Tag Archives: Transformation

AI, Digital Transformation, KPI

Transformation AI Blues

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Despite the wave of use cases and significant investment in Gen AI, current success rates remain low.

Back in 2021 Gartner reported that 85% of AI projects fail to deliver, and only 53% of projects made it from prototype to production. A more recent study by Infosys Study Dec 2023 indicates that only 6% of European Gen AI use cases have created value.

The hype around AI has led to a explosion of exploration activities across many organizations. In one example, in the rush to join the bandwagon, a bank bought tens of thousands of GitHub Copilot licenses without a clear sense of how to apply the technology. As a result most of those projects will fail to generate competitive advantage.

7 out of 10 digital transformation initiatives are considered failures using value measures such as user adoption and overall ROI profitability metrics. There are many parallels between AI and digital transformation project causes of failure. Spending big money on AI does not guarantee transformational results — its implementation execution that realizes the value.

Data

Part of the failure can be attributed to bad data: too little, poor quality, not in the right place, in the wrong format, missing key data points and often unintelligible across disparate systems. It’s insufficiency limits the the transformation and without a plan to transform the data in a way that creates value, AI initiatives struggle to tame the beast.

AI Magazine March 2022

Often organizations complete a one-off data cleanse to initiate the digital transformation, however this is short-lived given the unaddressed constant generation of bad data. A deliberate focus is required to build better quality data from the start, linked across organizational systems, to create a ‘single source of truth’. AI use cases operating without these data build considerations never achieve ‘lift-off’ .

Oliver Wyman 400 C-level executives in Europe and the Americas survey suggests that Data privacy (25%) and security concerns (22%) are the top factors preventing AI adoption.

Success Strategies

Accordingly to the Gartner AI Hype Cycle, we have now reached the peak of inflated expectations, and during 2024 will be moving into the trough of disillusionment. The big question is how to begin creating measurable value.

Gartner Predictions

By 2025, growth in 90% of enterprise deployments of GenAI will slow as costs exceed value.

World Economic Forum Agenda May 2024 Implementation Recommendations

  1. Pick the right composition of leaders for the AI transformation
  2. Embrace complexity and novel approaches
  3. Incorporate design principles in human-to- machine interactions.
  4. Bring workers along on the AI transformation with upskilling
  5. Score quick wins via efficiency gains.

My thoughts echo these recommendations. Transformational AI is a cross-functional effort, however many organizational departments operate in silo’s and there is a need to appoint senior business managers with sufficient granular view across the organization to help articulate and determine the journey steps. Organizations tend to delegate to IT as a default which is a mistake given the business success measures.

As with any digital transformation, human-centric design principles are essential to ensure that the AI output is structured and formatted in a way that is clearly understood by the human, for human decision validation.

By 2028, more than 50% of enterprises that have built large AI models from scratch will abandon their efforts due to costs, complexity and technical debt in their deployments.


If you don’t measure it, you don’t get it


KPI’s are critical in objectively measuring value and assessing AI success:

# Business Goal alignment

# Delivering data-driven insights

# User adoption

# Performance

# ROI

Paradigm Shift

GenAI is not just a tool; it’s a paradigm shift.

In conclusion, AI deployment complexities mirror the same challenges associated with organizational digital transformation initiatives. Transformational AI disillusionment will start with the realization that a strong business case, energy and resilience are required to stay the journey. There are no short cuts.

Should we be more cautious or is the risk worth the reward?

AI

AI is a Journey, not a Destination

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My journey with AI is progressing and as a great believer in active learning, I have been getting ‘hands on’ and ‘into the weeds’. I hope sharing and reflecting lessons learnt spreads the knowledge (and addresses the execution challenges that the technology pundits gloss over).

AI is not the silver bullet; the reality requires significant design purpose involving ‘sweat and toil’ to realize value. Rocking up to pitch AI without a solid use case or business proposition is like throwing mud on the wall and hoping some of it sticks.

The conclusion: there are no short cuts.

The Learning Journey – Execution Lessons

1. Quality of the prompt and question being asked is critical to elicit a complete and relevant response. A poor question leads to a poor and less relevant and ambiguous response. There is a excellent post from Anakin.ai blog [Mastering ChatGPT: How to Ask the Best Questions] which frames how to get the best out of ChatGPT:

  • Be Clear and Specific
  • Contextualize the Question
  • Experiment with different Question variations
  • Utilize prompts and examples

2. Format of the AI response is critical to structure the output insight. The construct of how the response is presented/ visualized is fundamental to comprehend the application results. This subject area seems to be underestimated in its difficulty and, arguably, its the advanced output revelation that you are seeking to gain by using AI.

For example, we are familiar with Netflix suggesting recommendations but without context relevancy and ranking, we end up with just another data list. Similarly if no consideration is given to how the output is structured, it becomes another mass of data that we struggle to make sense of.

3. Garbage In = Garbage Out. AI requires lots of good quality data. Where the volume of input data is limited, and its relevancy is questionable, the quality of the AI output will be similarly limited.

Furthermore, it is important to understand how AI derived the output; why did AI respond with that data or recommendation. Unfortunately this means diving into the detail. As part of the development and testing phase, this aspect is crucial to ensure confidence in a repeatable and reliable output. Configuring AI solutions will require several iterations as you experiment to determine an optimal context construct.

4. Engage Business User Subject Matter Experts. There is a direct correlation between the quality and validity of the output and the team engaged to support the AI development.

Despite what the data analyst or developer believe looks good, the accuracy and trust in the output can only be ensured if a strong team of experienced business SME’s are involved in the configuration and development process. The ‘devil is in the detail’ and the team allocated to support your AI initiative(s) need be at the top of their game (for the reasons listed above).

5. Business User Buy-In. As with any transformation; process, procedure, training and change management factors are key if AI is to be accepted and operated successfully. Open minds are required and there is a need to be receptive to an alternative way of working.

Courage in Procurement

I am very optimistic that AI can be implemented successfully to accelerate and automate a number of front and back office processes. My experience of the journey so far essentially mirrors a transformation project; it entails typical business transformation challenges, and requires a steady and resolute focus on capturing value!

What has been personally surprising is the level of granularity and detail that AI examines and extracts – this has exceeded my expectations and has forced me to re-think how we can use AI to raise the bar even higher!

What is your advice on AI execution? What is your use case and what works and what does not work. Please share the knowledge.
Change Leadership, Strategy

Procurement Pioneers

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Are you a Procurement person in the business, or a business person in Procurement?

For a few organizations, procurement is considered a ‘blocker’ preventing growth and success, and regarded as disconnected from business goals.

Procurement have a level of risk management responsibility, but this should not be applied with total disregard of the needs of the business. Our ability to build a collaborative relationship with the business requires procurement to act as business managers. We are all on the same team and have a common goal to ensure the business succeeds.

Procurement transformation is more than finding the right technology platform, it is changing the mindset and establishing a service centric approach with an attitude to succeed. Too often technology is applied to support a risk adverse, user unfriendly procurement transaction experience. This is a fundamental failure of procurement to understand its true value proposition and leverage the technology in the right way.

This does not mean we should be meek and afraid to challenge the business; our agenda should be to challenge and transform, thereby unlocking 10 x business contribution. Transform requires Procurement to understand the business challenges and pioneer a new approach. Set the vision. Technology enables humans. Become the business enabler! Lead change.

We are Bold, Optimistic, Human-Centric, Pioneering, Responsible.

We are Dreamers Who Do.

Our life is shaped by our relationships, and having recently started a new adventure, I want to thank all those that have enabled me and supported my latest journey.