Why AI Fails to Deliver in Many Enterprises – And What Matters Now
A recent Gartner forecast puts it into perspective: By 2027, over 40% of today’s agentic AI initiatives will be discontinued – mainly due to rising costs, unclear ROI, and poor strategic alignment.
It’s not the technology that fails. It's the lack of deliberate planning and systematic integration.
Many projects launch prematurely with vague objectives, disconnected from actual business priorities. These initiatives didn't collapse because AI doesn't work. They collapsed because no one defined what working actually means.
The hype moved faster than the strategy. Now it’s time to reverse that.
While individual teams are busy prototyping, what’s often missing is a strategic framework that turns isolated experiments into real progress. What remains is a paradox: The technology exists, but the impact doesn’t.
This article reveals five concrete use cases from manufacturing, retail, and knowledge work - plus the three decisive principles that determine success or failure in AI implementation.
Where are the biggest roadblocks on the path to meaningful AI?
Recent studies point to some clear answers:
Scaling AI Initiatives According to Boston Consulting Group (BCG, 2024), 74% of companies struggle to realize and scale the value of their AI initiatives. Only 26% have developed the capabilities needed to move beyond pilot projects and generate real business value.
Human and Process-Related Factors The same BCG research shows that around 70% of challenges in AI implementation stem from people and process issues. Only 20% are technological, and just 10% are algorithmic.
Lack of Clear Governance Structures A McKinsey survey (2024) highlights that many organizations face difficulties establishing clear responsibilities and governance for AI projects – hindering effective execution and scalability.
Integration into Existing Systems Seamlessly integrating AI solutions into current IT infrastructures and business processes remains a major hurdle – especially for large enterprises with complex legacy systems.
Risk Management and Compliance Identifying and managing risks related to AI, such as ethical concerns and regulatory requirements, is crucial for the long-term success of AI initiatives.
Many enterprises work with core platforms like S/4HANA, Salesforce, or Dynamics. For AI to drive value in these environments, it needs to be connected to modern services – such as SAP BTP, Vertex AI, or Azure OpenAI. This requires a partner that combines technology, processes, and people: cloud-native, human-centric, and globally scalable.
These challenges make one thing clear: The success of AI does not depend solely on technology. It is fundamentally shaped by organizational, process-related, and cultural readiness.
From Pilots to Business Value: Where AI Is Already Creating Impact
Many organizations initiate their AI journey with small pilot projects. These provide valuable initial insights but often remain siloed efforts, where tangible value proves elusive. This common pattern underscores the urgent need to proactively explore AI solutions beyond experimentation.
The real challenge lies in transitioning isolated successes into scalable, everyday operations. To break through this barrier, companies should focus on early implementations that may require low technical complexity but offer high value through automation and improved output quality.
Ultimately, measurable business impact is achieved when AI is strategically aligned with clearly defined objectives.
Below are real-world approaches showing how companies are already using AI successfully in industries such as manufacturing, automotive, and global supply chains and what they’ve learned along the way.
1. AI-Driven Quality and Efficiency in Manufacturing & Automotive
Context: In manufacturing and automotive environments, seconds often determine productivity and quality.
Solution: AI-powered smart maintenance models analyze machine data in real time to predict equipment failures. Predictive quality systems systematically detect deviations in the production process. In the automotive sector, aftermarket intelligence systems help identify maintenance needs early and enable better planning of service capacities.
Impact:
Less downtime thanks to predictive maintenance
Higher product quality through data-based anomaly detection
Improved service planning via automated demand forecasting
AI models integrated directly into MES or ERP systems
Production decisions become predictive rather than reactive with measurable impact on efficiency and quality
2. AI-Powered Support in Physical Retail
Context: On the sales floor, every moment counts. In physical retail environments, generative AI is increasingly being used to support staff directly at the point of sale.
Solution: Store employees receive fast, context-specific answers via generative AI, such as how to use technical products or which accessories to recommend. The responses are consistent, reliable, and immediately available.
In some cases, AI has also been integrated into omnichannel systems, giving store staff access to previous customer purchases. This creates a deeper understanding of individual needs and preferences – enabling highly personalized interactions.
Impact:
Increased conversion rates through more personalized interactions
Greater average order value (AOV) and product bundling via cross-selling
Positive impact on key business KPIs such as conversion rate, average order value, and overall profitability
Faster onboarding of new team members
Fewer escalations to central support
Higher customer satisfaction through confident, informed service
Advisory excellence becomes scalable, regardless of team size or individual performance on a given day.
3. Smart Shopping Assistance and Product Recommendations
Context: How can the in-store shopping experience be enhanced with digital tools and intelligent guidance?
Solution: In brick-and-mortar retail, AI-powered devices such as smart shopping carts are being piloted to improve both convenience and conversion. These carts provide real-time product suggestions, support touchscreen interaction, and help customers navigate the store efficiently. Weight sensors and AI-based anomaly detection can also support theft prevention.
Impact:
Reduced wait times
Personalized in-store journeys
Higher engagement with promotional content
Increased average order value through tailored recommendations
Reduced return rates due to better-informed purchasing decisions
AI turns brick-and-mortar shopping into a data-driven touchpoint – with direct impact on revenue and brand loyalty.
4. Scalable Knowledge Work with Generative AI
Context: In many enterprises, internal knowledge remains underutilized, scattered across systems, documents, and individual employees.
Solution: Tools like NotebookLM make it possible to structure, contextualize, and dynamically update this knowledge – e.g., when creating training content, decision support materials, or product documentation. Frameworks such as Retrieval-Augmented Generation (RAG) are also used to ground the AI model in the company’s specific datasets. This enables accurate and context-aware responses based on actual organizational knowledge.
Impact:
Faster and more consistent content creation
Stronger decision-making based on accessible, validated knowledge
Reduced factual errors and hallucinations due to AI grounded in company-specific data
More time for strategic work in IT and beyond
Tacit knowledge becomes actionable – instead of vanishing into silos, it becomes directly usable across roles within the organization.
5. Participation as a Key to Adoption
Context: Many technology rollouts fail due to a lack of employee engagement.
Solution: An internationally operating B2C company from the digital platform space approached its generative AI rollout differently. Rather than a traditional top-down model, the company encouraged active participation. Employees were invited to develop their own AI use cases – supported by internal challenges, competitions, and an early adopter program. The best ideas were implemented directly.
Impact:
Higher identification with the initiative
Faster scaling through practical, real-world use cases
Increased quality of work through more ownership and contextual relevance
Automation of repetitive tasks, enabling employees to focus on higher-value work
A stronger culture of innovation across teams
This turns participation into a true scaling lever and technology adoption into a lasting culture of transformation.
Whether it's personalization, efficiency, or automation – the strategic intent defines the outcome, not the tool itself. This framework helps map the impact.
What Matters Now: Three Principles for Real Progress with AI
These real-world applications show where AI creates value today – provided it's embedded in strategy, not hype.
Focus over variety: Don't attempt every use case at once, start where the leverage is highest.
Enable participation: AI doesn't succeed through tool deployment alone, but through everyday co-creation.
From pilot to strategy: Those who want to launch successfully need clarity around goals, data, and scalability.
The critical questions for your context:
For CIOs & CTOs: How do you design an architecture that remains scalable across systems, countries, and requirements?
For Line-of-Business Leaders: Which processes can be reshaped by AI to drive measurable productivity gains?
For CEOs: How can AI visibly support strategic goals – beyond the buzzwords?
But also ask yourself: Which business problems are significant enough that solving them would drive real progress? What foundations are already in place and where are the gaps?
Companies that ask themselves these questions seriously are laying the foundation for lasting transformation. They distance themselves from hype and focus on substance. AI doesn't need all the answers. It needs the right questions.
That’s exactly where the AI Orientation Workshop begins.
Together, we identify pain points, prioritize real use cases, and build a roadmap that turns strategic intent into scalable outcomes.