Novo will take an equity stake in Cellular Intelligence and is in line for future milestone payments and royalties. The startup plans to apply its AI platform to STEM-PD, a stem-cell-derived treatment Novo discontinued last October.
Novo Nordisk has handed over STEM-PD, an experimental stem-cell therapy for Parkinson’s disease, to Cellular Intelligence, an AI biotech backed by Mark Zuckerberg, the companies said on Monday.
Financial terms were not disclosed. Novo will take an equity stake in the startup and is eligible for future milestone payments and royalties. Novo discontinued the therapy last October when it shut down its cell-therapy unit during a broader restructuring.
STEM-PD is an allogeneic, stem-cell-derived treatment designed to replace dopamine-producing nerve cells lost in Parkinson’s disease. It had been in early clinical development at Novo before the unit was wound down.
Cellular Intelligence said it intends to use its proprietary AI platform to accelerate the programme, scale manufacturing and bring down production costs.
Cellular Intelligence has raised more than $60m to date from investors including Khosla Ventures and the Chan Zuckerberg Initiative, Zuckerberg and Priscilla Chan’s philanthropic vehicle.
The deal extends an investment pattern that has seen CZI commit substantial capital to AI-driven biology in recent months.
For Novo, the deal closes out one of the loose ends from last October’s restructuring, which prioritised the company’s obesity and diabetes franchises after demand for Wegovy and Ozempic continued to outpace supply.
The cell-therapy unit was an early-stage research operation rather than a commercial line; its discontinuation freed engineering and manufacturing capacity for GLP-1 production.
Last week Novo reported that Wegovy held 65% of new US prescriptions and that obesity-care sales rose 22% on a constant-currency basis.
For Cellular Intelligence, the deal gives the company a clinical-stage asset to anchor its AI-platform pitch. STEM-PD is well-characterised; Novo’s prior investment took it through the preclinical and early-clinical stages.
The startup’s case to investors will now combine an in-development therapy with the cost-and-speed claims its AI platform is built around.
There is no disclosed timeline for the next clinical milestone. Cellular Intelligence has not said when it expects to file an IND amendment or what dose-finding work it will pick up from Novo’s earlier programme.
Parkinson’s affects around 10 million people worldwide. Treatments that replace lost dopamine-producing cells have been pursued by multiple research groups for decades; Cellular Intelligence’s bet is that AI-assisted manufacturing and dose selection can make a treatment of this kind commercially viable on a timeline conventional cell-therapy programmes have not been able to match.
The companies did not say whether Novo’s equity stake gives it observer rights, IP back-licensing or any operational role at Cellular Intelligence. The deal closes immediately.
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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