Enterprise AI has a scaling problem. Pilots are everywhere. Production wins are not.

Now, TQA—long known for its automation credentials—is rebranding and formally expanding into Agentic AI, betting that enterprises don’t need more AI demos. They need AI that survives contact with real workflows.

The move positions TQA as a partner for companies stuck between generative AI experimentation and measurable business impact—a gap that, according to industry research, still swallows the majority of enterprise AI initiatives.

From Automation Specialist to Agentic AI Enabler

TQA’s rebrand is more than a cosmetic refresh. The company is reframing its mission around helping enterprises build what it calls an “agent-enabled workforce”—AI-powered agents embedded directly into business processes rather than bolted onto them.

Tom Abbott, Founder and Chief Revenue Officer at TQA, describes the current state bluntly: enterprises are piloting agentic solutions but struggling to move into active production.

The core issue? Many organizations attempt to layer AI tools on top of legacy processes without rethinking the underlying workflow architecture. The result is fragmented deployments that generate excitement—but not financial returns.

That observation aligns with a broader market reality. Despite heavy investment in generative AI since 2023, many enterprises report limited bottom-line impact. Scaling AI requires governance, orchestration, integration, and process redesign—not just access to large language models.

TQA’s strategy is to anchor AI initiatives in workflow reinvention, combining automation heritage with agent-based intelligence.

Expanding the Ecosystem: Microsoft and ServiceNow

To support multi-platform enterprise environments, TQA is formally introducing dedicated practices around Microsoft and ServiceNow—two ecosystems increasingly central to enterprise AI strategy.

Microsoft Practice

TQA will integrate:

• Microsoft Copilot

• Power Platform

• Azure AI

By embedding AI inside core enterprise systems, TQA aims to deliver secure, scalable solutions that align with governance and compliance requirements. This is particularly relevant as Microsoft continues pushing Copilot deeper into productivity and business applications, making AI-native workflows more accessible—but also more complex to manage.

ServiceNow Practice

On the ServiceNow side, TQA positions itself as a consulting and implementation partner specializing in Workflow Data Fabric (WDF) and AI agents.

ServiceNow’s evolution from IT service management tool to enterprise workflow platform makes it a logical anchor for AI-driven transformation. By modernizing legacy workflows inside ServiceNow and layering agentic capabilities, TQA aims to help enterprises shift from reactive process automation to outcome-driven orchestration.

The multi-platform strategy reflects enterprise reality: large organizations rarely standardize on a single AI stack. Instead, they operate across cloud providers, SaaS ecosystems, and legacy systems—requiring integrators who can connect the dots.

Staying Deep with UiPath

While broadening its alliances, TQA is doubling down on its long-standing relationship with UiPath.

The company remains a UiPath Diamond Partner across Europe and North America and was among the first to earn recognition as a UiPath Fast Track Partner for agentic AI capabilities. It has also won multiple awards for industry-specific UiPath solutions.

UiPath’s shift from traditional RPA to agentic automation mirrors TQA’s own repositioning. As automation evolves into intelligent orchestration—where AI agents, bots, and humans collaborate—partners capable of bridging legacy automation with next-generation AI become strategically important.

TQA describes its approach as “best-of-breed,” integrating:

• Legacy systems

• Cloud infrastructure

• Modern AI platforms

In practice, that means fewer isolated AI experiments and more end-to-end workflow redesign.

The Real Battleground: Production, Not Pilots

The rebrand arrives at a pivotal moment for enterprise AI.

The first wave of generative AI adoption focused on experimentation: chatbots, copilots, proof-of-concepts. The second wave is about operationalization—embedding AI agents into revenue-generating and cost-saving processes.

This is where many initiatives stall.

Enterprises face challenges around:

• Data readiness

• Governance and compliance

• Change management

• Cross-platform orchestration

• ROI accountability

By focusing on Agentic AI as a workflow transformation strategy rather than a toolset, TQA is targeting that bottleneck directly.

Abbott’s promise—AI-powered agents that “actually work in the real world”—isn’t flashy marketing. It’s a response to buyer fatigue. After years of AI hype cycles, enterprises are demanding proof of production-scale impact.

Why This Matters for MarTech and Beyond

For marketing and revenue teams in particular, the rise of agentic AI signals a shift from task automation to decision orchestration.

Instead of automating isolated steps—like data entry or campaign triggers—agentic systems can coordinate across platforms, analyze context, and execute multi-step processes autonomously.

But that requires deep integration with systems like Microsoft’s enterprise stack, ServiceNow’s workflow engine, and automation platforms such as UiPath.

TQA’s expanded ecosystem approach positions it as a systems integrator for this new phase of AI maturity—less startup experimentation, more enterprise engineering.

A Calculated Pivot, Not a Trend Chase

Unlike newer AI consultancies built entirely around generative AI, TQA enters the Agentic AI arena with a long history in intelligent automation.

That heritage may prove advantageous. Enterprises looking to modernize workflows often prefer partners who understand process mapping, governance, and enterprise architecture—not just prompt engineering.

The question now isn’t whether enterprises will adopt Agentic AI. It’s how quickly they can transition from curiosity to controlled, scalable deployment.

TQA’s rebrand suggests the company believes that the real opportunity lies not in inventing new AI tools—but in helping organizations finally make them work.

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