A New Impetus for Enterprise AI

Conduct, a London-based AI startup, recently announced the completion of a $60 million Series A funding round. This brings the total capital raised by the company to approximately $72 million, including a previous $12 million seed round. Founded by three former Palantir employees, Conduct aims to address one of the most persistent challenges in the enterprise IT landscape: the modernization of ERP (Enterprise Resource Planning) systems.

This investment round was co-led by prominent new investors such as Index Ventures and Iconiq, with a significant strategic investment from SAP, a dominant player in the ERP sector. Existing investors, including Creandum, Lucid Capital, and Booom, also participated. This influx of capital is earmarked to support the expansion of Conduct's engineering and go-to-market teams, among other strategic areas, as the 2024-founded company continues to grow and hire in the US, building on its current base of approximately 35 employees in London.

Artificial Intelligence Serving Legacy ERP Systems

At the core of Conduct's value proposition is its ability to leverage artificial intelligence to analyze and understand complex, aging ERP systems. The company's software scrutinizes code and configurations across enterprise systems, then uses AI to process this data. The goal is to help companies better comprehend their ERP systems, accelerate software changes, support migrations to new SAP versions, and reduce IT maintenance costs.

The focus on SAP is not coincidental, given its position as the dominant global ERP provider. Many large enterprises, including Conduct's customers like Daimler Truck, Heidelberg Materials, and DHL, rely on these systems for their critical operations. However, decades of customizations and layering have made these environments extremely opaque, difficult to manage, and challenging to innovate, even for human experts. Conduct's approach aims to make these systems 'legible and operable' for AI agents, thereby unlocking new possibilities for automation and optimization.

Implications for Deployment and Data Sovereignty

While the source does not specify the deployment model for Conduct's software, its application to critical ERP systems raises important considerations for CTOs and infrastructure architects. The management of sensitive and strategic enterprise data, often subject to stringent compliance regulations (such as GDPR), makes the choice of infrastructure for AI solutions a decisive factor. Organizations must carefully evaluate whether to opt for cloud-based solutions or self-hosted/on-premise deployments.

On-premise or hybrid deployments offer greater control over data sovereignty, security, and compliance—crucial aspects when integrating LLMs or other AI models with ERP systems containing proprietary and confidential information. This approach can also influence the Total Cost of Ownership (TCO), balancing initial capital expenditures (CapEx) with operational expenditures (OpEx) and considering factors such as latency, throughput, and the management of air-gapped environments. For organizations evaluating the deployment of AI solutions for critical workloads, especially those involving sensitive data from ERP systems, the choice between cloud and self-hosted infrastructures is paramount. AI-RADAR offers analytical frameworks on /llm-onpremise to delve into these trade-offs, considering aspects such as TCO, compliance, and the need for air-gapped environments.

Future Prospects and the Opacity of Complex Systems

Jan Philipp Haas, CEO and co-founder of Conduct, highlighted the challenge faced by large enterprises: the demand for concrete AI results, often hindered by the inability to fully comprehend the systems on which AI is supposed to operate. 'Decades of customization have made them opaque, even to the people running them,' Haas stated. This opacity, which slows down human operators, completely stops AI agents, as an agent can only act on a system it understands.

The investment in Conduct reflects a growing market awareness of the need for tools that can 'unveil' the complexity of legacy systems. Making these systems legible and operable for AI is the foundation upon which future innovations and automations can be built. The expansion of Conduct's teams and the interest from a strategic partner like SAP indicate a clear direction towards deep AI integration to solve infrastructural and operational problems that have, until now, significantly hampered the digital transformation of large enterprises.