November 8, 2024
Artificial Intelligence is no longer a futuristic concept—it’s here, and it’s transforming industries. From revolutionizing workflows to enhancing customer experiences, AI is setting the pace for what’s next in business innovation. As we look to 2025, here are what we believe are the top AI trends and how organizations can leverage them for success.
GenAI (think Chat GPT, Microsoft Copilot, Google Gemini, etc.) is rapidly becoming a mainstay for businesses. These AI models don’t just automate tasks—they create. Whether it’s generating quality content, producing unique designs, or developing marketing campaigns, GenAI is unlocking new avenues for creativity and productivity. This means faster ideation, more personalized customer experiences, and the ability to scale content production without sacrificing quality. Further, these models can now come pretrained and good at many tasks “out of the box,” a major shift from the traditional approach where they need to be specifically trained on an organization’s own data. This often means quicker start-up time and time-to-value.
Why it matters: Companies that harness GenAI can streamline creative workflows and do more with less. GenAI allows teams to focus on their vision while AI handles execution.
Natural Language Processing (NLP) is all about using text as the underlying data for ML systems, and extends to teaching computers to understand, interpret, and generate human language. Technological advancements in NLP are happening rapidly, making AI better at understanding and generating human language, and the field that has produced today’s Large Language Models (LLMs) behind systems like ChatGPT. This is revolutionizing the way businesses interact with customers and manage information. NLP advancements are driving smarter virtual assistants, more intuitive chatbots, and enhanced customer support solutions.
Why it matters: In a world where customer experience is a key differentiator, NLP-powered solutions allow businesses to provide instant, high-quality service—helping drive engagement and brand loyalty.
As digital threats grow more sophisticated, businesses are turning to AI to bolster their cybersecurity defenses. AI-driven cybersecurity solutions now provide real-time threat detection, rapid response capabilities, and predictive analytics to prevent attacks before they happen. By analyzing network data, AI can identify anomalies and patterns, safeguarding sensitive information and maintaining trust.
Why it matters: Cybersecurity is no longer just an IT issue—it’s a business imperative. AI’s ability to monitor, detect, and neutralize threats in real-time will be critical for organizations that want to protect their data and reputation.
AI-powered technology requires a substantial amount of energy to run. Sustainable AI focuses on minimizing the environmental impact of AI technologies by developing AI systems that reduce energy usage, minimize waste, and incorporate eco-friendly design principles. For example, GenAI and LLM models are trained on massive quantities of Graphics Processing Units (GPUs). Training a single large model can consume hundreds of megawatt-hours, comparable to the energy usage of several households in a year. To address this, tech leaders are rethinking energy solutions, exploring everything from optimizing data center efficiency to reimagining energy grids to accommodate AI's growing power needs sustainably. For example, companies like Google and Microsoft are integrating AI with renewable energy sources, deploying tools that dynamically adjust AI workloads based on grid stability and renewable energy availability. Researchers are also developing more efficient architectures to reduce overall energy consumption.
Why it matters: Sustainable AI is more than a trend—it’s a necessity. Businesses that focus on reducing their carbon footprint while implementing AI will not only contribute to environmental conservation but also appeal to consumers and investors who prioritize sustainability.
As AI’s influence expands, so do the ethical challenges that come with it. Businesses are increasingly focused on developing transparent and unbiased AI systems. Today, with accessible tools and open-source models, it’s easier than ever to apply AI in ways it wasn’t intended—sometimes creating unintended consequences or risks. For instance, a model built for image generation could be reconfigured for deepfakes, or predictive algorithms meant for customer insights might be repurposed for surveillance. This ease of repurposing AI makes it imperative for everyone to consider the ethical implications and unintended uses of these powerful technologies. Ethical AI frameworks ensure that algorithms are fair, accountable, and free from bias—an essential component in gaining and maintaining customer trust.
Why it matters: Consumers are more conscious than ever about how their data is used. Businesses that invest in ethical AI practices not only mitigate risk but also build stronger relationships with their customers and partners.
The rules and standards around AI are quickly developing. For example, the European Union’s AI Act is a major step toward regulating AI, requiring that high-risk AI systems meet strict requirements for transparency and fairness. In the U.S., the White House’s Blueprint for an AI Bill of Rights offers guidance on using AI responsibly, focusing on privacy, data protection, and fairness. Though there isn’t yet a single set of rules in the U.S., more comprehensive policies are likely on the way as AI use becomes widespread. Companies using AI will soon need to follow these rules closely, especially as countries around the world establish clearer guidelines. As the U.S. transitions to new leadership, the course of AI regulation could shift, leaving the future of policy development less clear.
Why it matters: Getting ahead of these regulatory trends can reduce compliance risks, foster trust with customers, and position your organization as responsible leaders in AI innovation.
In today’s fast-paced world, businesses are always looking for ways to be quicker and more efficient. That’s where Edge AI comes in. Instead of sending all data to the cloud for processing (which can take time), Edge AI processes data right where it’s generated. Think of it as having a mini brain right next to your sensors or devices. By handling data closer to the source, businesses can make real-time decisions without waiting for information to travel back and forth to the cloud. This has the potential of significantly reducing compute costs because the compute happens locally on a user’s device rather than in a hosted environment. Difficulties arise where the smaller edge devices (phone/tablet/etc.) would have lower hardware performance capabilities, meaning the ML models have to be smaller and lightweight.
Why it matters: Edge AI speeds things up, makes systems more secure, and boosts overall efficiency. This approach is especially helpful in industries like manufacturing, automotive, and healthcare, where delays can really impact operations. By processing data locally, companies can react instantly, helping them stay productive and responsive to changes on the ground.
Advances in robotic process automation (RPA), using software agents to automate repetitive tasks, are rapidly changing the AI and automation landscape. In the past, RPA was all about automating predictable, rule-based processes; but now, with the addition of machine learning, RPA can tackle more complex tasks that involve some uncertainty. This means these systems can learn from data, make smarter decisions, and adapt to new situations, allowing them to automate processes that were once too complicated for traditional automation tools. For example, in finance, RPA can automate invoice processing and account reconciliation, while in customer service, it can help manage queries and update customer records.
Why it matters: Automating more complex tasks can lead to big savings and improve operations. By taking repetitive jobs off employees' plates, businesses can empower their teams to focus on more strategic and creative work. Plus, as automation gets smarter, it can enhance customer experiences by delivering quicker and more accurate responses.
As we head into 2025, we know that AI is not just a technological trend—it’s reshaping the way businesses operate and compete. By creating a roadmap and governance framework with all departments and stakeholders in mind, organizations can effectively navigate the complexities of AI adoption and maximize ROI.
