KYOCERA Document Solutions
Artificial intelligence (AI) is quickly becoming a central part of digital transformation strategies. Across Europe, businesses are exploring how AI can automate tasks, analyse information faster and improve decision-making.
However, the reality behind the headlines is more complicated. While AI adoption is increasing, many organisations are still struggling to implement it effectively.
According to the European Commission’s 2024 report on AI adoption, only 13.5% of EU enterprises currently use AI technologies.
The gap between AI ambition and real-world implementation highlights an important point: adopting AI requires the right data, processes, infrastructure and organisational readiness.
For businesses considering AI adoption, the first step is assessing whether your organisation is ready.
Why AI readiness matters for business
Many organisations approach AI adoption by focusing on tools or software platforms. However, successful AI transformation depends on much more than technology.
AI systems rely on accurate data, clearly defined workflows and employees who understand how to use them effectively. Without these foundations, even the most advanced tools will struggle to deliver value.
Research from McKinsey’s State of AI report highlights this challenge. While many companies experiment with AI, only a small proportion successfully scale AI solutions across their organisation.
For businesses, assessing AI readiness before investing in new technologies can prevent costly mistakes and ensure digital transformation initiatives deliver meaningful results
Five dimensions of AI readiness
Frameworks exist to help organisations evaluate whether they have the foundations required for successful AI adoption.
In practice, many organisations begin strengthening these foundations begins with improving workflows through advanced document management solutions and enterprise printing solutions.
1. Information management and data readiness
AI systems depend on high-quality, accessible data.
However, many businesses struggle with fragmented information stored across spreadsheets, email inboxes and disconnected systems. When data is inconsistent or poorly organised, AI tools cannot produce reliable insights.
Before implementing AI solutions, organisations should ensure that information is digitised, searchable and stored in centralised systems.
Modern document management solutions can help organisations organise information more effectively and ensure data is available for analysis.
What this looks like in practice:
- Business information is digitised and searchable.
- Documents are stored in structured systems rather than scattered across multiple locations.
- Employees can quickly locate the information they need to make decisions.
2. Workflow visibility and process optimisation
AI is most effective when applied to clearly defined processes.
If workflows are poorly documented or vary significantly between teams, automation becomes difficult to implement.
For many organisations, documenting processes can reveal inefficiencies or unnecessary steps that slow down operations.
Mapping existing workflows allows organisations to identify where automation, document capture or intelligent routing could improve efficiency.
What this looks like in practice:
- Core workflows are documented and clearly understood across teams.
- Manual steps are identified and evaluated for automation opportunities.
- Digital workflow tools streamline how documents move through the organisation.
3. Digital infrastructure and connected business systems
AI systems rely on modern digital infrastructure.
Legacy systems that cannot integrate with other applications often create barriers to automation. Cloud platforms and integrated systems allow data to move more easily between tools, making it easier to introduce AI capabilities.
The OECD Digital Economy Outlook highlights that digital infrastructure and cloud adoption are key enablers of AI transformation among growing organisations.
Security is also essential. AI solutions often process sensitive business information, meaning organisations must implement strong access controls and monitoring systems.
Enterprise printing solutions, cloud platforms and integrated document systems help ensure business information flows securely across devices, users and locations.
What this looks like in practice:
- Cloud-based systems allow information to move easily across tools and teams.
- Security controls protect sensitive business data.
- Business systems integrate with one another rather than operating in isolation.
The purpose of an AI readiness assessment is not to achieve perfect scores but to identify where improvements are needed."
4. Workforce capability and digital skills
Employees do not need to be AI specialists, but they must be able to experiment with new tools, interpret insights and adapt workflows accordingly.
Research from the World Economic Forum’s Future of Jobs Report shows that digital and AI-related skills are among the fastest-growing capability requirements for organisations worldwide.
Training and experimentation play an important role in helping teams develop confidence with new technologies and preparing workforces for AI transformation.
What this looks like in practice:
- Employees receive training on digital tools and emerging technologies.
- Teams are encouraged to experiment with new solutions.
- Staff understand how technology can support their daily work.
5. Organisational culture and openness to innovation
Organisational culture often determines whether AI initiatives succeed or fail.
Introducing AI may change workflows, require new skills or challenge established ways of working. Organisations that resist change may struggle to implement new technologies effectively.
Leadership plays a critical role in communicating the benefits of AI and ensuring employees understand how technology will support their work rather than replace it.
Companies that encourage experimentation and continuous improvement are typically better positioned to succeed with AI transformation.
What this looks like in practice:
- Leaders actively support innovation and digital transformation.
- Employees feel comfortable testing new tools and workflows.
- Continuous improvement is embedded in everyday business operations.
From Intent to Action: the leaders’ guide to building AI-powered workplaces
A new Economist Impact report sponsored by Kyocera reveals a growing gap between AI ambition and workforce readiness. The research highlights skills gaps, weak governance and limited workforce development as key barriers to turning AI strategy into sustainable business value.
Turning readiness into action
Few businesses will score highly across all five readiness dimensions. That is normal.
The purpose of an AI readiness assessment is not to achieve perfect scores but to identify where improvements are needed.
Many organisations begin by focusing on foundational improvements such as:
- Digitising paper-based information.
- Improving document workflows.
- Centralising data management.
- Modernising digital infrastructure.
Implementing document management solutions for business and modern enterprise printing solutions can play a key role in this process by digitising information flows, improving accessibility and strengthening security.
Preparing your business for the AI transformation
AI will increasingly shape how organisations manage information, automate routine tasks and support decision-making.
However, successful AI transformation depends on more than selecting the right technology. Businesses must ensure they have the data, systems and organisational capabilities needed to support AI initiatives.
By strengthening information management, workflows, technology infrastructure, workforce capability and organisational culture, businesses can build a clear roadmap for AI adoption and long-term digital growth.
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