TOP TECH TRENDS 2025: THE NEXT WAVE OF AI: WHAT’S REALLY COMING IN 2025
As Chief Technology Officer (CTO) of Moneypenny, the leading outsourced communications company, Pete Hanlon believes we are reaching a pivotal milestone, especially with Artificial Intelligence (AI). Here’s his take on the next year, highlighting the obvious shifts and deeper changes that could redefine how we work in every sector- from facilities management to professional services.
Open Source Is Coming for the Crown
The most exciting battle in AI isn’t unfolding in corporate labs; it’s happening in the open-source community. By mid-2025, we’ll see open-source models going head-to-head with industry leaders such as GPT-4o and Claud-Sonnet-3.5. This isn’t just about matching performance metrics. It’s about making AI accessible to sectors that have been held back by data privacy concerns, opening doors for industries that have struggled to leverage this technology. The result? A new era where AI is democratised, accessible to all, and no longer controlled by closed-source businesses.
Forget AI Replacing Workers – Think AI as Your Digital Colleague
Remember when everyone thought AI would replace us all overnight? That’s not how it’s playing out. Instead, we’re witnessing the emergence of hybrid teams where AI takes on the repetitive tasks, leaving people free to handle more complex challenges. It’s less about replacing jobs and more about using AI to superpower people and using data to enable smarter decision-making. Moneypenny, for example, delivers outsourced communication solutions that blend the efficiency of AI with the personal touch of real people.
Integration: The Real Challenge Nobody’s Talking About
The next phase isn’t about building brand new AI systems, it’s about weaving them seamlessly into existing business processes and infrastructure. Picture Customer Relation Management (CRM) systems that can predict what customers need, knowledge bases that update themselves, conversations that flow naturally between voice and text, and customer support that breaks language barriers. Our clients can effortlessly embed our customer service systems powered by AI directly into their back office with minimal effort and maximum impact.
Industry-Specific Models: Tailored AI for Specialized Needs
We’re entering an era of industry-specific Large Language Models (LLMs) tailored for fields like finance, healthcare, and law. These models will come pre-loaded with domain-specific knowledge, enabling businesses to deploy AI that understands their unique requirements, language, and regulatory needs. In finance, LLMs could support compliance and offer investment insights. In healthcare, they could assist clinicians with patient histories and treatment guidelines. In law, they could streamline contract review and case law analysis. These specialised models will allow companies to quickly implement AI that’s relevant, compliant, and impactful in their field.
The Reality Check Is Coming
Some companies may soon realise they’ve taken on more than they can handle with AI adoption, facing a range of unexpected challenges. Many will struggle with complex integration issues as they attempt to launch AI initiatives within existing systems. Additionally, there may be difficulties in managing the high expectations around AI’s capabilities, as reality often falls short of the hype surrounding its potential.
Regulation: The Elephant in the Room
Companies should prepare for the growing impact of AI regulations, particularly in customer-facing applications. Forward-thinking organisations are already taking steps to build transparency into their AI systems, overhauling data governance practices to ensure accountability. They are creating detailed audit trails to track AI decision-making and making sure that their systems are both fair and accessible. These proactive measures not only help them stay compliant but also foster trust with their customers.
What This Means for You
The next year won’t just be about AI getting better – it’ll be about AI getting smarter about how it fits into our existing world. Success won’t come from blindly adopting every new AI tool. It’ll come from carefully choosing where AI can genuinely improve how we work.
