BJA | TDM

Some context:

Hardly an early adopter for any tech, innovation. Best case I have always been a mid to late adopter with pre-identified relevant use case(s).

Urgency and reality:

That being said, beyond the frenzy and fight for market leader position within the tech ecosystem – the age of AI is here to stay and next layer enhancements are already embedded into household productivity, planning and workflow tools or platforms we use today. 

At the back end, it’s already a big part of the speed, optimization and value-adding front end platforms, products, services that we engage with.

Waiting longer to tap value could be detrimental. To begin – I have chosen to dip my toes and already generating value from curating multiple perspectives and translation to everyday business narratives, plans and subject areas of interest per time. 

For a connector of dots and maximizer, AI tools have helped me to quickly sample overriding available perspectives for me to apply my personal logic and intuition to the context at hand.

1. Productivity remains an early quick win for me – Use Grok a lot of late – with caution as always but playing with various prompts till I get close to what I want has been fun.
2. Firm believer that human Intuition and people relationship would always be required to get the optimum value from AI. I sleep rest easy that my value is not at stake, we will all have a role to play as long as we stay curious.
3. Trying to understand the risks, opportunities and what is already safe and available in existing everyday platforms. Then leveraging for speed, optimization and value while staying close to the realities and unfolding use cases. 

Joined a virtual round table a few weeks with Global Senior Commercial and back office executives across all works of life from life sciences to energy and logistics: 

Here are the unfolding use cases for AI, realities and clustering perspectives:

1. Quoting optimisation – Emotional approach vs reality and connecting solutions to core customer requirements. Factoring the personal reality and eliminating human bias. (Large energy and infrastructure projects)

2. Efficiency – Matching customer needs, standardisation – taking out human bias to deliver value. Individualization and standardising the best way to approach value maximisation. (Sales efficiency)

3. Sales competency cycles and linking to training and next stages. (Capabilities development)

4. Solution best fit for customers – Turnaround time and matching allocation to the right places. (Solution design), Forecasting – Supply chain and demand planning.

5. Summarising notes, workflow optimization – speed to market. (Workflow optimisation).

Food for thought from the session and some reality check. 

Not dissimilar from some of the key voices in the AI space including Sam Altman and what the team at Open AI caveat their releases and journey with. 

1. Jury still out. More people coming to terms with it but the how, where and impact – People still figuring it out, Co-Pilot, Chat GPT, Einstein – SFDC. Still figuring it out, and is value being derived? Still at best rudimentary optimization of flows for the frontline. More advanced backend use case. 

2. Important to check for accuracy in specific industries. Before dipping toes for enterprise value – Can you get AI into training and control any risks?

3. Use case governance: Clear guidance for what we can use and not use to keep controls/optimize training and end goal outcome becoming crucial.

4. Adoption: Broaden the use case, identify early adopters and mechanism to bring people to speed.

5. How do we know it’s successful – not there yet, to start measuring. More focus on selective use case and linked to specific goals – existing KPI. Should be an enabler of already existing business objective and priorities.