Key Takeaways
The Challenge: Many organisations find themselves in extended PoC cycles, achieving technical successes while facing genuine obstacles in building the infrastructure needed to scale to production.
The Root Cause: PoC purgatory isn’t a technology problem—it’s an organizational complexity challenge. Transformation requires legitimately difficult decisions involving risks, resource commitments, and change across multiple departments.
The Path Forward: Organisations can break free by reframing PoCs as validation checkpoints (not destinations), building scaling playbooks before the first pilot, and creating forcing functions that make decisions clearer and timelier.
The Reality: Successful scaling requires investment in operational infrastructure, change management, and business-IT partnerships—capabilities that understandably get deprioritised during the pressure for quick wins.
The Opportunity: With the right organisational capabilities, leadership commitment, and operational support, what you’ve already proven in pilots can deliver enterprise-wide value.
Sarah stared at her screen, scrolling through yet another email thread about “next steps” for an automation proof of concept her team had successfully completed six months ago.
The bot worked perfectly. The business case was solid. The stakeholders were impressed during the demo.
Yet here they were, discussing whether to run “just one more test” before moving forward.
Sound familiar?
If you’ve worked in enterprise automation for more than a year, you’ve probably witnessed this scenario play out dozens of times. A promising automation PoC delivers impressive results, generates excitement across the organization, and then… nothing. It sits in limbo, neither killed nor scaled, existing in a strange twilight zone between success and implementation.
This isn’t just frustrating. It’s expensive, and it’s at odds with the transformative potential of automation technology. According to IDC research, 88% of AI and automation proof of concepts fail to transition into production. Ernst & Young reports that 30-50% of initial RPA projects fail outright. Perhaps most telling, Deloitte’s Global RPA Survey found that only 3% of organizations have successfully scaled their digital workforce.
The question isn’t whether automation works. We’ve proven that thousands of times over. The question is why organizations that clearly understand the value of automation face such consistent challenges in moving from proof of concepts to production-scale implementation.
Understanding the PoC Phenomenon
What is Automation PoC Purgatory?
Automation PoC purgatory occurs when organizations find themselves running multiple proof of concepts without successfully transitioning them to production.
These organizations invest in pilot after pilot, achieving technical successes while facing genuine challenges in building the operational infrastructure, securing organizational commitment, and developing the strategic clarity needed to deploy automation at enterprise scale.
The result:
- resources invested without full returns,
- competitive positioning at risk, and
- growing internal questions about whether automation can deliver its promised value in their specific environment.
What is the Purpose of a PoC?
A proof of concept serves a legitimate purpose. It’s meant to validate that a specific automation approach can solve a particular business problem within the constraints of existing systems and processes. The keyword here is “validate.” A PoC should answer specific technical and operational questions, then quickly give way to full implementation.
But something has shifted in how enterprises approach automation PoCs.
What was designed as a validation checkpoint has, for many organizations, become a longer-term state. Organizations often find themselves drawn to the relative safety of small-scale testing, where risks are contained, investments are manageable, and the difficult organizational changes that full automation requires can be deferred.
The Comfort of Perpetual Testing
There’s a natural human element at play. PoCs feel productive—and they are. They demonstrate forward motion and generate valuable learnings. You can show your board meaningful progress in “exploring automation” without immediately having to address the more complex questions around restructuring departments, large-scale retraining, or challenging long-established processes.
This can create an unintended pattern:
- Run an automation PoC.
- Celebrate the results.
- Form a committee to discuss scaling.
- Identify legitimate concerns that suggest another pilot might address remaining questions.
- Repeat.
The automation PoC conundrum isn’t a technology problem—it’s an organizational complexity challenge. The difficult decisions that transformation requires are legitimately hard, involving risks, resource commitments, and change across multiple departments.
Meanwhile, organizations that have successfully navigated the path from PoC to production have built significant advantages in their automation maturity.
Why Organizations Face Scaling Challenges
The reasons organizations find themselves in extended PoC cycles are numerous, interconnected, and often more complex than executives initially realize. Let’s examine the real factors at play.
Top 5 Reasons Automation PoCs Fail to Scale
- Misaligned success metrics that emphasize technical functionality over measurable business value
- Procurement-innovation disconnect creating 6-12 month evaluation timelines
- Natural organizational caution when scaling requires changes to established operations
- Capability gaps in navigating the complex intersection of technical and organizational scaling
- Decision complexity when transitioning from contained pilots to enterprise-wide investments
1. Misaligned Success Metrics
Most organizations measure automation PoC success by whether the bot works. But technical functionality is just table stakes. McKinsey research reveals that approximately 40% of executives found their pilots “exciting” but admitted “it was unclear what the business value actually was.”
Critical questions your PoC must answer
- Does this integrate with our existing technology stack?
- Can our team maintain and scale it independently?
- What organizational changes are required?
- How does this fit our broader automation roadmap?
- What’s the realistic timeline and investment for production?
When these aren’t addressed, organizations end up with a “successful” pilot they don’t know how to implement—so they commission another pilot, hoping for different results.
2. Procurement-Innovation Gap
Technology procurement processes were designed for traditional software purchases, not for initiatives that fundamentally change how work gets done. When an automation PoC succeeds, it triggers procurement workflows that often take 6-12 months to complete.
By then, the business case has aged, the original champion may have moved roles, and the market has shifted. The solution? Run another “refresh” PoC to validate the business case still holds.
This disconnect between innovation speed and procurement speed creates a structural barrier. Organizations need procurement processes that match the pace of digital transformation, not processes designed for 1990s-era software purchases.
3. Organizational Caution
Every organization has built-in mechanisms to carefully evaluate change. These aren’t obstacles—they’re protective systems ensuring initiatives are thoroughly vetted before affecting operations.
Meaningful automation does affect operations. It changes workflows, redistributes tasks, and challenges long-standing assumptions. When an automation PoC moves toward scaling, protective mechanisms naturally engage:
- Compliance considerations requiring deeper exploration
- Security reviews needing thorough evaluation
- Department leaders ensuring team readiness
- Finance teams seeking detailed ROI projections
- Legal reviews identifying risks needing mitigation
Each concern is valid. The challenge? They often occur sequentially rather than in parallel, or the organization lacks clear processes for working through them efficiently. This creates extended timelines that make it easier to defer decisions or run additional pilots.
4. Skills Challenge
Scaling automation requires different skills than running successful pilots. Organizations may have talented people who build effective pilots and experienced project managers who excel at execution. What’s often missing? People with deep experience navigating the complex intersection of technical, political, and organizational factors involved in scaling from 5 processes to 50.
This capability gap shows up in planning. According to Deloitte’s research, 63% of organizations report their time expectations weren’t met, and 37% said cost expectations weren’t met. Teams naturally base estimates on PoC experience, which operates in more controlled environments than production-scale deployment.
When projections differ from reality, it doesn’t reflect poorly on teams—it reflects genuine complexity. However, these variances can understandably impact leadership confidence in larger investments.
5. Decision Weight
PoCs are relatively straightforward to approve: manageable investment, limited scope, contained risk. Production implementations represent something entirely different—significant budget commitments, real organizational change, clear accountability for results.
This naturally shifts decision evaluation. Approving a $50,000 PoC that might not work carries acceptable risk. Approving a $2 million enterprise automation program carries the weight of significant capital allocation and organizational impact.
The challenge? While gathering more information through extended pilots feels risk-averse, it creates different risks: ongoing operational costs, shifting competitive advantages, and frustrated team members eager to see automation succeed.
The Real Cost of Extended Evaluation
The financial cost of automation initiatives in extended pilot phases is substantial, but it’s not the only impact. Let’s examine what organizations may miss when the transition from PoC to production takes longer than planned.
Top 4 Real Cost of Extended PoC Evaluation
- Direct Financial Impact
Organizations face underutilized software licenses, consultant and vendor fees for incomplete implementations, internal team time invested in repeated planning cycles, and opportunity cost of delayed savings and efficiency gains.
- Competitive Positioning Impact
During extended evaluation periods, competitors with scaled automation process customer requests more quickly, operate with lower error rates, redirect human talent to higher-value work, generate better data for decision-making, and build automation competencies that compound over time.
- Team Morale and Cultural Impact
Future automation initiatives face initial skepticism rather than enthusiasm, champions become harder to find as teams experience difficulty getting initiatives past the pilot stage, business units hesitate to volunteer processes for automation, and organizations develop reputations that make it challenging to attract and retain digital transformation talent.
- Vendor Partnership Dynamics
When organizations face scaling challenges, vendor engagement levels shift, reduced support makes successful scaling more difficult, and organizations may find themselves working with partners who specialize in pilot-stage work rather than partners with extensive enterprise-scale deployment experience.
Direct Financial Impact
Extended pilot phases involve ongoing costs:
- Software licenses that are underutilized relative to their potential
- Consultant and vendor fees for implementations that don’t complete as planned
- Internal team time invested in repeated planning and analysis cycles
- Opportunity cost of delayed savings and efficiency gains
Research shows that organizations working through scaling challenges face hidden costs across multiple dimensions—from repeated vendor evaluations to developing multiple business cases for similar processes.
Competitive Positioning Impact
Automation delivers benefits beyond cost reduction—including operational agility, data quality, customer experience, and competitive positioning. During extended evaluation periods, organizations with scaled automation continue building advantages:
- Processing customer requests more quickly
- Operating with lower error rates
- Redirecting human talent to higher-value work
- Generating better data for decision-making
- Building automation competencies that compound over time
This advantage gap tends to accelerate over time, as automation capabilities enable organizations to create increasingly sophisticated operations.
Impact on Team Morale and Future Initiatives
Perhaps one of the more challenging costs is the impact on organizational culture. When team members watch multiple automation PoC initiatives generate excitement and then face scaling obstacles, it naturally affects their outlook on future initiatives. This shift in perspective can become self-reinforcing.
Future automation initiatives may face initial skepticism rather than enthusiasm. Champions may be harder to find—not because people don’t believe in automation, but because they’ve experienced the difficulty of getting initiatives past the pilot stage. Business units may hesitate to volunteer processes for automation. The organization may develop a reputation that makes it more challenging to attract and retain talent excited about digital transformation.
This cultural impact is reversible, but it requires demonstrating that the organization can successfully move promising pilots into production.
Vendor Partnership Dynamics
Automation technology vendors and implementation partners naturally invest significant resources in proof of concepts with the expectation of production-scale engagements. When organizations face challenges scaling, vendor engagement levels may shift.
This can create a challenging cycle: reduced vendor support makes successful scaling more difficult, which may further affect the partnership dynamic. Over time, organizations may find themselves working with partners who specialize in pilot-stage work rather than partners with extensive enterprise-scale deployment experience.
Maintaining strong vendor relationships through clear communication about scaling timelines and challenges helps ensure access to the expertise and support needed when the organization is ready to scale.
Moving Forward: Strategies That Work
Successfully transitioning from PoC to production requires addressing the organizational, technical, and process challenges we’ve explored. Here are strategies that have proven effective for organizations that have successfully made this transition.
How to Transition from PoC to Production?
- Reframe the PoC Purpose
- Build a Scaling Playbook Before the First PoC
- Establish Clear Decision Frameworks
- Embrace Clear Go/No-Go Decisions
Strategy 1: Reframe the PoC Purpose
Stop treating proof of concepts as ends in themselves. A PoC should exclusively answer specific go/no-go questions that block production implementation. If it doesn’t answer those questions, don’t run it.
Before Launching Any Automation PoC, Document:
- The specific hypothesis being tested: Not “can we automate invoice processing” but “can our current infrastructure support automated invoice extraction at 95%+ accuracy for our top 10 vendor formats”
- Success criteria: Measurable, specific thresholds that determine pass/fail
- The production implementation plan: What happens if the PoC succeeds, including timeline, budget, and resource requirements
- Decision authority: Who has the actual authority to approve scaling, and have they committed to making that decision based on PoC results
This discipline eliminates most unproductive PoCs. If you can’t define these elements, you’re not ready for a proof of concept. You need strategic clarity first.
Strategy 2: Build a Scaling Playbook Before the First PoC
Organizations that successfully scale automation don’t figure it out after the PoC. They build the playbook first. This includes:
Technical Requirements | Organizational Requirements | Resource Allocation |
Infrastructure standards for production bots | Change management approach | Dedicated scaling team, not project-based resources |
Security and compliance frameworks | Training programs for affected teams | Protected budget for production implementation |
Integration patterns with core systems | Governance model for prioritization and oversight | Executive sponsorship with explicit accountability |
Development and deployment standards | Success metrics and reporting cadence | Vendor/partner relationships structured for scale |
Support and maintenance models | Risk management framework |
|
When the automation PoC succeeds, you’re not starting from zero. You’re executing a proven playbook.
Strategy 3: Establish Clear Decision Frameworks
Extended evaluation periods often result from unclear decision authority and indefinite timelines. Establishing clear frameworks helps. When launching a proof of concept, consider establishing:
- Defined decision timeline: “The steering committee will make a scale/kill decision on [specific date], 60 days after PoC completion”
- Pre-approved resources: “If success criteria are met, the CFO has approved $X budget for production implementation starting [date]”
- Resource planning: “The scaling team will be allocated to this initiative for 6 months starting [date] if PoC succeeds”
These frameworks bring clarity to the decision process and help ensure that successful pilots can move forward efficiently while maintaining appropriate governance.
Strategy 4: Embrace Clear Go/No-Go Decisions
Not every automation opportunity should scale, and recognizing this early is valuable. Some PoCs reveal that the approach isn’t viable, and that’s an important insight—it saves the organization from larger investments in approaches that won’t deliver expected returns.
Building a culture where clear outcomes—whether positive or negative—are valued helps the organization learn and improve. This requires:
- Rigor in defining success and failure criteria upfront
- Leadership that recognizes the value of learning what doesn’t work
- Transparent communication about why certain approaches weren’t pursued and what was learned
- Quick transition to alternative approaches when one path proves unviable
Organizations with mature automation programs make clear go/no-go decisions on roughly 30% of their PoCs, then apply those learnings to strengthen subsequent initiatives.
Conclusion
The automation PoC challenge isn’t about technology—it’s about organizational readiness and operational infrastructure. Organizations stay in extended pilots because scaling involves complex decisions about resources, risk, and change.
The good news? These capabilities are learnable. Organizations facing these challenges five years ago are now automation leaders. They succeeded by building scaling playbooks, investing in infrastructure, and establishing clear decision frameworks.
As automation becomes essential for competition, the gap between organizations that scale and those evaluating continues to widen. Every quarter in extended evaluation means competitors gain advantages while your costs remain high.
The question isn’t whether your PoC demonstrates value—it likely does. The question is whether you’re ready to build the capabilities needed to capture that value at scale.
With the right approach and support, that transition is within reach.
Key Automation PoC Statistics
The challenges outlined in this article aren’t just anecdotal—they’re backed by extensive industry research:
- 88% of automation PoCs never reach production (IDC)
- 30-50% of RPA projects fail outright (Ernst & Young)
- Only 3% successfully scale their digital workforce (Deloitte Global RPA Survey)
- 40% of pilots show unclear business value (McKinsey)
- 63% miss time expectations, 37% miss cost expectations (Deloitte)
These statistics demonstrate that PoC purgatory is a widespread industry challenge, not an isolated problem. The gap between pilot success and production scaling represents billions in wasted investment and unrealized value across enterprises globally.
Frequently Asked Questions (FAQs)
What is PoC in Automation?
A PoC (Proof of Concept) is a small-scale test that validates whether an automation approach can solve a business problem. It runs 4-8 weeks to answer: Can the technology handle our data, integrate with systems, and achieve required accuracy? The goal: prove feasibility and make a clear go/no-go decision.
What is the full form of PoC in RPA?
PoC stands for Proof of Concept in RPA (Robotic Process Automation). It involves building a basic bot to automate a single process, demonstrating the platform can interact with applications and deliver results before enterprise-wide implementation.
How to create PoC for automation?
Follow a 6-8 week approach: Define objectives and success criteria, select a rule-based high-volume process, set up test environment with realistic data, build and test with users, measure results against baseline, then decide to scale or kill—no repeat PoCs.
Why do most automation PoCs face scaling challenges?
88% of PoCs don’t reach production (IDC) due to five factors: metrics emphasizing technical function over business value, extended procurement timelines, change management complexities, capability gaps in enterprise scaling, and decision complexity moving from pilots to production investments.
What’s the difference between a PoC and a pilot in automation?
A PoC validates technical feasibility (4-6 weeks, sample data, yes/no capability questions). A pilot tests business viability (2-3 months, real data with users, measures ROI). Many organizations blur these stages, running multiple “PoCs” that delay scaling.
How long should an automation PoC take?
4-8 weeks maximum: Week 1-2 for setup, weeks 3-6 for build and test, week 7 for measurement, week 8 for decision. PoCs beyond 10 weeks indicate scope creep. Organizations often run 3-6 month “PoCs” that become indefinite pilots.
What percentage of automation projects actually succeed?
88% of PoCs fail to reach production (IDC), 30-50% fail entirely (E&Y), and only 3% scale successfully (Deloitte). However, organizations with proper infrastructure see 80% of pilots transition to production—success comes from capabilities, not technology.
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