Top of Mind: Key AI Trends That Are Reshaping Business

In this article we discuss:

• The current enterprise AI landscape
• What’s to come and trends on the horizon
• Recommendations for getting ahead

It's incredible how quickly AI has woven itself into the fabric of our organizations. 

A shifting landscape that began with language learning models (LLMs) and generative AI—helping us draft content, generate code, and spark creativity—is now evolving into autonomous agents capable of scaling businesses. 

Gartner predicts that by 2028, 33% of enterprise software applications will include agentic AI—digital agents that can autonomously perform complex tasks and make decisions on behalf of users—a significant leap from less than 1% in 2024.

AI has sparked new conversations and possibilities for businesses across industries. Let’s dive into the key trends and conversations around enterprise AI, spotlighting what leaders should be thinking about as they navigate this exciting new era of human-machine collaboration.

The Current Landscape 

As we look at the AI strategies taking shape within organizations today, a few key themes emerge. Here's a closer look at the trends driving current discussions in enterprise AI.

Agents, Agents, Agents 

They’re all anyone can talk about. 

We’ve preached the benefits of these autonomous digital workers many times and with many companies. 

Nearly 90% of businesses see agentic AI as a competitive advantage and spending on agents is expected to reach $47 billion by 2030. Even companies like Meta are hopping in on the trend, with the tech giant introducing a customer service agent to support small businesses.

As the use of agents becomes widespread, a question for many leaders has become: develop agents in-house or purchase from a third party? 

Considerations for each leader will be unique. Many organizations will likely adopt a hybrid approach, adjusting strategies to meet their needs. But specialized industries like healthcare will need to consider patient privacy, clinic workflows, and regulatory compliance when thinking of developing or buying agents. 

Additionally, teams with multiple agents will need to consider how they manage multi-agent systems. Some companies have found solutions for this, but it will no doubt be on the mind of many leaders as their digital workforce grows. 

Managing a Hybrid Workforce

With agents now a part of the team, leaders are also grappling with how to effectively manage a workforce that includes both humans and digital workers.

How can humans and AI truly collaborate, rather than simply co-exist? How do we drive adoption of these new AI tools and agents among our teams, ensuring they feel empowered rather than replaced? 

As agents work alongside humans, leaders will need to figure out who manages this new workforce. Some companies are handing the reins to HR, while others are considering creating new roles or narratives altogether. 

This introduces new questions about lifecycle management for agents and how they’re evaluated alongside their human collaborators. 

These are the conversations shaping the future of work and leaders need to ensure a human-first approach, where AI powers a more productive and engaging hybrid workplace.

Deepfakes and Data Fraud

As AI becomes more integrated into our systems, data security and privacy are more critical than ever. And for many companies, deepfakes and data fraud are top of mind.

Generative AI has made it easier for scammers to manipulate text, audio, images, and video. There’s been a significant surge in deepfake fraud attempts, making it a critical concern for businesses.

Ninety-two percent of businesses say they have experienced financial loss due to deepfakes. In its report on identity fraud, Onfrido revealed a 3,000% increase in deepfakes. The issue has even caught the attention of the Federal Reserve Governor Michael Barr, who urged banks to find ways to combat AI-based scams.

But companies are fighting back with AI-powered solutions. Last year, IBM teamed with Reality Defender, a company that uses tech to detect manipulated audio, images, and video, to combat fraud and deepfakes. Even everyday people have found a way to use AI against scammers. 

As the technology evolves, bad actors will, of course, find a way to abuse it. So it’s essential that leaders consider how they are protecting their employees, customers, and company.  

So, Who Owns This?

It’s a question leaders will need to answer soon. Who within your organization is responsible for AI? There isn’t a simple answer.  

Should companies create entirely new leadership roles dedicated solely to AI? Is it best to assign responsibility to a single existing leader, maybe the CTO or CIO? Or, is it a collaborative approach among teams?

These conversations reflect the deep integration of AI across all facets of a business, making its oversight a complex but crucial decision for leaders.

Trend Forecasting: The Trajectory of Enterprise AI

Looking beyond today's most pressing AI trends, what's on the horizon for enterprise AI? 

Let's take a look at some of the key developments and shifts we anticipate seeing in the near future.

The Rise of Specialized AI

Companies have been quick to adopt broad AI tools and agents, rapidly driving adoption across their organizations to ensure they remain competitive. 

But that won’t be enough soon. Leaders will need to think critically about the types of agents and tools they need.

Specialized agents and AI can be a benefit to businesses, providing in-depth industry-specific solutions for complex situations, further building trust and reliability with customers and teams. 

Some industries—healthcare, transportation, manufacturing—already require specialized AI-powered tools and this will likely increase as more and more companies begin to truly understand the role agents play in driving business value.

Already, large rounds of funding have put many specialized tools and startups centerstage. VC firm SignalFire recently raised over $1 billion to invest in early stage AI startups, specifically those focused on foundational AI models and vertical-specific solutions. 

And companies like Nabla, a clinical AI startup, and Augury, focused on industrial AI, have already received millions in funding to develop tools specific to their industries. 

Measuring AI ROI and Accountability

As enterprises move beyond the hype of AI, there is a growing need to demonstrate a clear return on investment from AI initiatives and to establish a strategy for accountability. 

Leaders will need to think about how these initiatives align with business goals from the outset, rather than adopting AI merely for its appeal. Some benefits are already being measured. 

A Microsoft study found that for every $1 organizations invest in generative AI, they realize an average of $3.70 in return. Furthermore, the study revealed that productivity-focused applications delivered the highest ROI among all AI use cases for 43% of organizations, particularly those designed to enhance individual employee efficiency and reduce task completion times.

Still, teams will need to redefine what metrics and KPIs look like for their business and take into account organizational ROI. Leadership will need to clearly articulate an AI-powered vision that guides how they’ll use efficiency gains and reward employees. 

Businesses will need to actively experiment with AI-integrated workflows to provide the concrete data needed for executives to demonstrate tangible returns and ensure AI drives true value.

Governance, Legislation, Regulation

The development and implementation of robust AI governance platforms is crucial to manage the legal, ethical, and operational performance of AI systems.

The conversation is bubbling and soon we expect it to boil over. 

Effective AI governance goes beyond simple risk assessment; it's a structured system of policies, ethical principles, and legal standards guiding AI from development to deployment, ensuring systems operate safely, fairly, and in compliance with evolving regulations.

The market is rapidly responding with sophisticated AI-powered tools designed to automate policy enforcement, detect risks in real-time, and adapt to new security challenges without constant manual oversight. 

Governments and regulatory bodies worldwide are also beginning to transition from general guidelines to legislation that aims to address concerns on data privacy, accountability, and societal impact. 

This evolving global regulatory landscape means businesses will need to carefully navigate this complex environment. Organizations must adopt agile governance models, integrate privacy-by-design principles, and invest in privacy-enhancing technologies to ensure adaptability and continuous compliance.

Strategic Recommendations for Success

With a look at current trends and the conversations shaping what’s ahead, how can businesses get the ball rolling so they don’t get left behind? What’s shared above is a good place to start, but we suggest taking your strategy a step further:

• Experiment cross-functionally with clear KPIs for ROI: Move beyond the usual framework for measuring success. Consider creating a cross-functional team explicitly tasked with building, testing, and scaling agents and AI-powered tools. Focus on metrics that demonstrate tangible business value, not just task reduction.
• Reassure and incentivize your human workforce: Proactively establish clear guidelines for AI use, reassure employees through upskilling and learning opportunities, and offer significant incentives for employees who identify and share transformative AI use cases and workflows. This not only drives adoption, but has the potential to improve organizational ROI and human-AI collaboration in a hybrid workforce.
• Proactively engage with emerging AI governance platforms and standards: Don't wait for regulations to fully mature. Begin piloting emerging AI governance platforms that offer features like intelligent data discovery, automated data lineage, bias detection, and continuous compliance monitoring. 
• Invest in specialized AI solutions: Broad adoption of generative AI tools and agents may work for your organization now, but consider the specific needs of teams across your company. Are there benefits to implementing industry- or task-specific tools? Taking a strategic approach will ensure you avoid unnecessary risk and missed advantages. 

The landscape of enterprise AI is dynamic and complex, marked by the transformative potential and challenges. As organizations navigate these shifts, it’s important to remember that success hinges on rethinking strategies and approaches to adopting AI. 

Businesses that proactively address these interwoven trends will be best positioned to unlock AI's full potential and secure a competitive advantage in the evolving digital economy.

AI presents unlimited potential for businesses and 98 % of CEOs would agree. Dive into this report to discover the positive impact AI can have on your company.