A recent report from Gartner predicts that by 2027, artificial intelligence will handle half of all business decisions, either fully automated or partially augmented by AI agents. This trend is driven by the increasing pressure on technology companies to deliver returns on their significant investments in AI, with smaller businesses also quickly adopting these tools. According to Gartner, one in ten executive boards globally will rely on AI for substantial decision-making by 2029. The report emphasizes the need for human oversight to ensure data quality and effective governance, highlighting that companies prioritizing executive training in AI literacy may see revenue increases of up to 20 percent.
Small and mid-sized businesses are quickly adopting artificial intelligence, yet a significant 95% report needing more training to use these tools effectively. A global survey by TeamViewer, involving 1,400 business leaders, found that while 72% consider themselves AI experts, only 35% use AI daily, showing a gap between confidence and actual use. The report points out that 28% of decision-makers in these businesses see not adopting AI as leading to higher operational costs, while larger companies focus on the fear of falling behind competitors. Despite worries about infrastructure and security, 75% of small and mid-sized business leaders plan to increase their AI investments next year, marking a shift from testing to advanced deployment.
A recent study from Stanford University shows that while artificial intelligence agents are gaining popularity in the workplace, professionals mainly want to use them for automating boring and repetitive tasks rather than for more advanced responsibilities. The research involved interviews with 1,500 professionals and highlights a strong preference for keeping human control in work settings, even as AI technology becomes more advanced. The study found that most respondents are willing to use AI agents for low-stakes tasks, suggesting a desire to focus on more engaging work. However, it also pointed out significant mismatches between the tasks companies assign AI for and those that workers actually want to automate. This research contributes to a growing body of literature indicating that AI’s impact on employment will vary greatly depending on the type of job and responsibilities.
Why do we care?
The headlines indicate a move from testing to full operational use, with SMBs investing more despite skill shortages and worries about governance, infrastructure, and security.
A major risk exists when executives think they understand AI well but lack practical implementation knowledge. This can result in poor deployment choices, vendor lock-in, or security issues if guidance isn’t consulted early.
Introducing AI without proper workforce alignment or clear expectations can lead to increased employee frustration. Deploying advanced AI that doesn’t address real-world needs wastes resources and damages trust in both the technology and the MSP that implemented it. Many SMBs are eager to adopt AI but have not upgraded their infrastructure or security measures to support it safely. Without an MSP guiding a structured implementation strategy, these businesses risk outages, compliance issues, or data breaches—problems that can reflect poorly on the provider. The disconnect between AI deployment and worker expectations is where MSPs can step in—bridging intent and usability through human-centered workflow design.
If you’re still only reselling licenses or providing automation “add-ons,” you’re missing the bigger play: helping businesses redesign how decisions are made and tasks are performed in an AI-powered environment.
The winners in the next phase won’t be those who promise AI. They’ll be the ones who help clients actually use it—well.