Lack of process visibility undermines automation efforts
Let’s be clear, automating a broken process doesn’t fix it. It makes it fail faster. A lot of companies get excited about automation and AI, which is good. It shows leadership teams are thinking about efficiency. But then, many organizations dive in without first understanding what they’re automating. That’s a fundamental mistake. You have to see the process clearly before you accelerate it. Otherwise, you’re just scaling inefficiencies.
Kerry Brown, transformation evangelist at Celonis, summed it up well: “You don’t know what you don’t know and can’t improve what you can’t see.” That’s the core of it. Without process visibility, you risk automating pain points that should’ve been eliminated in the first place. This leads to wasted budgets, delayed outcomes, and eroded trust in automation itself.
Think of this at the enterprise level. When you’re spending large sums on infrastructure, platforms, or automation tooling, you should first know exactly which parts of your business will see measurable value. Eric Johnson, the CIO at PagerDuty, understands this deeply. He notes that CIOs who can’t translate AI into actual business strategies will struggle to show ROI. And without ROI, you won’t get long-term buy-in from the board.
Process mining, workflow audits, and end-user data bring visibility. They help tech leaders make smart bets, ones that align with metrics your CFO actually cares about. That’s where automation should start. Know the bottlenecks, improve the workflows, and then automate them. It’s how you build momentum that lasts.
Poor data quality compromises automation effectiveness
Automation doesn’t work without data. And poor-quality data turns enterprise AI into just another expensive tool with low impact. This isn’t a niche IT problem, it’s a boardroom issue. Bad data affects customer experience, operations, security, and trust.
Shuai Guan, co-founder and CEO at Thunderbit, put it in plain terms: “Automation tools are designed to process and analyze data at scale, but they rely entirely on the quality of the input data.” That’s reality. If your CRM has incorrect customer data, then your automated marketing flows might target the wrong people. Or worse, annoy current customers. Mistakes like that hurt brand reputation and revenue. They’re avoidable.
Data hygiene isn’t exciting, but it’s foundational. You need structured, consistent, and accurate data, and that takes discipline. It means investing in teams and systems that maintain data pipelines over time, not just during implementation. Too many leaders push data governance to the bottom of the roadmap. That’s probably the most expensive shortcut in digital transformation.
Good data is how you make good decisions at scale. It’s how automation becomes ROI-positive instead of a burden. C-suite leaders should prioritize this. Because without clean data, everything else, analytics, AI, systems integration, loses strategic value. Data doesn’t just support automation. It powers it. Don’t automate before you’re sure your data can handle it.
Misalignment between Task-Level automation and strategic business goals
Too many companies make the mistake of chasing quick automation wins. Yes, reducing ticket volumes or simplifying repetitive tasks might make teams feel like they’re making progress. But this kind of surface-level automation doesn’t move the needle if it’s not solving the actual problems weighing down your operations. You can automate your way into short-term wins that do nothing for long-term business health.
Erik Gaston, CIO at Tanium, made the problem clear. He warned that when CIOs focus only on noise suppression, reducing minor incidents, they often end up ignoring what’s really going wrong beneath the surface. According to him, it’s actually the unattended mid-level problems that evolve into larger, business-impacting issues over time. Suppressing alerts doesn’t fix the reasons those disruptions are happening in the first place.
This is where alignment with the business mission becomes critical. You don’t want disconnected automation initiatives. You want programs that help the company scale, stay agile, and serve customers better. That requires identifying high-leverage, strategic areas where automation can drive clear business value, and then monitoring performance over time.
Muhammad Nabeel, CTO at Begin, emphasized this long-view mindset, urging businesses not to treat automation as “fire and forget.” You don’t launch a tool and walk away. You stay involved. You iterate. You refine. Leaders should expect change. Technology evolves, customer needs shift, and so do business models. Automation should be flexible, not frozen.
If automation isn’t aligned with business context and evolving goals, it becomes shelfware. The point isn’t to automate everything. The point is to automate what matters. And that takes insight, not just bandwidth.
Insufficient integration planning for automation tools
One of the most common points of failure in automation rollouts is poor integration planning. Companies underestimate what it takes to make automation work across legacy systems, cloud environments, and increasingly complex IT architectures. Whether you’re deploying scripted workflows or advanced AI, if your systems don’t work together, you’re just adding complexity.
Mason Goshorn, senior security solutions engineer at Blink Ops, called out this risk clearly. Too many teams assume AI-driven automation can run smoothly right out of the box without real-time oversight or coordination with human operators. That’s wrong. Human input is still essential, particularly for error handling, bias detection, and managing emerging security risks.
Beyond that, Goshorn warned about vendor lock-in. This happens when companies adopt tools that don’t support open standards or require dedicated ecosystems to operate. If your automation platform is incompatible with the rest of your environment, it reduces optionality and forces you into rigid architectures, limiting future adaptability.
Smart integration planning is not optional. It’s a necessity. You have to build interoperability into your automation roadmap from day one. That means identifying points of friction across data systems, infrastructure, and departments. And then choosing technologies that play well together, transparent APIs, modular designs, and scalable frameworks.
IT complexity isn’t going away. Automation should reduce it, not stack on more. If you don’t plan for integration, you’re setting yourself up for silos, inefficiencies, and stalled rollouts. Automation is only as powerful as its connections. Handle them well.
Premature automation of unsuitable processes reduces ROI
Not every process is worth automating. Chasing immediate wins by automating simple tasks often creates a false sense of progress. That kind of rushed implementation ignores the real cost drivers, like stability, complexity, and future change in operating conditions. If a process is expected to evolve or requires high-level human judgment, automation may add more overhead than value.
David Brauchler, technical director & head of AI and ML security at NCC Group, cautioned that leaders need to look beyond surface-level savings. Automation requires investment. If a process doesn’t happen often, or takes minimal time to complete manually, automating it may burn through time and capital without producing measurable ROI. Leaders must be disciplined and avoid automating for appearance or convenience.
Sourya Biswas, also with NCC Group as technical director, risk management and governance, explained that when a process involves human reasoning, emotion, or inconsistent rules, it should be deprioritized. AI is improving quickly, but it’s not always cost-effective or operationally sound to force it into decisions it’s not ready to make. He also flagged that automating processes likely to undergo significant operational changes soon will make it difficult to recover the initial investment before the automation becomes obsolete.
Executives need to set clear qualification criteria for automation candidates, starting with the question: is the process stable, repetitive, rule-based, and frequent enough to justify the investment? If the answer is no, skip it, for now. The goal is not to automate as much as possible. The goal is to automate where it delivers sustained, measurable returns.
Overemphasis on cost savings may undermine automation value
Focusing too much on cutting costs can backfire. Yes, automation can save money. But if cost is the sole metric, companies end up choosing tools that are difficult to scale, integrate, or operate. That trade-off can quietly erode productivity and innovation over time. Choosing the cheapest option may slow down your teams and lock you into outdated systems.
Derek Ashmore, application transformation principal at Asperitas, raised a strong warning here. He pointed out that many CIOs fall into the trap of evaluating automation technologies purely through the lens of cost, while completely overlooking factors like agility, scalability, customer experience, and innovation capacity. When those broader strategic dimensions are ignored, automation doesn’t build competitive advantage.
The real power of automation comes when cost savings align with productivity gains and business growth. Better decision speed, higher output quality, and seamless customer engagement add significant business value, even if they don’t show up immediately on the budget sheet.
Executives should consider lifetime cost, integration effort, and the potential downsides of vendor lock-in. Evaluate automation tools for flexibility, openness, and alignment with longer-term transformation goals. Simply reducing today’s spending without accounting for tomorrow’s limitations is the wrong trade.
Leaders need to think long-term. Cost control matters, but not at the expense of agility and innovation. The strongest automation strategies balance economics with strategic optimization. Don’t focus narrowly. Focus smart.
Evolving regulations and compliance burdens increase automation complexity
Regulation isn’t slowing down. In fact, it’s accelerating, and automation is directly in the crosshairs. Enterprises rolling out AI-driven systems or large-scale process automation in 2025 are navigating a tightening web of compliance rules. Data security, privacy, transparency, and ethical deployment standards are all shifting fast, especially across jurisdictions.
Chris Drumgoole, EVP of Global Infrastructure Services at DXC Technology, put it plainly: CIOs need to be vigilant about emerging regulatory requirements that could disrupt automation strategies. This includes understanding both industry-specific mandates and broader regulations related to how automated systems store, process, and protect data.
The U.S. is now seeing a surge in state-level AI-focused legislation. Derek Ashmore from Asperitas highlighted that hundreds of regulatory proposals are being drafted addressing AI’s use in consumer protection, decision-making, chatbots, and even data center energy consumption. These aren’t just theoretical risks, they’re legislative realities business leaders must be ready for.
Compliance is no longer a legal sidebar. It must be built into automation plans from the start. Executive teams need real-time legal advisory support, proactive audits, and internal policy alignment to avoid regulatory setbacks. You don’t get flexibility after a rule is passed. You stay ahead by designing your automation systems for traceability, control, and compliance by default.
Ignoring regulation delays growth and opens up legal risk. Treat it as a constant. Build systems that are agile, observable, and defensible, across every process you automate.
Increased complexity in AI integration and risks from shadow AI
Deploying AI-led automation is no longer a clean-line project with a single implementation team. It now involves cloud compatibility, vendor ecosystems, evolving models, and ungoverned tools entering the business through individual user actions. All of this adds up to a significant increase in operational complexity.
Deepak Singh, President and CTO at Adeptia, emphasized that many enterprises aren’t prepared for the hidden costs and technical pressure that come from integrating AI into large ecosystems. AI tools must be connected across fragmented environments, multi-cloud, hybrid stacks, and legacy infrastructure. Without that integration, automation becomes more of a load than a lift.
Then there’s shadow AI, the silent disruptor. Business users are rapidly adopting free or low-cost AI subscriptions, often without any IT oversight. These tools are embedded into workflows, devices, and third-party software, sometimes without clear policies for governance or data hygiene. That opens the door to privacy violations, data exposure, and unintended AI training on proprietary information.
CIOs need to increase visibility. They should implement policy-driven restrictions, monitor tools entering the environment, and apply risk scoring to unauthorized AI usage. Legal and security operations must work together to identify non-compliant systems and shut them down, or onboard them formally with guardrails.
If you’re investing in AI, monitor the entire ecosystem. The external build is only one part of the complexity. The internal chaos, shadow usage, fragmented environments, unmanaged models, is what introduces risk. Address it head-on with integrated governance, security-by-design, and clear rules on who uses what, and how.
Talent deficits hinder scalable automation adoption
Technology moves fast, but people need time to adapt. Right now, the biggest constraint on automation isn’t tool availability, it’s the shortage of people who can deploy, manage, and scale it effectively. Most organizations don’t lack ambition. They lack cross-functional talent capable of integrating automation into complex workflows, and doing it securely, reliably, and with business impact in mind.
Tim Gaus, Smart Manufacturing Business Leader at Deloitte, was direct on this point. He stressed that leaders should focus on upskilling existing employees while building long-term talent pipelines. This doesn’t just support adoption, it creates internal capacity to manage the evolution of automation over time. Gaus also made it clear that automation success now demands convergence between IT (Information Technology) and OT (Operational Technology). It’s not about who owns the tool. It’s about shared understanding across domains.
Whether you’re in manufacturing, financial services, or any information-dependent sector, domain expertise paired with automation fluency is what unlocks value. The best automation efforts right now are driven by people who not only understand the technology but also know where and how it can have the biggest impact in their specific business environment.
Leadership teams should rethink how they enable their workforce. Training isn’t a checkbox. It should be constant, strategic, and linked closely to the actual automation initiatives rolling out across the business. Invest in the people who already understand your operational ecosystems, and give them the skills to reshape them with automation.
Lack of talent isn’t just a hiring gap, it’s a serious scalability issue. If your organization can’t run, monitor, and expand automation with internal insight, it becomes dependent on external expertise. That slows you down, drives up cost, and reduces ownership. The path forward is clear: enable your people now or accept limits later.
Recap
Automation will keep moving forward, with or without your organization. But whether it drives measurable gains or creates hidden complexity depends entirely on how it’s executed. This isn’t about chasing hype or deploying every tool on the shelf. It’s about designing automation that aligns with real business priorities, adapts to regulatory shifts, and delivers operational clarity, not chaos.
The fundamentals matter. Visibility before action. Clean data over speed. Strategic focus instead of scattered wins. And above all, talent that can think cross-functionally and execute with both precision and context. Without these, no automation program scales efficiently.
For executive teams, success in automation isn’t just technical, it’s structural. Every tool or platform you deploy should map back to outcomes you can track and trust. Build governance into the design. Control complexity. Scale what works. Ignore what doesn’t. Don’t just automate, lead with intent.
