As always, these are just my own personal opinions
There isn’t a consulting firm out there today that doesn’t have AI and specifically GenAI included in their respective stories. Over the last year or two – consultants and system integrators have done a huge lot of proof of concept work. I don’t know a single client who doesn’t have a clear mandate to derive value from GenAI. This mandate is usually from the board but at a minimum it’s from the C suite. Analysts have been trumpeting GenAI as well.
Having been in this industry for more than a quarter century now, I have seen versions of this play out from ERP in the 90s to web to mobile to cloud and now AI.
So back to GenAI – why is it that everything looks conducive to explosive value add and yet no one seems to be putting massive transformative projects into production?
There are a few common themes
- People have only a vague idea of GenAI and hence most people are looking for use cases that scale with GenAI
- Lot of idea generation sessions have happened but there is no framework to decide which ones to bet on
- For those ideas that seemed promising and hence piloted – the first million dollars of value was easy but that didn’t seem to translate to a scale of tens of millions.
- The quality of data makes it quite hard to translate the value seen on PPT to hard dollars that show up in the general ledger
- Unit economics don’t seem to work in favor at massive scale and business cases don’t hold up. CFOs are especially worried given they already went through one nightmare with unpredictable spend on public clouds.
- Last but not the least – regulatory frameworks have been a challenge for many use cases and deployment models
What we keep forgetting is the simple fact that GenAI is still quite a nascent technology. The good thing about it is that we are past the point where we need to worry about whether it is useful or not. Now the challenge is largely operational in nature – where we need more engineering than science, and more product management than marketing
The world of POC and pilots does not really look all that transformative in my opinion. Sure it’s incrementally better and has some productivity gains for sure. As an engineer, I love how GenAI helps me with code completion, test generation and so on. I enjoy the geekiness of an LLM helping me with emails. Since I am a decent engineer and since I believe I can communicate quite effectively myself – I won’t miss that help if I am told that I can’t use it from tomorrow. These are all just good things to have and gives me confidence to think about the bolder transformations that will follow
Where the small productivity improvements help a lot is that it will conserve time and cost to invest into the bigger things that come next. So I do think that what the tech world has achieved so far is quite useful.
I think the strategy consultants will need to learn GenAI in some serious depth to evolve the frameworks used to qualify which use cases to invest in. Jargon – as much as our industry loves it – ain’t gonna cut it. They will need a much better grounding on the financial modeling of a GenAI deployment as well when the world moves from one model working monolithic to many models working together in a compound system
I have always been a big fan of open source. I think the tech consulting world will largely make use of open source over the long term.
A lot of conversation on GenAI is centered around the idea of a model. Model is absolutely the foundational building block – but I don’t see massive deployments in the enterprise landscape based on one model, and nor do I see GenAI as a stand alone technology.
A minimally sophisticated use case today needs a model and usually some RAG construct to go with it. The future though belongs to compound systems – many different highly specialized smaller models working together in some well orchestrated manner and using both GenAI and other technologies. A crude analogy from the past would be ERP and CRM which were touted to be the one answer to all questions – and when we look back we can see that most of the work happened in integrating them with a lot of other things in the enterprise landscape.
This needs a lot of upskilling for the current tech talent – and will need some serious interdisciplinary training. It will need more training in systems thinking than the consulting world historically is used to and that won’t be an easy transition for many. And given the speed at which AI develops – the tech community will have to spend countless hours learning to just keep up. That’s again not something that we have seen at scale. I am not even sure if the HR teams around the world are capable of handling that scenario, not to mention the CFO teams sweating about modeling the investments and the return on capital. “How we work” will have as much disruption in our organizations as the tech disruption will be in the engineering world.
Let’s consider something like optimzing back office process – say accounts payable – as an example on how the “way we work” will change. Traditionally we would use some lean six sigma analysis to optimize the process, use some tech to automate what we can with OCR and some elementary AI , and then depend on labor arbitrage via BPO to save most of the cost. A lot of companies have taken out hundreds of millions of dollars already this way – there is only so much left to squeeze out with the traditional approach.
If we look at an AI first approach – then the BPO staff would need to learn to properly label the data they are working on . Then an AI team will need to use that to do some supervised learning to get a model trained. To some degree – the better consulting firms all do some version of this already. But we do know that learning by mimicking only gets us to a certain level of efficiency. So we will need to do even more AI work – where the model learns via trial and error (like reinforcement learning). From that point the team will need to build a compound system to orchestrate the piece parts. I am sure those of you who are from the consulting world can already extrapolate the changes in operating model a consulting business will need to pull this off.
As the AI agents become more mainstream and start working with humans – perhaps even interchangeably- there are a host of other aspects to consider. We will need a modern version of the current HCM suites to onboard, train, performance manage and retire the digital agents. It will need a whole new set of integrations with finance and costing apps. And all this assumes that the governments all around the world will get smarter with appropriate regulations
What about the skills we already have? Does anything at all that we know now survive this massive shift?
I do think that despite the need to upskill constantly – many of our existing skills will transfer over just fine. As an example – let’s consider data management. There is no way that any of this AI goodness will happen without great data management ( quality, governance, security, lineage and all that). If anything I think GenAI will make data management the new black. GenAI will make it easier to execute data management for sure – everything from discovery to code fixes will be much easier but the core principles will all be still transferable
I am a massive optimist when it comes to technology. I am perhaps a little too excited about the fun challenges I will get to solve in the next ten years. If I have any regret – it’s just that I am not an engineer in my twenties anymore. I guess my generation had our share of fun with other technology shifts and still will manage to play a small role in this one
And Vijay Says... 