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5. Conclusion

The author defined four sets of objectives to justify the reserch. The first

research objective was to conduct a litreture review to assess the extent to

which BD, AI and analytics is applied globally across the wine life cycle. The
literature review identified a range of BD, AI and analytics related applications

applied across the wine life cycle by global wine organisations. The literature

review indicated that there is a myriad of new BD, AI and analytics

applications develop to be applied within the wine life cycle stages. It
highlighted the application and the respective contribution of BD, AI and

analytics across the multi-billion-dollar wine industry. The second objective

was to consolidate the literature review findings by themes, serving as the
basis for composing a set of interview questions across the UK and Italy wine

organisations. The third objective was to conduct qualitative research

methods with structured in-depth interviews to ensure that the correct

research method was being applied. The author aimed to identify the level of

BD, AI and analytics applications for the UK and Italy wine organisations to
identify the supporting reasons for applications and reasons for not applying

such application tools. The fourth objective was to collate and stratify the

findings and compare the BD, AI and analytics application against the
literature review findings. The main reason for conducting this research is to

identify the gap between the literature findings against the actual application

of BD, AI and analytics related tools across the wine life cycle stages. The

gap analysis was stratified by themes within the wine life cycle stages,
categorised by BD, AI and Analytics. This presents the opportunity to identify

reasons for further research. The research finding indicate that wine

organisations have limited applications of BD, AI and analytics, when

compared to the literature findings due to various factors such as lack of
awareness, budgets, required skilled resources. This result shows the current

maturity level of the wine industry, highlighting the requirement for the wine

industry to apply BD, AI and analytics across the wine life cycle and streamline
their processes and sustain growth. It is apparent that there is no defined

scalable operating model that encapsulates the BD, AI and analytics across

the wine life cycle organisations for small and large wine organisations.

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