Many analytics initiatives fail to realize the promised value. Learn here about key success factors & pragmatic lessons from hundreds of delivered projects.

A recent survey on LinkedIn asked for the key factor to realize lasting AI & Analytics transformation initiatives. The results were clear – Value!

To us this is not a surprise – however it’s clear, all dimensions have to have a well-balanced approach and been integrated in a roadmap to execute but value has to be at the heart of what we do.

Without focus on value you e.g.:

• Waste time on unimportant tasks
• Risk to not achieve overall goals
• Likely betray yourself on the outcome of your initiative
• Miss the opportunity to align the AI & Analytics initiative across business, operations and IT
• Lead to the situation that you’re not optimizing the results of your initiative.

Some practical thoughts along our learning on key success factors allowing AI & Analytics transformations to focus on value:

1. Value Realization has to be a core guiding principle throughout the AI & Analytics transformation. Hence, this deserves to be monitored and observed through the program. In large transformations, some even establish a “Value Realization Office” to track value throughout the transformation and not just at the end. Value should be treated “by design” and not “by accident” or subsidiary at the end of a project.
2. A value generating mindset – the involved teams and stakeholders have to always ask themselves “how is this and that generating value for our customers or employees?”. Furthermore, they should challenge key business, operations and IT stakeholders on must-haves / nice-to-haves.
3. A Value Realization framework has to be agreed that is setting the common rules on how value is tracked and operationalized, e.g. how do you deal with qualitative value, how do you attribute improved results to analytics initiatives and distinguish from market effects or changed conditions.
4. Stop the waste – what does not generate direct or indirect value has to be stopped – ruthless! This allows the team to focus on the key elements.
5. Start small and think big – in our experience it is important to deliver value not only at the end of a transformative program – it is important that “you walk the talk” and while defining your strategy and roadmap for an AI & Analytics roadmap already realize a few, selected use cases to show value – instead of only talking. This raises trust with the stakeholders and creates a momentum within the organization. So it’s much better to implement small use cases while you form a holistic, bigger strategy.
6. Train your people on Business Cases and Value Assessment – don’t assume everyone knows how to assess value – especially when it comes to understand KPIs and transform these in hard business metrices like “new revenue” or “increased profit”. Working with proxies and benchmarks and A/B testing, e.g. will be key to get trustworthy and tangible results you can use.
7. Apply transparency – celebrate what value you’ve achieved, but also share what you’ve not achieved. This raises trust in your organization and beyond and allows everyone to understand their value contribution. Everyone should be invited to share how a new AI or even a new dashboard has changed their decision and created value.
8. A final point we believe is central to grow the organizations continuous improvement to focus on value generation – a central learning log on what has been achieved, what has been failed to form best practices and experiences.

These are some key learnings from our side – there are more practical advice and ideas on how to better manage value in AI & Analytics initiative. We are happy to engage in a discussion.

What are your learning and experience on how to secure value from AI & Analytics initiatives? What did you succeed with / what did fail?