But once you have that agreement from your stakeholders, how do you decide on a project that justifies significant investment?
A quick look online or at the agenda of any upcoming business event makes it clear that big data and AI is now a very trendy topic.
We see many impressive use cases across these mediums, but often these are created in isolation, even by different teams and organisations.
They can be strong examples of the potential that these technologies combined (data, cloud, analytics, AI etc.) can offer, but this demonstration is different to an embedded process that drives long term results across an organisation.
In my experience, for big data and AI to be a success, to have real business impact and adoption, the crucial factors to consider at every stage are scalability and automation.
The beginning of the journey
In order to demonstrate a significant business outcome, it’s important to differentiate between bespoke and scalable projects.
Of course, any use case is a great step forward for an early adopter, but we should be clear that it is only the beginning of the journey and cannot alone drive long term value.
In some industries, such as in the consumer and retail space, AI and predictive analytics are delivering results already – yet there is much more than could be achieved.
Chatbots are one such example of this; a tool which has been widely adopted and is proving successful.
But from a more holistic, scalable perspective, there are many other touch points on the retail journey which could yield additional impact.
Delivering value at scale
This might mean live customer recommendations made with AI or personalised content marketing delivered automatically at each stage of that customer journey.
And from a broader network perspective, any type of predictive support which identifies the need for enhanced resources on a given day – a seasonal sale, for example – has tremendous value.
Add to this the vast potential of anonymised data for insight; from credit card usage to predict GDP and inflation to the economic potential of an area, then it is quite conceivable to go from delivering tens use cases a year to multiple hundreds or thousands.
I even recall many years ago advising top global organisations that instead of designing 15-20 predictive models as part of their next best action strategies, they should be looking to implement an end-to-end analytics factory capable of building hundreds of models in weeks.
Four steps to maximise the potential of data
Overall, conventional understanding of AI has been around for quite some time now.
This is especially true for use cases with a clear objective, such as reducing customer churn in long tail organisations like Netflix, Amazon or traditional telcos, who target a large number of niche markets in a highly competitive sector.
Now, as digital adoption grows and data with it, more and more organisations can maximise their customer interactions to better understand their different customer journeys and monetise this momentum.
To do this, they will need:
A clear business opportunity and an understanding of its potential value.
Good data knowledge, including data governance, metadata, dictionaries and processes to ensure there is good quality data ready to be used.
A cloud platform ready to build and process large amounts of data at scale. On prem could always be an option, but comes with different pros and cons.
The skills required to apply AI effectively. Today it is difficult to find experts in this field - Insourcing this talent is a must-do strategy.
Agreement that AI adoption is for everyone. This means that the outcomes generated must be integrated into all digital channels to maximise results in a seamless and consistent way.
Looking within Vodafone as an example, using this methodology our teams have been able to ensure that a data monetization service in Portugal can be expanded to Spain, and into Italy, by following the same methodological and solution approach.
The keys to success
To realise the true value of AI many teams need to work together and align on objectives, meaning that internal transparency and strong collaboration across departments is essential. As well this, the right level of in-house and external support may be required.
However, by approaching the topic with scalability and automation in mind as key factors, businesses can ensure that any investment is best spent in driving change for more than a case study or demonstration.
With this approach, we can expect to see big changes in the effectiveness and value realisation of AI and data analytics.
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