beyond, how do we safeguard against the dangers of AI while allowing for innovation?The debate over AI regulation is intensifying globally. The EU's ambitious AI Act, often criticized for being too restrictive, has faced backlash from startups claiming it impedes their ability to innovate. Meanwhile, the Australian government is pressing ahead with landmark social media regulation and beginning to develop AI guardrails similar to those of the EU. In contrast, the US is grappling with a patchwork approach, with some voices, like Donald Trump, promising to roll back regulations to unleash innovation.This global regulatory patchwork highlights the need for balance. Regulating AI too loosely risks consequences such as biased systems, unchecked misinformation, and even safety hazards. But over-regulation can also stifle creativity and discourage investment.Striking the Right BalanceNavigating the complexities of AI regulation requires a collaborative effort between regulators and businesses. Its a bit like walking a tightrope: Lean too far one way, and you risk stifling innovation; lean too far the other, and you could compromise safety and trust.Related:The key is finding a balance that prioritizes the key principles.Risk-Based RegulationNot all AI is created equal, and neither is the risk it carries.A healthcare diagnostic tool or an autonomous vehicle clearly requires more robust oversight than, say, a recommendation engine for an online shop. The challenge is ensuring regulation matches the context and scale of potential harm. Stricter standards are essential for high-risk applications, but equally, we need to leave room for lower-risk innovations to thrive without unnecessary bureaucracy holding them back.We all agree that transparency is crucial to building trust and fairness in AI systems, but it shouldnt come at the cost of progress. AI development is hugely competitive and often these AI systems are difficult to monitor with most operating as a black box this raises concerns for regulators as being able to justify reasoning is at the core of establishing intent.As a result, in 2025 there will be an increased demand for explainable AI. As these systems are increasingly applied to fields like medicine or finance there is a greater need for it to demonstrate reasoning, why a bot recommended a particular treatment plan or made a specific trade is a necessary regulatory requirement while something that generates advertising copy likely does not require the same oversight. This will potentially create two lanes of regulation for AI depending on its risk profile. Clear delineation between use cases will support developers and improve confidence for investors and developers currently operating in a legal grey area.Related:Detailed documentation and explainability are vital, but theres a fine line between helpful transparency and paralyzing red tape. We need to make sure that businesses are clear on what they need to do to meet regulatory demands.Encouraging InnovationRegulation shouldnt be a barrier, especially for startups and small businesses.If compliance becomes too costly or complex, we risk leaving behind the very people driving the next wave of AI advancements. Public safety must be balanced, leaving room for experimentation or innovation.My advice? Dont be afraid to experiment. Try out AI in small, manageable ways to see how it fits into your organization. Start with a proof of concept to tackle a specific challenge -- this approach is a fantastic way to test the waters while keeping innovation both exciting and responsible.Related:AI doesnt care about borders, but regulation often does, and thats a problem. Divergent rules between countries create confusion for global businesses and leave loopholes for bad actors to exploit. To tackle this, international cooperation is vital, and we need a consistent global approach to prevent fragmentation and set clear standards everyone can follow.Embedding Ethics into AI DevelopmentEthics shouldnt be an afterthought. Instead of relying on audits after development, businesses should embed fairness, bias mitigation, and data ethics into the AI lifecycle right from the start. This proactive approach not only builds trust but also helps organizations self-regulate while meeting broader legal and ethical standards.Whats also clear is that the conversation must involve businesses, policymakers, technologists, and the public. Regulations must be co-designed with those at the forefront of AI innovation to ensure they are realistic, practical, and forward-looking.As the world grapples with this challenge, it's clear that regulation isnt a barrier to innovation -- its the foundation of trust. Without trust, the potential of AI risks being overshadowed by its dangers.