You’ve probably come across LinkedIn posts that sound way too polished. These feel inauthentic while trying to sound motivational and strangely empty. The kind that turns a basic workplace thought into five neat paragraphs that push a fake lesson, and a comment section full of robotic applause.
Well, LinkedIn is now calling this a problem. The platform says it is taking new steps to reduce the reach of what it calls “AI slop,” referring to low-effort, AI-generated content that may sound clean on the surface while offering little original thought, expertise, or lived perspective.
LinkedIn
How LinkedIn is cracking down on AI noise
LinkedIn’s Laura Lorenzetti says AI can be useful for refining language, although posts and comments still need to reflect the person behind them. So the company is building technology systems with its editorial team to identify signals of generic AI content. These systems are being trained to distinguish between posts that add perspective, context, or expertise and posts that feel repetitive, polished, and empty.
It doesn’t apply to full posts as the new system will recognize and act on comments created at scale using automation tools. These will include comments that have little to no human involvement. LinkedIn is also targeting replies that merely restate the original post without adding anything of substance.
LinkedIn isn’t saying every AI-assisted post will be punished. The focus is to make AI-generated content less present. When the platform detects such posts, it will be less likely to distribute it beyon the poster’s immediate network.
LinkedIn mentioned that early testing has been encouraging, with its systems correctly identifying generic content 94% of the time. The company also says members are already seeing fewer of these posts from outside their networks.
Alongside this, verification is playing a big role in fighting bots and fake AI profiles as well. With more than 100 million verified members, this could reduce the exhausting AI noise from dominating users’ feeds. It’s about time LinkedIn has started its fight against AI, with other companies like Meta and YouTube also readying tools against the avalanche of AI-generated content.
Trusted quality data is the backbone of agentic AI.
Identifying high-impact workflows to assign to AI agents is key to scaling adoption.
Scaling agentic AI starts with rethinking how work gets done.
Gartner forecasts that worldwide AI spending will total $2.5 trillion in 2026, a 44% year-over-year increase. Spending on AI platforms for data science and machine learning will reach $31 billion, and spending on AI data will reach $3 billion.
The global agentic AI market will reach $8.5 billion by the end of 2026 and nearly $40 billion by 2030, per Deloitte Digital. Organizations are rapidly accelerating their adoption of AI agents, with the current average utilization standing at 12 agents per organization, according to MuleSoft 2026 research. This rate is projected to increase by 67% over the next two years, reaching an average of 20 AI agents.
According to IDC, by 2026, 40% of all Global 2000 job roles will involve working with AI agents, redefining long-held traditional entry, mid, and senior level positions. But the journey will not be smooth. By 2027, companies that do not prioritize high-quality, AI-ready data will struggle to scale generative AI and agentic solutions, resulting in a 15% loss in productivity. While 2025 was the year of pilot experiments and small production deployments of agentic AI, 2026 is shaping up to be the year of scaling agentic AI. And to scale agentic AI, according to IDC’s forecast, companies will need trustworthy, accessible, and quality data.
Scaling agentic AI adoption in business requires a strong data foundation, according to McKinsey research. Businesses can create high-impact workflows by using agents, but to do so, they must modernize their data architecture, improve data quality, and advance their operating models.
McKinsey found that nearly two-thirds of enterprises worldwide have experimented with agents, but fewer than 10% have scaled them to deliver measurable value. The biggest obstacle to scaling agent adoption is poor data — eight in ten companies cite data limitations as a roadblock to scaling agentic AI.
McKinsey identified the top data limitations as primary constraints that companies face when scaling AI, including: operating model and talent constraints, data limitations, ineffective change management, and tech platform limitations.
Data is the backbone of agentic AI
Research shows that agentic AI needs a steady flow of high-quality, trusted data to accurately automate complex business workflows. Successful agentic AI also depends on a data architecture that can support autonomy — executing tasks without human intervention.
Two agentic usage models are emerging: single-agent workflows (one agent using multiple tools) and multi-agent workflows (specialized agents collaborate). In each case, agents will rely on access to high-quality data. Data silos and fragmented data would lead to errors and poor agentic decision-making.
Four steps for preparing your data
McKinsey identified four coordinated steps that connect strategy, technology, and people in order to build strong foundational data capabilities.
Identify high-impact workflows to ‘agentify’. Focus on highly deterministic, repetitive tasks that deliver value as strong candidates for AI agents.
Modernize each layer of the data architecture for agents. The focus on modernization should support interoperability, easy access, and governance across systems. The vast majority of business applications do not share data across platforms. According to MuleSoft research, organizations are rapidly adopting autonomous systems. The average enterprise now manages 957 applications — rising to 1,057 for those furthest along in their agentic AI journey. Only 27% of these applications are currently connected, creating a significant challenge for IT leaders aiming to meet their near-term AI implementation goals.
Ensure that data quality is in place. Businesses must ensure that both structured and unstructured data, as well as agent-generated data, meet consistent standards for accuracy, lineage, and governance. Access to trusted data is a key obstacle. IT teams now spend an average of 36% of their time designing, building, and testing new custom integrations between systems and data. Custom work will not help scale AI adoption. The most significant obstacle to successful AI or AI agent deployment is data quality, cited as the top concern by 25% of organizations. Furthermore, almost all organizations (96%) struggle to use data from across the business for AI initiatives.
Build an operating and governance model for agentic AI. This is about rethinking how work gets done. Human roles will shift from execution to supervision and orchestration of agent-led workflows. In a hybrid work environment, governance will dictate how agents can operate autonomously in a trustworthy, transparent, and scaled manner.
The work assigned to AI agents
McKinsey highlighted the importance of identifying a few critical workflows that would be candidates for AI agents to own. To begin, an end-to-end workflow mapping would help identify opportunities for agentic use. McKinsey found that AI adoption is led by customer service, marketing, knowledge management, and IT. It is important to identify clear metrics that validate impact. Teams should identify the data that can be reused across tasks and workflows.
McKinsey concludes that having access to high-quality data is a strategic differentiator in the agentic AI era. Because agents will generate enormous amounts of data, data quality, lineage, and standardization will be even more important in the agentic enterprise. And as agentic systems scale, governance becomes the primary level for control. The data foundation will be the competitive advantage in the agentic era.
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