Key Takeaways

  • The newly launched platform is targeting managed service providers with agentic AI designed around lead generation, sales execution and measurable revenue growth.
  • Strong demand for packaged AI creates an opening, but MSP adoption remains commercially uneven and buyers increasingly expect evidence of return on investment.
  • Governance, data quality and human oversight will shape whether automated sales agents become durable operating tools or short-lived experiments.

Levelled is taking agentic AI into a part of the managed services market where automation has often remained fragmented: commercial growth. The Leeds-based business has launched a platform built exclusively for managed service providers, positioning it as a revenue engine that can support lead generation and sales execution while making outcomes easier to measure.

That focus matters. MSPs have spent years automating service desks, endpoint management, billing and infrastructure monitoring, yet sales activity can still depend on manual prospecting, inconsistent follow-up and information scattered across different systems. The company is betting that AI agents can take on more of that operational workload, rather than serving only as writing assistants or isolated chat interfaces.

The timing is favorable, at least on paper. Research reported by the MSP Summit found that 66% of MSP 501 companies were purchasing prebuilt AI solutions, while 72% were using Microsoft Copilot. Enterprise generative AI investment also reached $37 billion in 2025, compared with $11.5 billion in 2024, and 76% of solutions were purchased rather than developed internally (source).

In other words, MSPs appear more comfortable buying packaged capabilities than assembling AI systems from scratch. That preference could help the platform, particularly among smaller providers that lack dedicated data science and AI engineering teams. These businesses may understand the potential of agentic workflows but have limited appetite for lengthy integration projects.

Adoption is broadening too. The MSP Global State of the Industry Report 2025 found that 47% of surveyed MSPs offered AI services in late 2025, an increase of 8 percentage points from the previous quarter. More than half identified investment in AI and automation as a strategic priority. Still, offering an AI service and generating dependable margin from it are different things.

Sales automation is a sensitive proving ground. An agent that drafts an internal summary presents one level of risk. One that selects prospects, initiates outreach or influences pipeline decisions can affect brand reputation, regulatory compliance and customer trust. How much autonomy should an MSP give a system communicating with potential buyers?

The provider therefore enters a market where measurable commercial results will count more than the agentic label. Relevant indicators could include qualified opportunities created, conversion rates, sales-cycle duration, cost per acquisition and the amount of staff time recovered. MSP leaders will also want visibility into why an agent took an action, which data it used and when a person can intervene.

The warning signs are already visible across the wider market. Gartner forecasts that 33% of enterprise software applications will include agentic AI by 2028, up from less than 1% in 2024. At the same time, Gartner predicts that more than 40% of agentic AI projects will be cancelled by 2027 because of escalating costs, unclear business value or inadequate risk controls. The opportunity is substantial, but so is the chance of expensive experimentation.

That said, MSPs may be well placed to manage this transition. Their existing role already covers technology selection, integration, monitoring, security and ongoing support. Agentic AI extends those responsibilities into model behavior, permissions and data governance. Guidance such as the NIST AI Risk Management Framework can help teams assess reliability, transparency and potential harm, while the emerging Model Context Protocol offers a more standardized way for AI applications to connect with tools and external data.

The operational model will matter as much as the underlying model. Microsoft Copilot and Anthropic Claude have helped normalize AI-assisted work, but a revenue-focused agent needs access to business context such as customer records, service portfolios and approved messaging. That access should be tightly scoped. Poor records or excessive permissions can turn a fast agent into a fast source of mistakes.

There is also a human angle. A former founder of a UK MSP brings direct relevance to the commercial challenges the technology is addressing. MSP founders often balance technical delivery with pipeline creation, account management and recruitment. A system that reduces repetitive sales work could be valuable, particularly during growth phases when adding headcount carries risk.

As Deloitte has noted in its work on agentic AI strategy, organizations are moving toward environments where people supervise increasingly capable digital workers. For Levelled, the near-term test is simpler and tougher: whether MSPs can connect its automated activity to credible revenue outcomes without losing control of customer relationships.