Artificial Intelligence in the Traditional Business

The real AI revolution will not happen in the companies that develop it, but in those that apply it. From our Certus division, we have spent two years evaluating how AI can create value in middle-market companies that are not, and do not aspire to be, technology companies.

The myth of complexity has been debunked. Cloud services, no-code/low-code tools, and large language models can be integrated into business processes with investment accessible to any company with revenue above five million euros.

Practical applications: demand prediction (one portfolio company reduced forecasting errors by 35% and inventory costs by 12% with under 50,000 euros investment), predictive maintenance (analysing sensor data in real time to predict failures before they occur), administrative process automation (invoice processing, bank reconciliation, standard client queries), price optimisation (dynamic pricing based on demand elasticity, competitor prices, and marginal costs), and customer analysis (segmentation, churn prediction, cross-selling).

How to start: identify the problem, not the technology; evaluate available data quality; start small with a pilot project; measure, iterate, scale; and build internal capability over time.

Common mistakes: starting with technology rather than problems, expecting immediate results (realistic timeline is 6-12 months), ignoring change resistance, and underestimating data quality requirements.

A traditional company with good business fundamentals, rich historical data, and processes susceptible to AI optimisation is an extraordinary investment opportunity. The value we can create through intelligent technology implementation in traditional companies is enormous and largely unexplored in the Spanish middle-market.

AI will not replace the business owner or the manager. It will amplify their capabilities, improve their decisions, and free their time for what truly matters: strategy, relationships, and vision.

The Technology Transformation Opportunity

The intersection of traditional business and technology represents one of the most compelling investment opportunities in the Spanish middle-market. While the narrative around technology investment is dominated by venture capital and startups, the reality is that the greatest value creation potential lies in applying technology to established businesses with proven economics.

The typical middle-market company in Spain has been operating for two or three decades. It has a loyal client base, experienced employees, and established market positions. What it often lacks is the technological infrastructure to compete effectively in the coming decade. This gap between established business fundamentals and technology capability is precisely where we see the opportunity.

Our approach is pragmatic rather than ideological. We do not pursue technology for its own sake. Every technology investment must demonstrate a clear return — reduced costs, improved margins, better decision-making, or enhanced competitive positioning. The projects that generate the most value are typically not the most technologically sophisticated but the most operationally relevant: replacing manual processes with automated ones, providing managers with real-time data instead of month-old reports, or enabling pricing decisions based on market analytics rather than intuition.

Implementation Realities

Implementing technology in traditional businesses requires patience and cultural sensitivity. The resistance to change is real — employees who have done their jobs successfully for years are understandably sceptical of systems that promise to do it differently. The key is involving people in the process, demonstrating quick wins that build confidence, and being honest about what technology can and cannot do.

Data quality remains the single biggest barrier. Companies that have operated with rudimentary information systems for decades cannot implement sophisticated analytics overnight. The preparatory work of cleaning, structuring, and standardising data is unglamorous but essential. We have learned to allocate adequate time and resources to this phase, even when it means delaying the implementation of more visible solutions.

The talent challenge is equally real. Finding professionals who understand both technology and business — who can translate between the language of algorithms and the language of the warehouse floor — is extraordinarily difficult. Building this capability internally, through targeted training and strategic hires, is a long-term investment that pays extraordinary dividends.