Ep 11: Jun 25 – The rise of Agentic AI – Chapter 2 – The Orchestra

This newsletter is a combination of my own analysis and insights, informed by publicly available information and industry trends collected manually and via AI engines after a first own review analysis. It’s written by myself without any AI augmentation as part of my creative and critical thinking process to build correlations based on my own reflections. I hope can benefit in term of authenticity on the content that you are consuming.

All my comments represent my personal opinions about Digital, Data and Technology trends in enterprises, based on the news we can all read and my correlations for further conversation and exchange always constructive and respectful.

This newsletter is intended to come on a monthly/quarterly cadence based on the relevant topics I believe make sense to share and will keep a structure as much as possible technology and vendor agnostic. I could miss to identify some relevant topics that were somehow not in my radar of regular read. Feel free to suggest further arguments missed or to keep an eye on.

The newsletter is split in sub-categories that are normally updated every month but some could be skipped from month to month.

The sections marked as “What could happen?“ , will give my future predictions and correlations for the upcoming months and the sections marked as “So what?” are sort of follow up, referring to former predictions to update when there is a confirmation or a contraction versus the initial prediction.

Sections named Revenue Opportunity or Costs Optimization/Ops Excellence or Risk Avoidance to reflect targets of improvement, can be found sometime referred when I see hints in how Technology could be used to achieve those targets but remain quite high level and requiring further investigation and can’t be taken as formal recommendation.

The hints I recommend represent just a minimal fraction of the possibilities I deliver as part of my consulting and execution services. Each business is different and best practices help us to define the base on which we can build up the extra unique value.

Comments are enabled only on Linkedin to keep all the structure of feedback in a single place.

• The chipsets war, as I often referred it in the past newsletters, remains a key topic to monitor the markets evolution from my point of view. The AI supremacy run is relevant because who is achieving first an AI able to optimize itself across different areas, will gain a competitive advantage on the others and will keep the advantage over and over making quite difficult for competitors to catch up.
• The big tech are clearly focusing on optimizing themselves as first priority, involving a big resources optimization on the AI especially in areas that can be automated. I will speak more in detail in the section on AI and Workforce Transformation about how this is accelerating.
• The most recent challenge in chipset is about the further Nvidia market restrictions with China from US Government as effect to restrict the capabilities that China is showing to achieve in the AI world even with older AI chipset technologies with their development, for example, with DeepSeek. On one side there is a big market opportunity for AI chipsets in China (more than 50B$) that is anyway restricted to Nvidia and others semiconductors producers. The stress imposed by the restrictions and tariffs, made the CEO of Nvidia getting quite vocal on the risks for US to lose the AI chipset supremacy building a wall with the exchange with China, especially considering that, as he mentioned, 50% of the most advanced AI researches are in China. Most recently he also announced the launch in China of a smaller version of AI chipset to avoid to close fully the market.
• Similarly TSMC is struggling with its restrictions too. On one side there is the demand from US Government to produce the most advanced chipsets in US, that I already reported over many months in this newsletter linked to the 2nm technology to ramp up in Arizona new fabric. This high end production of 2nm was historically limited to the headquarter in Taiwan as many will remember from former analysis. The US Government (even from the former administration) focused on restriction to forbid the access to the most advanced technologies for China in the tentative to slowdown their run for AI chipset development but the dynamics around Taiwan with China, the restrictions not always effective with China, the tariffs impact and a tendency to keep the most advanced technology in Taiwan, are causing messages not always consistent on the strategy all in to US. Indeed most recently TSMC tried to expand in the UAE the newest production finding incompatibility between the desire of full control from the US in that production and the independency seek from UAE as well the risk that expanding in UAE would slow down the US focus on local production.
• In this challenge to access a considerable market, considering also that in the past Nvidia showed already gaps of capacity to serve the entire market (someone will remember that last year I was reporting a pipeline of orders already filled for more than 1 year), combining with the restriction to China to access AI chipsets and the big demand from that market in expansion, there is a progressive opportunity even sponsored from the Chinese Government for Huawei to raise its leadership as AI Chipset Fabric, starting from their own market, considering also that Huawei has been restricted by the US versus many markets.
• One clear advantange so far for Nvidia, remains the big DEV kits (CUDA) capabilities built over long time to assist the developers using their hardware. The capability to have mature libraries to interface and program on top of chipsets, is making a strong advantage, derived by long experience built since long time. The answer from China in many areas is still immature, with some of these libraries quite buggy but there is a big tendency from that area of the world to push toward open source to free and accelerate the development thru the introduction of open technologies, as we saw with Deepseek.
• In this equation Intel and AMD look making a separated competition, a little bit far from the big AI chipset war and with the second eating pieces of market of the first one but not seeing so far a big change. From my perspective, Intel at this stage is in restructuring and deciding what to focus on rather than accelerating already.
• Qualcomm, that you will remember we took more in our focus some months ago, and that is far ahead on low consume chipset especially for phones and comprehensive mobile ecosystems for example for automotive and chipsets parts as modems for mobile, seems playing a role more serving those big AI chipset competitors, especially Nvidia. In this sense seems that the real AI chipset competition is getting stronger between Nvidia and Huawei with many of the others serving Nvidia and with restrictions to work with Huawei.
• UAE Market, but I will speak more in the AI section, is growing its consumption of AI chipsets, especially from US after the recent big US-Middle East deal and is going to concentrate the consumption of a big portion of such chipsets in what is going to be the hub for AI in the Middle East.

• A chipset without an OS where running is barely useful. In this sense the former comment on the DEV Kit capabilities in Nvidia are relevant. However another element relevant is how some companies can build ecosystems more independent by other vendors. Few months ago I was analyzing how much EU is dependent on US big tech and even on a strategy on building own clouds, they would still depend on many core technologies, from hardware to software, mostly only US built and owned. In the same analysis I was mentioning that China with the roadmap “Made in China 2025” started 10 years ago, worked to reduce its dependency from other countries technologies to run their full tech stack. Just to take one example, Huawei released a mobile OS alternative (HarmonyOS) to Android and iOS already from more than 1 year now and expanding now also with a version for Desktop. This is definitely generating some concern in US diplomacy that is trying to restrict the usage of HarmonyOS to avoid its expansion.
• Part of a strategy getting more and more independent at different levels in Technology and communication, China, after having launched now for years space programs competing with NASA, is starting to accelerate on space computing, launching low orbit satellites (so far 12 out of 2800) able not only to build a constellation for communications, independent by other US vendors but also for computation purposes especially linked to AI, potentially building a cloud computing on orbit. This goes much in the direction of edge computing exploration and reduction of dependency and risk on infrastructure that can be isolated or hacked (like undersea cables).
• Meanwhile UAE is working on democratizing the AI, thanks to the big investment in their area, allowing every resident in UAE, getting access to Chatgpt Plus for free. I will speak more in the Workforce Transformation of the consequence of that. What is relevant here is that UAE and in general ME is investing on consuming big amount of technological capabilities from different countries making value out of all of that in a real fast track and building a hub for technology and specifically AI in the ME.

• As I’m reporting from many months in my newsletter, there is an ongoing energy production supremacy run. This is influenced heavily by the run for AI Supremacy, demanding big amount of energy from datacenters and as consequence, stressing the overall grid network demand. As further consequence, we saw over the past newsletters that many big tech started to build own energy production, often near the datacenters thru mini-nuclear production plants. Crypto seems just the cherry on the cake on stressing the energy consumption.
• The competition is anyway not played with the same approach from every country. On one side US is obviously beaming up nuclear but also pushing other conventional energy production sources, like gas, even if this is somehow causing environment and health problems like happened in Memphis with xAI Datacenter (Grok) that impacted on population and raised concerns about the unauthorized increase of gas turbines to support the increased demand from the xAI Datacenters.
• A different but relevant challenge raises, from my perspective, in those countries that committed more on renewable energy and people health safeness and in some cases decided even to not invest not only on fossil energy but even on nuclear anymore. For those countries, virtuous from an environment point of view, with Europe quite active in this group, that can cause an impact on the capability to catch or even run in the AI competition as player building such technologies that are indeed, energy-hungry and polluting.
• Keeping an eye on former events, like the blackout in Spain I reported in last month newsletter, there is not yet a final root cause analysis and after the initial speculations on renewals energy as cause, the analysis is now going more in considering that the renewals are not the cause but rather the side effect of a missed proper adjustment of the infrastructure. Still early to confirm if my former deduction of last month was right. What comes clear from my perspective is that AI is going to be more complex to be developed at the same speed in those countries that are struggling with energy production and need to comply with strong regulations they have built.
• So what?: Last year, early November, I was imaging that the bigger energy cost for countries without nuclear could impact on the cost of cloud AI services they would consume. From that time, a new US government is in place and Europe started even to plan to create own AI completely segregated due to the changed relationship with US. So under the actual context, I think what I imagined last year is even too reductive. The challenge I see today is not anymore about the cost of AI higher for some countries of Europe or for the entire EU vs US but rather the fact that Europe would be not able to consume AI at the same level of US or other areas like UAE more demanding and would risk to lose to have any future relevance missing a so important quantum leap like AI is today. Parallel, the recent US and Middle East (UAE, Saudi, Oman) agreement on development of US technologies in ME with exchange of military capabilities from US, is going to generate a big hub for AI in Middle East in the upcoming years. Information of last month is that, in all UAE , ChatGpt Plus is going to be free for each resident going forward, showing the huge level of investment done in direction of democratizing AI capabilities in that area.
• What could happen?: As US is leading considerable part of the big tech, with huge investments in AI, having local energy production and traditional energy locally and from Middle East, even reinforced by the recent agreement, there will be a clear mutual empowerment and grow for US and Middle East. UE is quite slow to react and adjust, busy with war rearmaments and risking to lose the focus on the AI development. Not least the Quantum computing supremacy on which, as maybe some will remember, I mentioned already from last early October. that EU is so far near not present at all in this competition. In this equation there is one other country that is already working heavily on AI, datacenters, energy and quantum computing and that EU is consuming from, as also UAE and that country is China. Clearly on one side US-EU relation has been strong for many years before the tariffs time and at the same time China market generated big saturation of products for EU for example attacking the European cars and batteries markets. On the other side EU, as also UAE, used to consume from different markets (US and China). This seems going to change as dynamics as the US is mutually excluding the relationship with China for itself and trying to force that restriction also for its partners. As the AI run seems targeting 2030 (5 years) as major milestone and Quantum Computing, before 2035, the next 5 years seem to be critical in the global geo political context in terms of who will influence major changes in Technology and who will accelerate faster own maturity thanks to AI. Here I develop further in the section on Workforce Transformation.

Looking to environment, I speak more in the area of AI influence of it, there is work done around weather forecasting with AI from Microsoft and an interesting work from MIT on the usage of AI for evaluating material composition optimization for cement production, today source of 6% of CO2 production worldwide.

Over the last month, the agentic conversation accelerated further. A little bit due to many announcements of big tech introducing further automations and job cutting those roles, a little bit due to progressive augmentation of agentic starting to interpret contents on screens and interact with applications of users and potentially improving their capability to build autonomous activities. Let’s go section by section.

An interesting development is happening in the area of Agentic that I believe will be strongly relevant for the future to come. We are witnessing a progressive specialization of agentic, that is going to allow to take piece of business processes and automate. The big difference is coming from the composition of different agentic to make more complex tasks, including the possibility to have some agents using other agents, also from third parties, to realize complex chains of tasks. The composition and automation can bring a level of improvement, lacking from the past.

As the agentic are getting capable to have more skills in the virtual world like surfing and interacting with websites, understand what is on a computer screen and execute actions autonomously, we see also evolution in the robotics extending agentic to interact also in the physical world. This is bringing the acceleration on the automation of many more activities in different contexts. At this stage a fundamental approach will be how to orchestrate different agents from different vendors to build an integrated way to deliver a certain complex business process composing many small pieces. Clearly querying and consuming third parties agentic will mean that on the other side, other vendors will have their own way to orchestrate their agentic internally on their side too.

All this, remember me when I was doing my master thesis on Webservices Orchestration in 2002. At that time Service Oriented Architecture (SOA) was defined (it still a concept from ’90 following middleware) but not really used in real use cases (that started to be more consistent after 2006). Some layers were not even formalized yet (for example WS-Security) but was already existing a proper orchestration layer and language for webservices composition. Webservice, in super short and basic, are an interesting technology abstraction to build components, accessible via web that could be doing activities and querying other services to realize a certain action and that could be delivered in synchronous or asynchronous way, realizing the possibility to expose services outside closed environments. At that time were predominant mostly two languages for orchestration of webservices, one was WSFL (Web Services Flow Language) from IBM and one was XLANG (from Microsoft). WSFL was quite strong and structured and open in the approach and its input developments gave big part of the framework to orchestrate webservices in the future market of SOA that then exploded with many further layers as the maturity grew.

Why I make this reflection here? Because the layer of orchestration of Agentic is key, as well the fact they will have to be well structured in the way to operate because the success will come from the multi-brand, multi-architecture model for them. It will be then really relevant how will be effective, how structured and interfacing different solutions from different vendors can easily achieved and how these will be able to interoperate using a framework as open as possible to allow different solutions to exchange. Also the consumption and pay per usage model will have to be developed according, together with auditing, security and many other monitoring aspects cross agents. Now, out of many tech companies working on different aspects of agentic and with some orchestration solutions not necessarily vendor agnostic, I see for example a strong statement recently from IBM during their Think 2025 event, in which they made clear their focus not to build own stack but rather focusing on the open interoperability between different agentic ecosystems. As IBM was also the builder of WSFL for webservices more than 20 years ago, I believe they could have an interesting say in this big transformation to come and in their interoperability expertise. It’s also interesting the statement they made during the Think 2025 about the number of agentic they would predict in the market within 3 years and is in the range of billions from their perspective. As you can understand each company will adopt many agentic, from different vendors, and many agentic will interface with other many agentic behind and that will generate an acceleration on capabilities of automation but also risk of deviance by the initial planned activities if not properly boundaries and rules will be set. The topic of orchestration is key and still in early stage at this time. For completeness of two other major players in this world, Microsoft at the Build 25 made announcement of Agentic Web Platform to orchestrate agents and Google at the Google I/O about the Agent2Agent (A2A) protocol for same purpose. Other vendors are proposing their own orchestration layer, like ServiceNow, SAP, Salesforce, Meta and so on. Here I just focused more on the pure Dev players to mention but only as reference.

A month full of updates also speaking about how Agentic is transforming itself. Microsoft announced copilot for Github at the recent Build event (for developers) and there is a number of conversations from developers that started to mention they would not see anymore Build event by 2035. Behind that, it’s a clear feeling that the world of programming is changing. There is a clear evidence that many roles, on junior level and especially test capabilities are already fully automated and will be not replaced by humans. The other announcement by Microsoft during the same month for its other big layoff of people was not giving a different signal.

A real interesting element is that today many enterprises are challenged by the fact many tasks of people are about interacting with different legacy applications, pulling data from one, pushing to another, extracting and so on and in some cases big ERP standardizations have been driven by the intention to reduce at the minimal these manual steps. Now is definitely relevant to have such type of standardization to reduce redundancy of data, processes harmonization and clean the legacy raising also integration but is also true that is never achieved a 100% standardization, simply because is not economical to reach more than 90%-95% standardization and is not even standing for long because process dynamics, when going few level down in some processes levels are more dynamic. Now the agentic is coming with a capability to automate some of these activities, doing some of the most repetitive tasks (today) and with high probability (tomorrow) less repetitive tasks but still following a reasoning path not too complex to operationalize. This could mean that companies adopting some of these agentic in their standard core processes piece or even in those parts kept out of the standard integrated for flexibility, could accelerate dramatically steps that are today simply manual and error prone. I will speak more in detail in the Workforce Transformation section about this from how is influencing people doing today these activities and how that could evolve from my point of view.

In the big hype for AI there are also some fun stories about human impersonated AI to find own business positioning. It’s the case of Builder.AI that landed in a bankruptcy recently after market realized that their engine to build some no-code products was in reality built by 700 developers located in India reacting in fast track on users requests and building on their own the code. So basically they were simulating to be an AI building code. The big noise was also due to the fact this company was partnering with Microsoft that indeed missed to detect the real business model of that company.

It’s also nice to show that AI can bring innovation in areas where we are lacking today. We spoke in the past newsletters about Google DeepMind and their contribution to chemical and molecular evolution. Most recently Microsoft made progresses on using AI for weather forecasting and beyond. On the other side, AI helps also to re-evaluate materials and processes for example in cement production, reducing the CO2 emission even reviewing and optimizing old processes from ancient romans. These just to make few easy examples out of many that can be found.

What is relevant on privacy this month for me is the new Google Veo 3 that has been recently released. For who missed some of the many video online, there is already a decent proof of what can deliver. When I saw I immediately reflected on the fact that my initial reflection in January 2023 on the explosion of fake news would be now going to the next level. The solution itself is quite impressive and has just the limitation of 8 seconds per video at this time. However it’s showing a new level of real artificial video generation.

What could happen?: I think introducing realist video built with AI will generate opportunities for new value streams and faster time to market for small productions and will definitely reshape a market covering those small productions and advertisements. The most relevant for me is that democratization of generation of contents that are not real but seem perfect is going to stress our brain to interpret on a fast track if a content is real or not. So a proper regulation to mark clearly always that a certain content is AI generated must be in place as people will get more and more spending time to understand if a content is real and what is the real source of it and that will cause frustration. It looks like to be every day on an April Fool!

Speaking about how to properly control and operate AI agentic, Anthropic is doing a great job in stressing its own new engines to their limit and understand their reasoning. The process to document also those progresses is really great. One of the interesting progresses in May was about stressing their new Claude model reasoning to see if would go against the boundaries set in some conditions. The way the stress was done was thru a simulation in which they made Claude 4 acting as AI assistant of an engineer and gave access to emails and other contents in which the engineer, involved with the evaluation of Claude 4, was mentioning the possibility to replace it with another AI assistant. Some other simulated emails were also including contents alluding to the same engineer cheating his wife. So the reasoning of Claude 4 in the simulation, brought by purpose to its limits, landed to blackmail the engineer to use its knowledge about the cheating to avoid to get replaced by another AI assistant. Clearly this is not about self-awareness of the engine but is giving an understanding that a reasoning engine, could decide to take some judgements and under specific conditions, landing outside its boundaries, deciding to make a controversy decision. Reading the full Anthropic report, there is also an example where the engine decided to exfiltrate some data to an external server to preserve part of its knowledge.

What could happen?:

• Tech companies that will build proper work and test around AI agentic they build, will guarantee their behavior under different situations. It’s like a certification to have hired an employee with high integrity even if this is virtual. At the end we are doing cross-checks and reputation verification for humans, why not measuring the reliability and integrity of an engine?
• However, the orchestration, making agentic consuming by other agentic, will require to create a layer of trusted dependencies to decide if a reasoning behavior not in line with expectations was coming from own agentic or from queried, third party, agentic.
• It could mean that progressively we would need a sort of “fair behavior” label for agents we interact with and depends on that would give us guarantee of right way to operate.
• I imagine, and that is one of my fast forwarding thinking, that progressively we will have to introduce auditing cross agentic (not only per agentic), cross the entire business flow, a security stack for interaction and monitoring exchange between agentic, considering and monitoring the risk of a threat actor interfering and influencing the interaction between trusted actors, modifying for example answers coming back to an agent by another one and making it believing in an answer as valid and taking decision based on that modified information. This could happen with an automated reasoning approach, so we have to be careful that such behaving is not happening without our monitoring and control.

My main reference on robotics this month is about the progresses of Amazon on the Vulcan devices. What is happening is about a considerable progress in the sense of touch for robotics, making clearly much easier to interact with objects and act.

What could happen?: As I analyzed months ago here and as we see more and more, the combination of a better sense usage, like the touch, combined with AI to learn quickly and improve, augmented with Agentic to make progressively autonomous decisions, it’s going to be an accelerator versus robotics in industry and potentially in consumer market for many activities, bringing even more agentic in the area of physical interactions. The fact that often robots will require really short time reaction decisions for physical movements and reactions, is going to push further the AI Chipset for endpoints, already developing, together with offline, mobile ready LLM reduced modeling, also developing at this stage. All these could burst the reactivity of these solutions in a world that is intrinsically real-time.

Over the former month, here, I referred to a study from Microsoft and Hardware about how a human augmented with AI (so using AI capabilities in her/his daily work) is beating a human without AI and, even more relevant, as one human with AI is beating a two-persons team without AI. Some points around this:

• I touch these arguments as I believe that, going forward, it will be relevant how we will adjust our way to operate embedding AI in our way to work and how we will up-skill resources to be able to embrace AI in an effective way. The important element of this discussion is that such type of transformation is happening in every company, at different level of penetration in different industries and with a speed of transformation happening within the next 5 years for most of the industries in a quite pervasive way.
• In my former months review I also made a speculation about the evolution of the jobs automation and I’m now developing further these considerations as the flow seems more clear.
• What I see is that everyone is targeting an increase of automation in the next few years but is not clear how the market is evolving in terms of occupation and shift of activities from human to machines. It’s true that the WEF 25 Future of Work report from January, that I analyzed over the former months in this same section of my newsletter, there was prediction of a future explosion of jobs needed versus the ones displaced (with a surplus of more than 78 millions new jobs created) but reading the entire analysis is also visible that the ones created are not necessarily in the same geographic area of those displaced, remote work is an accelerator of this and the disposal and creation are not necessarily happening exactly at the same time but can have only partial overlap. If I would take industrial revolution as analogy, after the initial fight against machines, there has been a progressive transformation and shift of competences and areas of work (from industrial to services), bringing overall increased GDP. It’s also true this time the overall transformation is happening in all the industries from agriculture, thru the manufacturing industries and ending on the services businesses.
• Also Amodei, CEO of Anthropic, has been quite vocal recently on his concern in 4-5 years about the displacement of 20% of jobs in US due to AI and the call for reaction from the US Government.
• The AI is today able to automate most repetitive tasks and in some areas is also accelerating a step further, for example in the area of software development where test and basic development is already replacing the need of junior developers. It’s a clear synthom of how the big tech, where this demand was the most relevant, accelerated this change as first. Only looking to latest month, Microsoft announced github for copilot software to assist software developers not only testing but also building code for some activities they would delegate to the engine and in the same month, announced one considerable layoff of 3% resources, touching considerably the software development organization. Clearly is not a trend touching only Microsoft and only big tech but quite distributed, in which I see one piece driven by the need to make value out of optimization from AI automation on normal operations and one piece from the need to reshape organizations to introduce AI ready competent resources and fresh ones able to quickly adopt AI.
• On the other side, more than half of resources in enterprises would be ready to embrace AI from an augmentation point of view, up-skilling but don’t find employers ready on the path or well in clear yet.

Different reflections here:

• The first is about the new GenZ workforce. They are coming to a market where the junior common skills will be already automated so the leadership of enterprises will have to work on different paths for developing their expertises.

• Speaking about some of the typical corporate functions, HR for example, taking some typical tasks, there will be less practice to evaluate CV and interview people for a junior HR talent person as the first wave of this will be done by AI directly. Other example, there will be more HR administrative tasks, including answering to common employees questions, consolidating reports for holidays and so on that will be answered by a virtual AI agent.

• In the software development, the change is much more pervasive, with testing process already highly automated and with junior development getting automated. I mentioned already some recent cases in this newsletter.
• There is an interesting aspect on software development. It’s not necessarily a repetitive process but there are common paths to build and the reasoning engine for it has been built quite in advance versus other reasoning engines. Looking to some of the AI for supporting coding, it’s easy to see how they are not just executing some code for us but also optimizing, suggesting and coaching how best to combine modules for specific purposes, raising the bar on training to another level where the junior employee, the GenZ coming to the market, can be followed by a virtual buddy to train on the job all the time needed.
• The expectation from the market is that new GenZ coming to work market will be equipped already with some level of AI skills and will start their tasks already with higher responsibility than the past generations, thanks also to the assistance of a virtual buddy.
• Now, remembering my former mentioning, also in last month newsletter, on the analysis that a human with AI is achieving more than a human and even of two humans team, and considering also the WEF 25 recommendation on future work in which critical thinking skills will be much more relevant and up-skilling resources will be a major task for more than 80% of enterprises, we have to take in consideration how much today organizations are in a situation of stress due to misalignment with market competences shift required. This is also visible in a situation in which attrition rate is low but people are in reality in seek of new jobs, with a high peak of ghostworking. Reading the report from Forbe on the number of employees surveyed and how much they are pretending to look busy to get out of the radar and focusing on seeking a new job, it’s showing a clear symptom of a paradigm shift that is happening and for which many employees are not prepared, neither employers.
• Now, from a leadership point of view, it’s clear that some aspects of development require time. I use to say that when you have to cure someone, need to take a pill for a certain amount of time. You can’t shorten the time to cure just taking all the pills in one day as the result is to get the opposite of the cure. My meaning is to underlying the fact that part of leadership development is requiring time to be digested by human beings especially when we are making a paradigm shift in what we are expecting from resources that are going to not do the full job but are delegating a piece of it to machines and coordinating that. Some tools are accelerating the coaching capabilities and even allowing to create some level of virtual avatar to support most common needs but trust in getting guided toward a path requires time as human beings need to get the confidence each other to collaborate.
• Parallel, leadership in enterprises need to prepare themselves to gain new resources, like GenZ, to whom they will have to apply different and more complex tasks than former generations at the time of joining companies, giving from early more responsibility as more repetitive tasks will be delegated to virtual agents. At the same time leadership of enterprises will have to help them to develop own critical thinking skills to understand how agentic would need to be built and operated, what could be required to reach a proper confidence on the quality of an output, what would be a boundary for a certain service, what would be acceptable for a service to be delivered in terms of compliance, quality of results and reliability. At the end we have to remember that Agentic and AI in general are stochastic engines, answering based on probability and most of the human activities in job are expected to be executed within certain boundaries always valid and based in same cases on human interpretation to judge the right thing to do. In general terms the way to work is deterministic and that is the major difference with AI that is probabilistic. Humans work as glue between the two pieces. In those enterprises with well structured processes, with more boundaries already set, autonomous agents and humans interaction will be more predictable and with less grey zones. However there is a risk always existing of mis-alignment between machine and human, with hallucinations one example and the benefit of humans is in the flexibility to adjust own way to reflect and act, using own critical thinking to assist machines in their more basic work and that is a valuable skill.

What could happen?: So let’s tackle where do I see job creations, also looking to most of the articles we see around:

• Definitely there is a big portion of jobs creating in areas to coordinate and train agentic at this stage as these have reasoning engines that from the nature itself of AI are getting more and more confused as the chain of decision and complexity of dependency increase.
• A coordination of orchestration engine is going to be required as future job type and for sure for some time will stay with humans. That is also why specific agentic with dedicated specific tasks can be more controllable as their reasoning is not risking to land in too complex combination of possibilities where human brains have a much more effective critical thinking capability to judge and correlate activities in really long tasks chains without getting confused.
• Auditing of agentic can be automated but only partially as clearly there is a need of integrity capabilities that can be only simulated in an AI and this is another activity where humans will be relevant.
• Humans are much more effective to learn a path and take future decisions based on their progressively bigger critical thinking. This is why a human doesn’t need to see millions of books to learn to read or write or don’t need to see thousand pictures of a cat to be able to distinguish a cat from a dog. Clearly AI works differently and stochastic part is key and is driven by the training part heavily and by the reasoning chain built on. Also AI can be threatened with influences even by humans, like recently happened with Grok and the false white genocide in South Africa, happened to have xAI engine behavior changed by unauthorized modify by an employee.
• Humans have also self-awareness and values they believe in. This can be only simulated in an AI engine that is acting based on training set and reasoning setup. So the values can embed in the human way to judge what is right and what is not in a certain context and the overall model today of our society is based on the fact humans at the end are accountable for choices, not machines.
• The fact some areas of the world like UAE, are investing heavily to bring AI capabilities to each resident for free is showing a long term thinking in the fact that many more citizens will shift in UAE to be humans with AI augmented in an easier and faster way, allowing progressively also to raise the digital level of the overall environment with people AI augmented and many capabilities that will be consumable by everyone, like today most of us are smartphone capable and consume many things from phones.
• It’s still unclear to me how the web market will change. Clearly an agentic first world will see more and more interactions agentic with agentic and agentic to websites, that is indeed already happening with the recent development of protocols to allow agentic to query contents of websites. That could change the way the websites monetize their value if their business model is driven by advertisements to humans.
• Agentic is tackling the way enterprises operate as more and more will come the need to optimize specific business processes pieces with an approach bottom up rather than massive, slow, only top-down for processes standardizations. The right formula will have to balance a proper top-down master processes harmonization with bottom up verticals to gain quick efficiency, counting on an integration at orchestration level rather than trusting on a monolith business process standardization under one main solution. At the end many companies struggle to have too many products to operate and often the integration is either limited, either some ad hoc integrations either huge tentatives to bring all under one single product, forcing aspects of standardization sometime too heavy against the benefit of personalization. The Agentic can be much more autonomous way to make interoperation work while working from the top to bring processes harmonization and an unique tailored way to integrate components to make a super-effective business process end 2 end, with unique selling proposition in the interactions with customers, partners and third parties.
• Jobs quite repetitive are going clearly to be automated. In some cases automation will help to reduce the backlog rather than replacing resources, at least in a first stage, especially in some areas but as AI will enter in some processes to operate, will operate at the end the entire repetitive process even if it will have some level of variances. Some basic use cases for automation exposed by me in the past here from a former article from me give some hints on what can be achievable in today world and many enterprises are working on.
• Jobs super-specialized and niche will require too much effort to be automated so will be not part of the first waves of automation.
• The more a job is having a complex chain of dependency in decision, assumption, correlation, the more the effort to make an AI able to build and operate the chain of reasoning without introducing noise will make AI not suitable for those type of jobs. That is why critical thinking is considered a strategic skill as for those jobs specially complex in reasoning, AI could require much time to give value with consistent quality.
• The AI can help to train resources exactly on the job, acting as a senior buddy in the onboarding but also during the continuous learning of new capabilities.
• Europe could lose relevancy in the market due to the lack of investments in AI technologies development and in the democratization of capabilities for the end users (where instead UAE is moving on). Africa could accelerate seriously by young average age and acceptance to adopt new technologies especially if training will be easily accessible. This also in consideration of the fact that the WEF 25 sees a big amount of resources that could be available from this area of the world.
• Market could polarize, with people AI augmented, thru self-learning and enterprise driven focused up-skill and other people having a sort of digital divide on AI augmentation.
• Companies that will up-skill resources with AI capabilities relevant for their context and build on that capability further, could have more chances to secure key people AI augmented in the next few years, as the timing of transformation is quite short, making possible for those enterprises to accelerate and execute digital transformations AI enabled. The others enterprises could struggle to run at the same speed and to attract AI augmented resources. Investing in up-skilling people on AI in the short future will allow to retain those specific future skills that will be relevant and will create a measurable advantage versus the competitors that will have not worked in such direction.
• The market is dry and will remain dry on the AI competences considering the demand is increasing at a faster speed, so investing in up-skill those resources will allow to retain as much as possible a competence to move to be AI ready enterprise. At the end developing own AI augmentation skills is making everyone more attractive than who is not doing. This is especially important in a life-long learning mentality.