The grace period for AI experimentation is over. The challenge now is successful AI implementation – yet most companies see no real return on their investment. On a closer look the problem isn't the technology; it's a strategy built on flawed assumptions. This article from our Simon-Kucher Elevate experts debunks the six myths killing AI value creation and provides a clear playbook for bridging the gap between potential and profit.
In this second part of our three-part series on AI, we uncover why 95% of AI initiatives fail – and how to avoid stepping into the most common pitfalls and myths.
The AI paradox: High spending, low impact
Nowadays, leaders are pouring billions into AI initiatives, yet an estimated 95% of these projects fail to deliver a measurable return.
This dangerous gap between investment and impact is creating a divide between companies that merely experiment with AI – and those that have built up long-term data strategies and are successfully implementing AI for their competitive advantage.
The reason for this widespread failure of many projects is a set of flawed assumptions about how artificial intelligence (AI) creates value. These myths lead to scattered pilots, frustrated teams, and wasted budgets. This Simon-Kucher Elevate blog isn't another piece celebrating AI's potential – it's a guide to the operational realities of a successful AI implementation.
Myth 1: “AI automatically boosts productivity”
The reality: Productivity is not self-executing. The expectation of a simple, plug-and-play productivity boost from new AI tools is seductive but fundamentally flawed. Treat AI as a managed change in how work gets done – and the numbers start to move for the right reasons.
While studies show that generative AI can accelerate routine tasks and help less experienced employees close skill gaps, the impact on complex work is far more nuanced. For experienced developers, for example, using language models has been shown to increase total implementation time on complex tasks, as nominal time savings on drafting are often erased by added review cycles and debugging.
Productivity isn't a feature you install – it's a system outcome you must design.
What to do instead:
- Redesign the workflow, not just the toolset. Adapt your review process and decision checkpoints – which still require human oversight – to capture the big efficiency gains.
- Target the right tasks. Prioritize AI for routine work and to support less experienced staff, where the uplift is most reliable.
- Measure what matters. Track the end-to-end cycle time, quality, and rework rates – not just how quickly a first draft or a first prototype is generated.
How Simon-Kucher Elevate helps: We help our clients move beyond the tools and analyze the entire workflow. Our experts identify the specific bottlenecks where AI can deliver the highest return and help redesign processes to ensure that individual time savings translate into measurable throughput gains for the entire team.
Myth 2: “Employee time savings equal company benefits”
The reality: Individual efficiency doesn't automatically translate to enterprise value. Instead, build the processes that convert gains into business outcomes, and not just individual gains.
Without active management, however, time saved by employees tends to drift into low-leverage activities like internal meetings and email. True AI adoption is not a given – employees may hesitate without clear guidance on how to reinvest their freed-up capacity into work that requires uniquely human creativity and strategic thought.
What to do instead:
- Redirect saved capacity with intent. Translate team-level time savings into concrete business goals (OKRs, sales quotas) that are tied directly to the P&L.
- Establish clear usage norms. Replace shadow usage with managed guidelines on where AI is allowed, what “good” AI usage looks like, and when escalation is required.
- Institutionalize what works. Create shared playbooks to scale successful AI patterns across the organization.
How Simon-Kucher Elevate helps: We are highly experienced in the change management required for successful AI adoption. Our experts work with your leadership team to define KPIs and operating models that explicitly redirect saved time toward high-value activities – ensuring that efficiency gains are converted into tangible business outcomes.
Myth 3: “AI is an IT topic”
The reality: This is one of the most dangerous assumptions. While IT provides the infrastructure for AI systems, value is created on the business side – in the interplay of pricing, marketing, and sales decisions.
Therefore, a successful AI deployment is a business transformation exercise, not just a new tech setup. The organizations that win understand this and avoid leaving their AI implementation solely to the IT department, recognizing that the scoreboard is the P&L – not the tech stack.
What to do instead:
- Put business owners in the lead. Anchor every AI use case in a line-of-business KPI (e.g., lead qualification, churn) and give the P&L owner the final say.
- Run cross-functional teams. When implementing AI, a successful pod includes a business lead, process owner, data expert, and IT enabler working in concert.
- Redesign processes end-to-end. Don’t just bolt AI onto legacy workflows; rethink the entire process to maximize its impact.
How Simon-Kucher Elevate helps: Our unique strength is bridging the gap between business strategy and technical execution. We support cross-functional teams, and our consultants ensure every technical decision made by data scientists and IT is directly linked to a commercial goal – preventing the development of technically impressive but commercially irrelevant solutions.
Myth 4: “Our data is too poor for AI”
The reality: This is the most common excuse for inaction – but as we uncovered in the first article of this series, it's rarely the real problem: Effective AI requires meaningful data, not perfect data. The bigger blockers are almost always organizational – weak process integration and underdeveloped MLOps for the continuous fine-tuning of AI models.
What to do instead:
- Start where data and ROI impact intersect. Identify a high-value use case where the data is "good enough" to build initial AI solutions
- Build MLOps from day one. Treat models – from simple regressions to complex neural networks – like products that need to be monitored and retrained.
- Establish a single source of truth: Invest in pragmatic data governance so that definitions are consistent for your most critical data assets.
How Simon-Kucher Elevate helps: Our data strategists are experts at building pragmatic data foundations. We help you identify the highest-impact use cases that can run on your existing data, while simultaneously creating a roadmap to develop a data and governance model that will support more advanced deep learning applications in the near future.
Myth 5: “AI only pays off in the long run”
The reality: Treating AI as a single, multi-year moonshot is a recipe for delayed value and stakeholder fatigue. There are many low-hanging fruits you can harvest now, while building the capabilities that unlock the bigger prize.
While foundational changes take time, narrow and well-scoped AI-driven projects, such as churn reduction or creating demand forecasting, can deliver a positive ROI within months, not years. The key is to avoid waiting for 100% automation; partial automation often delivers the majority of the value.
What to do instead:
- Treat AI like a portfolio: Balance long-term capability building with quick wins that prove the economics and fund the journey.
- Target bounded, high-signal processes. Focus on initiatives like lead scoring or promotions planning where data is accessible and the business objective is unambiguous.
- Design for partial automation. Automate the 20-40% of a process that is most repeatable to unlock value quickly while keeping experts in the loop.
How Simon-Kucher Elevate helps: We specialize in building an AI portfolio for our clients that balances short-term wins with long-term strategy. Our use case valuation process identifies low-hanging fruit for immediate ROI, which builds momentum and funds the more ambitious, transformative projects that create lasting competitive advantage.
Myth 6: “AI projects take forever”
The reality: Time-to-value is a choice. We see this happening repeatedly: The top-performing companies move AI projects from pilot to production in about 90 days, while slow movers take a year or more.
The difference isn't the AI technology – it's the operating model and a relentless focus on scope. Projects drag on due to vague goals, organizational friction, and a tendency to reinvent infrastructure for every pilot.
What to do instead:
- Think big, start small, and scale fast: Solve one concrete, high-value problem in weeks, not quarters.
- Reuse platforms and guardrails. Don't reinvent your entire infrastructure for every pilot; leverage what you already have to adopt AI as quickly as possible.
- Make a business owner the lead – with IT as an enabler, and design clear escalation paths for critical human decision making.
How Simon-Kucher Elevate helps: Our agile, step-by-step approach is designed to accelerate time-to-value. We help you define a clear scope for an initial pilot, leverage existing platforms to get started quickly, and establish a business-led governance model that ensures projects move from concept to real-world production without getting stuck.
The time to do it right is running away …
Time is flying by – and leading players are adopting AI technology. That’s the essential truth of the current tech revolution.
These players’ success with AI isn't about having the most sophisticated models; it’s about creating new value that the market cannot easily copy. Successful companies that build a durable competitive advantage are the ones using AI to improve their top-line power, while institutionalizing the capabilities that make that value repeatable. This requires a robust AI strategy and an operating model to support it.
At Simon-Kucher Elevate, our experts help you challenge the most common AI myths and engineer the conditions for your success – guided by our think big, start small, and scale fast mindset. We connect your AI implementation projects to concrete commercial outcomes – ensuring that artificial intelligence becomes a profit engine, not just another cost center.
Interested in learning more? In our final article of this series, we'll dive deeper into the role of AI agents and how they can shape the future of your UX. Or just reach out to our Simon-Kucher Elevate experts to discuss your specific AI and implementation challenges.
