AI trends and opportunities are emerging quickly in the enterprise landscape.
A few years ago, companies were restricted to basic AI solutions, such as rule-based chatbots and simple analytical tools. Now, we have endless types of AI to explore, from LLMs and generative AI models to conversational bots and multimodal systems.
As a result, the market for artificial intelligence is growing faster than ever. PwC even predicts that AI could contribute more than $15.7 trillion to the global economy by 2030 – more than the output of India and China combined.
So, for organizations ready to embrace the AI revolution, what are the most important trends to consider? Where is artificial intelligence headed in the next few years, and what opportunities and risks are emerging? Here are the top enterprise AI trends worth watching right now.
1. Generative AI Trends: Gen AI Continues to Grow
Though there have been countless evolutions in the artificial intelligence space in recent years, the rise of generative AI introduced a significant turning point for the industry. Since ChatGPT and OpenAI burst into the scene in 2022, demand for generative AI applications has grown.
Now, by 2032, experts believe the generative AI market will be worth over $1.3 trillion. In the years ahead, the use cases for generative AI will only continue to grow. Already, this technology is helping specialists diagnose diseases and develop medications in the healthcare space. In the creative sector, it’s driving massive increases in content production.
In the automotive and industrial landscape, generative AI introduces new opportunities to enhance product design and development. As generative AI solutions grow increasingly advanced, accessing ever-larger data sets and deep learning algorithms, the potential for these tools will be endless.
What’s more, we can expect to see greater accessibility in the landscape as major leaders, from Google and Meta to Microsoft, embed generative AI systems into their platforms.
2. The Evolution of Multimodal AI
Multimodal AI solutions, building on large language models and generative AI, represent one of the most important AI trends for enterprises today. Already, the market for multimodal solutions, capable of understanding various types of data, from text to images and voice, is growing at a phenomenal rate. The current CAGR for the market is 35.8%.
Multimodal AI in the enterprise can assist organizations with a wider range of tasks and use cases than standard text-based models. These tools can empower businesses to analyze data in the customer service landscape across multiple channels and interactions, drawing insights from videos, text, and voice. They also allow companies to create content in a range of different, engaging formats.
Multimodal AI is still being developed, but innovators like OpenAI are already working on new multimodal capabilities for GPT bots. Additionally, Google’s Gemini collection of large language models have been specifically designed with a focus on multimodal functionality.
Multimodal solutions will improve user experiences in the enterprise, allowing companies to draw insights from all types of data. Plus, it will lead to better training experiences, more advanced decision-making processes, and even stronger training and onboarding opportunities.
3. Small Language Models Take the Stage
Large Language Models (LLMs) have earned significant attention in the enterprise landscape thanks to their ability to enhance AI systems with huge volumes of valuable data. However, in some environments, new AI trends are emerging, pushing companies towards smaller, more precise models.
Even OpenAI’s Sam Altman was recently quoted saying that he believes the focus on “more parameters”, will reduce in the years ahead. While massive models jumpstarted the current age of generative AI, they do have their drawbacks.
For the most part, only the largest, most affluent organizations can afford to access the server space and computing resources required to train these models. Plus, training LLMs has a significant impact on our environment. One GPT-3 size model, for instance, would consume the yearly electricity of more than 1,000 households.
Small language models, such as Microsoft’s new Phi-3, can deliver exceptional results without being as resource-intensive. Plus, certain research papers have even indicated that training smaller models on specific data can lead to better results, fewer AI hallucinations, and greater accuracy.
4. Open Source Models Democratize AI
Enterprise leaders are increasingly discovering new and more convenient ways to develop their own AI systems. Microsoft has already introduced Copilot Studio, empowering organizations to make simple, no-code, and low-code changes to AI bots to suit their specific needs.
For organizations that want to make more granular changes to frameworks, and develop bespoke models for specific use cases, rather than repackaging services from leading AI companies, open-source AI models are an incredible opportunity.
Solutions like Mistral’s “Mixtral” model, and Meta’s Llama 3 model will help to democratize cutting-edge AI for today’s business leaders. If organizations can access the right data, and development strategy, they can leverage the open-source AI models and tools available on the market to create bots and systems tailored to any use case.
Open-source models offer organizations a chance to develop models trained on their proprietary data and fine-tuned to their needs without expensive infrastructure investments. This could be extremely useful in certain industries like healthcare and finance, where highly specialized customer service tasks and vocabulary might not be fed into generic systems.
This open-source approach could even help organizations to avoid compliance and governance issues. Keeping AI training and inference local is a good way to reduce the risk of sensitive information being used to train closed-source models. It can also make AI more explainable, which will be crucial in adhering to new governance mandates.
5. New Methods Emerge for Model Optimization
As demand for advanced AI solutions continues to grow, enterprise leaders are increasingly seeing the value of switching from more “generic” models to specialized, custom solutions. New model optimization methods are shaping AI trends, helping to ensure more compact solutions and smaller models can still drive exceptional results for organizations.
Many key advancements are being driven by new techniques and resources in the AI landscape. In 2023, for instance, various model-agnostic techniques for optimization emerged, such as:
- Low-Rank Adaptation: The LoRA method for AI optimization involves freezing pre-trained model weights and using trainable layers in each transformer block. This basically reduces the number of parameters that need to be updated when training a model.
- Direct Preference Optimization: DPO builds on the benefits of reinforcement learning from human feedback to train models often used for chat and customer service. It offers a lightweight and simpler approach to “fine-tuning” model outputs.
- Quantization: Similar to reducing the bitrate of audio to minimize latency and file size, quantization reduces the precision used in data point representation. This can reduce memory usage when training and developing models.
These new optimization techniques could offer smaller players in the AI landscape and enterprises with new solutions for unlocking advanced AI capabilities.
6. Chatbot and Virtual Agent AI Trends
For enterprises, there’s no limit to the potential use cases offered by the latest AI models. These solutions can enhance everything from team productivity, to decision-making. However, we are seeing a significant focus on using the latest AI systems to enhance user experiences.
In particular, there’s a growing focus on making self-service solutions like AI chatbots and virtual assistants more human, intuitive, and capable. With more sophisticated AI algorithms, and solutions like generative and conversational AI, enterprises are in the perfect position to expand the functionality of their virtual agents.
Already, business leaders are experimenting with multimodal AI models to help enable more advanced omnichannel interactions with customers. For instance, with multimodal AI, a user can simply point their camera towards a product with a technical issue and ask the model to guide them through a step-by-step troubleshooting process.
Advanced chatbots and virtual agents will also be able to do more for customers and users. Rather than just offering access to information, they’ll be able to complete a range of tasks, such as planning trips, making reservations, or arranging schedules.
This will lead to an increase in bots that can not only transform customer experiences and boost business efficiency but also improve worker productivity and performance.
7. Ethical and Regulatory Concerns Effect AI Trends
Not all of the AI trends enterprise leaders need to focus on today are connected to the potential benefits and opportunities of AI. As adoption of advanced artificial intelligence systems continues to grow, so too does the demand for greater governance, and security standards.
Each advancement in the AI landscape brings with it new challenges. For instance, multimodal capabilities give criminals more opportunities to create advanced deepfakes. In fact, the number of deepfakes detected between 2022 and 2023 increased by 8 times. Generative AI models with access to huge amounts of data can facilitate data breaches, and even suffer from bias.
At this stage, regulatory groups are still struggling to agree on guidelines for regulating artificial intelligence. However, in the US, new mandates are currently being passed to ensure that AI remains secure, ethical, and safe for everyday users. Equally, in 2023, the European Union introduced the Artificial Intelligence Act.
Primarily, the governance standards emerging around AI focus on tackling a few major concerns, such as the potential for AI mis-use, discriminatory bias, and unexplainable AI. We’re also seeing new rules emerging that will determine how companies can use copyrighted materials to train AI models, helping to protect creators in the future of generative AI content production.
Enterprise leaders will need to take a proactive approach to understand these new regulations as they emerge and ensure they remain compliant with the latest standards.
8. The Risk of Shadow AI Leads to New Policies
New regulatory standards won’t just affect how companies respond to AI trends, implement new technologies, and train their systems. It will also have an impact on comprehensive operational policies throughout enterprise organizations.
The escalating potential for legal, economic, reputational, and regulatory issues to emerge from AI usage will force companies to implement new strategies to govern AI use. Already, some companies have attempted to protect themselves from potential issues by trying to prevent team members from using popular AI tools like ChatGPT.
Unfortunately, the rising demand for AI solutions will make simply “banning” the use of these systems impossible going forward. Already, 90% of people say they regularly use AI at work. Banning the use of certain applications, just like banning the ability to use tools like WhatsApp and Messenger in customer conversations, could lead to a rise in Shadow AI.
Shadow AI, like Shadow IT, is likely to become a critical issue as employees grow more comfortable with AI technologies and discover the benefits they can deliver in terms of productivity and efficiency. Rather than simply eliminating access to AI solutions, business leaders will need to ensure they’re training their team members on how to use AI safely and providing access to the right tools.
9. AI Solutions Enhance Cybersecurity
While it’s clear that the rapid adoption of AI trends in the enterprise presents significant risks, particularly in regard to data privacy and security, it’s worth noting that AI evolutions can enhance cybersecurity and improve compliance in some areas too.
AI tools built into contact center and UCaaS platforms can already assist with recording conversations, transcribing data, and even automatically redacting sensitive information from documents. They can also monitor conversations in real-time, for signs of potential compliance risks, or to notify team members when sensitive information is revealed.
Plus, AI solutions can track potential risks throughout the enterprise, monitoring network access points and possible threats in real time. They can even improve authentication methods by analyzing biometric data, like a customer’s voice, instead of relying on passwords.
Some companies, like Microsoft, even invest in dedicated generative AI security solutions. Microsoft Security Copilot, for instance, is custom-made to address the needs of security teams and can even upskill employees to reduce the threats caused by talent gaps.
Preserving security and compliance in the enterprise in the years ahead will likely require a meticulous approach to using AI carefully and implementing AI tools for threat mitigation.
10. Embedded AI in Every Enterprise App
Finally, as AI use cases continue to evolve across the enterprise, in every industry, we’re seeing an increasing number of companies embed the latest artificial intelligence solutions into their technologies. Unified Communications platforms like Microsoft Teams have Microsoft Copilot.
Contact center platforms have their own AI assistants and solutions designed for customer support, employee training, and data analysis. Even smaller everyday applications like Customer Relationship Management (CRM) tools, meeting booking systems, like Microsoft Places, and workforce management apps are being infused with AI.
We’re rapidly entering an era where AI is everywhere, acting as a teammate in our conversations with colleagues, helping us to create content for marketing campaigns, and even improving development processes. AI is even transforming the metaverse, shaping how we create virtual content, non-playable characters, and avatars.
In certain sectors, we’re even seeing the rise of dedicated AI solutions for specific needs. For instance, in manufacturing, computer vision, AI, robotics, and hyper-automation strategies are accelerating the production of new products and systems, from cars to business machinery.
Going forward, AI will become a commonplace feature in every technology we use, whether we’re communicating with friends over tools like WhatsApp, designing new products, or simply managing hybrid work calendars.
The Future of Enterprise AI Trends
AI trends have come a long way in the last couple of years alone. From the rise of generative AI in 2022, to the global adoption of advanced ecosystems in 2023, and now the customization and optimization of AI in 2024, the market is constantly moving forward.
AI is set to transform the way we work, live, and communicate on an astronomical scale. It will enhance operational efficiencies, innovation, and growth in enterprise environments across all business sectors. However, it will also introduce new challenges to overcome, from building ethical AI frameworks to managing data security.
The next step in our AI journey will be one that enterprises need to navigate carefully. Companies will need to adapt quickly and implement AI in the right ecosystems as responsibly as possible to avoid risks and serious repercussions.
However, even with challenges to address, it’s safe to say that AI will be a fundamental part of the enterprise landscape in the years ahead.
