AI Transformation

AI transformation is the deliberate use of artificial intelligence to raise a company's value after an acquisition. In practice it means automating repetitive work in quoting, procurement, scheduling, accounting and customer service, and putting decisions on a better data footing. Economic value arises when additional revenue or cost savings exceed implementation and running costs. Investors therefore need a concrete use case with a measurable benefit. Higher productivity raises company value sustainably only if quality, customer retention and execution capability are maintained.

It does, however, require a reliable data foundation and a minimum level of process maturity, which is why reviewing the systems landscape, data quality and process documentation belongs in due diligence. Realistic assessment of the effort matters equally, because implementation, training and process redesign absorb time and management capacity. In succession deals it meets businesses with often low levels of digitisation, which makes the potential effect larger but the execution harder.

So far the economically viable applications are mainly clearly bounded use cases with measurable effect, such as automating quotation, analysing technical documentation, pre-qualifying enquiries and supporting customer service. They require an orderly data base, which in many mid-sized businesses has yet to be created. Data protection, rights to use data and human oversight matter alongside the reliability of outputs. A pilot should therefore measure not just time savings, but also error rates and the review effort required. For valuation it also has to be established whether the tools deployed rest on proprietary data and thereby create an advantage, or whether they are generally available to every competitor.