Scaling agentic AI demands a strong data foundation – 4 steps to take first


datafoundationgettyimages-1472653690

Eugene Mymrin/ Moment via Getty Images

Follow ZDNET: Add us as a preferred source on Google.


ZDNET’s key takeaways

  • 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. 

Also: How to build better AI agents for your business – without creating trust issues

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. 

Also: AI agents are fast, loose, and out of control, MIT study finds

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. 

Also: Prolonged AI use can be hazardous to your health and work: 4 ways to stay safe

  1. Identify high-impact workflows to ‘agentify’. Focus on highly deterministic, repetitive tasks that deliver value as strong candidates for AI agents. 

  2. 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. 

  3. 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.  

  4. 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.

Also: These companies are actually upskilling their workers for AI – here’s how they do it

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. 





Source link

Leave a Reply

Subscribe to Our Newsletter

Get our latest articles delivered straight to your inbox. No spam, we promise.

Recent Reviews







In the ever-shifting geopolitical sphere, China’s growing military presence and the ongoing tensions over Taiwan and the South China Sea continue to be a closely watched topic — particularly in regard to China’s ambition for naval power. In recent years, much speculation has been made over the country’s rapid military development, including the capabilities of the newest Chinese amphibious assault ships.

While there’s no denying its military advancements and buildup, much has been made about the logistical and military difficulties that China’s People’s Liberation Army (PLA) would face if it launched an amphibious invasion of Taiwan. However, there’s growing concern that if a Taiwan invasion were to happen, it wouldn’t just be military vessels taking part in the action, but a fleet of commercial vessels, too — including a massive new car ferries that could quickly be repurposed into valuable military transports.

While the possibility of the PLA using commercial vessels for military operations has always been on the table for a potential Taiwan invasion, the scale with which China has been expanding its commercial shipbuilding industry has become a big factor in the PLA’s projection of logistical and military power across the Taiwan Strait. It’s also raised ethical concerns over the idea of putting merchant-marked ships into combat use.

From car ferry to military transport

The rapid growth of modern Chinese industrial capacity is well known, with Chinese electric vehicle factories now able to build a new car every 60 seconds. Likewise, China has developed a massive shipbuilding industry over the last 25 years, with the country now making up more than half of the world’s shipbuilding output. It’s from those two sectors where China’s latest vehicle-carrying super vessels are emerging. 

With a capacity to carry over 10,000 new vehicles for transport from factories in Asia to destinations around the world, these ships, known as roll-on/roll-off (Ro-Ro) ferries, are now the biggest of their type in the world. The concept of the PLA putting civilian ferries into military use is not a new one, or even an idea China is trying to hide. Back in 2021, China held a public military exercise where a civilian ferry was used to transport both troops and a whole arsenal of military vehicles, including main battle tanks.

The relatively limited conventional naval lift capacity of the PLA is something that’s been pointed out while game-planning a Chinese amphibious move on Taiwan, and it’s widely expected that the PLA would lean on repurposed civilian vessels to boost its ability to move soldiers and vehicles across the Taiwan Strait. With these newer, high-capacity Ro-Ro ferries added to the fleet, the PLA’s amphibious capacity and reach could grow significantly.

A makeshift amphibious assault ship

However, even with the added capacity of these massive ferries, military analysts have pointed out that Ro-Ro ships would not be able to deploy vehicles and soliders directly onto a beach the way a purpose-built military amphibious assault ship can. Traditionally, to deploy vehicles from these ships, the PLA would first need to capture and then repurpose Taiwan’s existing commercial port facilities into unloading bases for military vehicles and equipment.

However, maybe most alarming is that satellite imagery and U.S. Intelligence reports show that, along with increasing ferry production output, the PLA is also working on a system of barges and floating dock structures to help turn these civilian ferries into more efficient military transports. With this supporting equipment in place, ferries may not need to use existing port infrastructure to bring their equipment on shore.

Beyond the general military concern over China’s growing amphibious capability, there are also ethical concerns if China is planning to rapidly put a fleet of civilian merchant vessels into military service. If the PLA were to deploy these dual-purpose vessels into direct military operations, the United States and its allies would likely be forced to treat civilian-presenting ships as enemy combatants. On top of all the other strategic challenges a Taiwan invasion would bring, the U.S. having to navigate the blurred legal lines between military and merchant vessels could potentially give China a strategic advantage amidst the fog of war.





Source link