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AI and its continuous MVP status: How can this impact Consultancy Services and Clients?

Is AI hype?

It is widely stated that AI cannot substitute Humans in the following skills: Empathy, Creativity, Judgment, Technological Management, and Analytical skills. Critical and strategic thinking are complex for AI to replace, even if it can perform tasks that require logical reasoning and pattern recognition.

There are debates among those who are sceptical (or who they can become later adopters…?) if AI now is hype? From my point of view, there is hype around AI, and it has created a sort of hysteria with unrealistic expectations (me included). With so many overpromises. There could be a risk if we add worldwide concerns about the lack of development boundaries for it and a poor understanding of its real capabilities, it could push back this technology for decades[1]. However, it is impossible to wait and wish (by many) that AI will crash somehow. The question for consultancy is: what am I going to do with AI?

AI Will always be an MVP.

In the last two months of experimentation and learning with AI, I have understood that the following high-level phases identify an MVP ( Minimum Viable Product) cycle. Features introduction-users tests-ML data filling feedback- improvement features/remove issues-new apps. From Start-ups practice, an MVP can be of interest to customers and requires a minimum set of functionalities which will be improved and added. Those features can satisfy minimum requirements for customers. Two questions:

a. is the concept of an MVP also relevant to artificial intelligence? YES.

b. Will AI always be an MVP despite how AI creators and developers will create some maturity levels? YES.

But as end users (or tomorrow as experts) in our line of business, we need to acknowledge that AI products have unique requirements that must be met. Ability to collect and process high-quality data, incorporating human expertise to improve accuracy, and providing value (money) to customers since day 1.

MVP- Requirements[2]

Data: Data are fundamental while supplying them to ML (machine learning); training ML is another step. There is a phase when input data is required to start, and the second phase is to structure data flows from diverse sources in real-time.

Hybrid Models? AI products cannot be created using machine learning alone, even with clean, proprietary data. However, a priori knowledge modelling and logical reasoning rules may be used in early products. A hybrid model that requires fewer data is less complex and provides enhanced transparency but requires exploratory research about the ways of building it.

Domain knowledge: Never forget that an MVP needs to solve a particular business or client’s problem, so it is essential to have domain knowledge of that problem. And I add this cannot be left only to the AI technical engineers. Does it ring any bell?

Minimum Viable Performance: Many of you, I bet, have already experienced what performance is in using the basics of AI functionalities. Performances are essential to creating and improving AI. The solutions should deliver value to customers and improve upon the status quo.

MVP, customer benefits and engagement rules:

· For developers: minimum time and costs.

· For clients: expectations of a new solution that works from day 1 VS. their engagement in testing it…

· Risks: Hype and hysteria can undermine the logic of MVP for AI. We have a potential conflict between MVP-oriented thinking and clients’ expectations.

Takeaways from the webinar “Debunk the Myths and Embrace the Future of AI in Consulting”.[3]

The fantastic webinar organised by Jack Houghton and Dexter Hutchings of Mindset AI can be summarised for consultancy in the following key points:

Consultancies are influential in operationalising new methodologies and approaches for better learning and scaling with AI support. Jack said consultancies will focus on selling strategy forms, improvements, learning implementation, coaching, and becoming more software-enabled for recurring revenue. Embedded AI in consulting and critical workloads will also help scale and provide AI expertise to clients.

Another interesting point is managing IP with ML, AI and platforms. The indications are to feed data to your ML, collect data from clients, and use AI within a platform that works with your data for consultancy and clients. What will be done will be training in an actual replica of GPT but understanding the context. And with the user feedback, it will become a clever system that can respond as if it were the consultant.

Points for reflection

Consultancy is people’s work. According to AI evangelists, Humans will not be replaced because AI lacks critical and reflective skills.

Ø Is it a sustainable scenario to believe in?

Ø Do you believe that this hype is a fad-based demand?

Ø If not, can you identify which services can become a commodity?

Ø How to preserve the people’s aspect of consultancy?

Next appointment

Is it enough to consider the distinction between commodities and specialised services? If not, what are further market segmentations? And how can we resolve the uncertainties related to the following:

- We know what we do not know.

- We do not know what we know,

about the client’s behaviour, technology, AI vs not-AI competitors, knowledge transfer, and added value, to create and implement a strategy with AI as an enabler?

[1] - [2] MIT articles [3] “Debunk the Myths and Embrace the Future of AI in Consulting”

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