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KEY INSIGHTS
Telcos and Industrials are by no means reluctant to use AI. However, they currently deploy the
technology primarily to optimize existing processes with a view to gaining COST SAVINGS AND
GREATER EFFICIENCY.
The potential of AI to be disruptive will unfold over the coming five to ten years, when it will
RADICALLY TRANSFORM BUSINESS MODELS IN TELECOMMUNICATIONS AND MANU-
FACTURING INDUSTRY. By this point, at the latest, enterprises that lag behind other players in
AI adoption or have failed to adapt their business models at all will suffer serious consequences.
Yet despite the need for action, AI initiatives can only deliver genuine value if they are aligned
with the CORE OF THE ENTERPRISE – they must fully embrace it and extend it. There is no
point just jumping on the bandwagon without a sense of direction.
The GREATEST AI MATURITY is found in business models and processes based on data that is
too extensive, complex or confidential to penetrate using human intelligence or conventional IT
methods. Examples are network optimization and customer service in telecommunications, the
entire field of predictive maintenance in manufacturing industry, and also processing of personnel
data in HR.
Enterprises have to do the heavy lifting today in order to MAKE THE RIGHT AI INVESTMENT
DECISIONS for their business later. As in digital transformation, startups can be strong partners.
However, with AI technology being so complex, the upfront investment is often considerable. A clear
investment strategy is imperative to minimize risk. It is also feasible to source the expertise from
outside the company – a telco, for example, could work with an IT company as one of its suppliers,
and obtain AI as a service.
The success of AI projects hinges on the QUALITY OF THE DATA – and in many enterprises this is
surprisingly poor. AI is set to become a key business driver in the future, so it is imperative to overhaul
legacy structures right now, before it is too late, and create a robust future basis for capturing
high-quality, comprehensive customer data, process data and machine data.