Enterprises that invest in robust data foundations today will be able to deploy agentic systems more effectively tomorrow. Accurate historical data is essential for forecasting, decision-making, and intelligent automation. Without it, even sophisticated agents will generate unreliable or hallucinatory outputs.
Despite advances in AI, the core challenges of enterprise data remain. Incompatible data models, inconsistent nomenclature, unpredictable free-text inputs, and flawed pipelines still create obstacles. Garbage in still equals garbage out. High-quality, reliable data is now more critical than ever. Strong LLMs are widely available and no longer a differentiator. What matters is how enterprises apply AI to their own processes and provide accurate, contextualized data.
Agentic AI Primitives Are Advancing Quickly
The key lesson for 2026 is flexibility. Enterprises should avoid heavy lock-in and design systems that can evolve as tools improve. Investments in training, culture, and strategic adoption are as important as the choice of platform. Companies that focus on adaptable solutions today will be positioned to adopt next-generation agentic capabilities without repeating costly cycles.
Cloud-based agentic platforms such as Azure Foundry and Databricks Agent Bricks simplify memory management, context augmentation, and deployment. Multi-cloud platform connectors promise to streamline system integration. No-code and low-code platforms make experimentation and internal automation faster and cheaper. While not yet suitable for client-facing production, these tools allow enterprises to prototype solutions and validate processes quickly.
Physical action carries real-world risk. Unlike text, errors in the physical world can cause harm, slowing enterprise adoption. Despite these hurdles, LAMs represent the next logical step beyond LLMs. The rapid adoption of LLMs ensures that research into LAMs will accelerate in the coming years. Today, however, agentic systems dominate. They may lack physical capabilities, but their versatility and adaptability already outperform traditional rules-based automation when paired with proper orchestration and safeguards. Enterprises can already deploy agentic AI to manage complex workflows, schedule tasks, and integrate across multiple tools, delivering measurable operational gains.
Physical AI Maturity Is Emerging
Homomorphic encryption allows computations on encrypted inputs but increases training costs. Anonymization and synthetic data generation reduce exposure but can degrade model quality. Balancing privacy with performance will be a critical consideration in 2026. Enterprises that embed privacy-preserving techniques and compliance practices into their AI pipelines will gain a competitive advantage by reducing legal risk and building trust with clients and partners.
The most successful organizations will treat AI as a strategic capability rather than a tool. They will invest in culture, governance, and adaptable systems, enabling them to pivot quickly as new platforms, standards, and innovations emerge. Enterprises that embrace flexibility and prioritize data quality, security, and interoperability will lead in operational efficiency, product innovation, and competitive agility.
By Vitor Avancini
Data Quality Bottlenecks Persist
Enterprises are cycling through AI adoption faster than they ever did with traditional software. Teams initially attempt to build everything themselves, developing custom memory components, context augmentation layers, and integrations to external systems. Early tools often fail to meet expectations, leading to repeated build-or-buy decisions.
Real Large Action Models, or LAMs, are still in their infancy. Early examples like Google’s RT-2 or consumer devices such as Rabbit R1 exist, but consistent production-grade outcomes remain elusive. LAMs require enormous amounts of diverse training data covering environmental states, actions, and feedback loops. Gathering this data at scale is both expensive and technically challenging.
Digital twins and simulation platforms will also accelerate R&D cycles. Engineers will test processes virtually, optimize production lines, and validate new products before committing physical resources. These capabilities reduce risk, speed innovation, and democratize access to advanced experimentation tools. Enterprises that embrace this shift early will gain both cost and time advantages.
Privacy and Security Are Critical Constraints
Training AI with enterprise or public data introduces privacy and compliance challenges. GDPR mandates consent and the ability to remove user data, yet erasing information from trained models remains unsolved. Enterprises are experimenting with anonymization, synthetic data, and homomorphic encryption to reduce risk.
Enterprises entering 2026 must balance innovation with caution. Agentic AI can already automate complex processes effectively, while LAMs remain experimental. Physical AI and digital twin ecosystems are democratizing access to advanced simulation, reshaping industrial R&D. High-quality data, privacy safeguards, and flexible deployment architectures will determine which enterprises turn emerging AI capabilities into tangible business advantage.
Preparing for 2026
The competitive frontier will shift. Enterprises must manage cloud simulation spend effectively, adopt open standards such as OpenUSD to avoid vendor lock-in, and address data quality bottlenecks. The new era of Physical AI will reward companies that integrate simulation and AI into development pipelines efficiently, while legacy vendors relying on proprietary hardware and high-priced integration services risk being sidelined.
Knowledge graphs, ontologies, and AI-assisted documentation tools help agents operate within domain-specific constraints. AI can tag and document datasets automatically, reducing manual curation effort and improving reliability. Sound governance, separation of environments, testing, and disciplined development workflows remain essential. AI accelerates these practices but does not replace them.
Physical AI ecosystems like NVIDIA’s Omniverse and Apollo, combined with interoperability standards like IEEE P2874, are set to reshape industrial R&D in 2026. Cloud-based simulation, robotics workflows, and digital twins will transition from high CAPEX models to pay-as-you-simulate OPEX. Smaller companies will now access advanced robotics and simulations once reserved for capital-rich incumbents.
