AI in business: Beyond the hype and the future reality

Artificial intelligence (AI) is no longer a vision of the future but a reality reshaping business today. Yet, the report “The Reality and Future of AI in Business” shows that the gap between hype and actual impact remains significant. Despite billions invested in generative AI (GenAI), 95% of companies have yet to see measurable returns. Adoption is widespread, but genuine transformation remains elusive.

The GenAI divide: Hype vs. reality

The report introduces the concept of the GenAI Divide, highlighting AI in business challenges: a structural gap between AI’s potential and its actual value creation.

Many organisations experiment with tools such as ChatGPT or Microsoft Copilot, but only a few succeed in scaling pilot projects into integrated solutions. Most AI initiatives end up as isolated applications with no strategic anchoring.

Notably, the most meaningful progress often doesn’t come from formal programs but from employees’ practical use. This “shadow AI economy”, the informal use of AI tools outside of IT, often serves as the most realistic bridge between hype and impact. Employees use AI to write texts, summarise meetings, or analyse data, often without official approval.

Practical applications: Where AI delivers value

While widespread transformation is still pending, there are already targeted applications where AI demonstrates clear value. In support processes such as customer service, marketing, administration, and manufacturing software development, AI acts as both an accelerator and cost saver.

Customer service: AI assists with answering first-line questions, summarising conversations, and routing calls, leading to faster response times and relief for support teams.

Marketing & sales: AI personalises communication at scale, analyses customer data, and speeds up lead qualification. Top companies report up to 40% faster follow-up and 10% increase in customer retention.

Document processing: AI automates the classification and summarisation of contracts, invoices, and emails, resulting in substantial outsourcing savings of up to $10 million per year. Strong collaboration frameworks, like design system principles, help ensure AI integrates smoothly into workflows.

Software development: AI assistants generate routine code, detect errors, and write documentation, delivering 5–30% time savings for IT teams.

A key observation is that AI supports and accelerates human work rather than replacing it outright. Efficiency gains are often used to improve service or reduce external costs, not for cutting staff.

Challenges: Reliability, ethics, and trust

The promise of AI is vast, but so are its vulnerabilities. Businesses face not only technical limitations but also issues of reliability, ethics, and human trust.

Hallucinations: AI models can provide incorrect answers with such conviction that they undermine trust and necessitate human oversight.

Bias: AI learns from historical data, which may contain intrinsic biases, potentially leading to discrimination, especially in HR or lending.

Security & control: AI should reflect human intent, not just algorithmic optimization. Without clear guidelines, AI systems can make undesirable decisions.

The solution lies in transparency, explainability, and human governance. AI should be treated like a junior colleague: useful, but always supervised by an experienced professional.

Technological progress: From silos to learning agents

Future of AI in business: AI is developing at lightning speed. Once limited to isolated algorithms, it now encompasses integrated systems, multimodal models, and autonomous agents that learn, remember, and act independently.

Foundation models & LLMs: Large language models, such as GPT-4, can perform a wide range of tasks and are increasingly utilised as generic business platforms.

Multimodal AI: AI combining text, image, and audio is opening new opportunities in customer service, healthcare, and content creation.

Agentic AI: Virtual assistants autonomously pursuing goals within boundaries, such as following up on quotes or managing projects.

Efficiency & hardware: Smarter algorithms and more powerful chips make AI more accessible and affordable, even for smaller businesses.

This technological wave offers enormous opportunities but requires a proactive strategy. Companies must invest in modular, future-oriented approaches and organise internal discussions to translate new developments into concrete applications.

Strategy: Pathway to responsible success

Success with AI does not come automatically. It requires a thoughtful approach that aligns technology, people, and governance. The report formulates five strategic pillars:

Start small: Choose feasible use cases with clear KPIs. Avoid large-scale, unfocused projects.

Invest in people: Ensure access to high-quality datasets and build AI skills through training and cultural change.

Safeguard ethics and transparency:  Establish internal guidelines and conduct ethical checks on every application.

Collaborate with the right partners: External suppliers and industry peers can provide valuable insights and solutions.

Keep people central:  AI should empower people, not replace them. Redesign roles so that people and machines complement each other.

Conclusion

AI in business is at a tipping point. The era of experimentation is over; now comes the time to push forward and scale up. Companies that invest coherently in technology, people, and governance will truly reap the benefits of AI, including processing information faster, serving customers more personally, reducing costs, and gaining new insights from data.

At the same time, the challenges must not be overlooked. Reliability, transparency, and safety are not luxuries, but prerequisites for sustainable success. If designed correctly, AI will serve us, not replace us.

The future of AI in business is both bright and challenging. Those willing to learn from data, mistakes, and people will discover that AI is not a mysterious oracle, but a powerful ally in improving how we work and do business.

The message for management and boards is clear: embrace AI, step by step, with clear frameworks and a human-centred strategy. Trust is the new capital in an AI-driven economy. Organisations that build a reputation as reliable, honest AI users now will attract customers and talent in uncertain times.

The future of AI is bright and challenging. Those willing to learn from data, mistakes, and people will discover that AI is not a mysterious oracle, but a powerful ally in improving how we work and do business.