The Top Nine Technology Trends Reshaping Life Sciences Supply Chains
From therapeutic complexity, FDA regulatory policy changes, geopolitical shocks and an AI race, life sciences supply chain leaders are forced to rewrite their playbook to stay relevant in the era of AI. Since my 2023 market outlook, the conversation has shifted from digital transformation to digital autonomy, whereby networks can sense, decide and act for themselves. I've pulled together the nine trends from my client engagements with the world's largest pharmaceutical companies, complemented by grounded analyst data, new surveys and live deployments. These trends set the agenda for the rest of the decade.
Nine Life Sciences Supply Chain Tech Trends
Traditional control towers relied on manually stitching together data and dashboards. That changes with agentic AI. Pharma clients that I advise are running AI agents to improve demand forecasting, optimize inventories and reduce lead times in real time. In McKinsey's 2024 State of AI survey, supply-chain and inventory management was the most cited function for material revenue gains from generative AI deployments, with 53% of adopters already seeing uplift.
Second-generation twins fuse process historians, ERP signals and risk feeds, enabling a virtual knock-down of change orders before a vial is filled. AstraZeneca's World Economic Forum Lighthouse site in Sweden logged a 56% productivity jump after scaling line-to-network twins along with other digital solutions. IDC analysts estimate "that by 2027, 35% of G2000 companies will employ supply chain orchestration tools featuring digital twin capabilities, with the aim of boosting supply chain responsiveness by 15%."
(Disclosure: AstraZeneca is a Microsoft client.)
Since ICH Q13 became final in 2023, the U.S. Food & Drug Administration (FDA) and European Medicines Agency (EMA) have moved CM from pilot to mainstream review to push the number of FDA-approved CM applications into the double digits. Shop floors are now AI-equipped with vision systems and autonomous mobile robots. For example, VisionNav Robotics' automated industrial vehicle forklifts enable CM in material handling through multilayer frame stacking, which improves efficiency.
The CGT modality of drugs is evolving fast and requires even faster supply chains to move this living medicine from a single patient and then return it to that same patient. Market analysts put the CGT supply-chain and cold-chain segment at about $1.5 billion today but forecast it to reach $4.4 billion by 2034. Moving goods to a transparent, modular and trustworthy point-of-care solution turns a historically linear, week-long CGT supply chain into a high-velocity loop that keeps patients confident that the right cells arrive on time, every time.
Leading pharma executives are treating optionality as essential among multiple tariff-agnostic sources as a strategic hedge. The pharma companies I advise are upgrading their digital backbone for increased tariff agility. GSK's CEO highlighted how diversified manufacturing footprints flatten the curve of tariff exposure, allowing the company to protect U.S. margins. Technology can mitigate tariff turbulence through the implementation of AI agents that continuously monitor tariffs, provide proactive what-if analyses and perform sub-second telemetry that can pivot production or sourcing before tariff costs hit the P&L.
(Disclosure: GSK is a Microsoft client.)
Pharma's next performance leap won't come from a shiny new robot; it'll come from tapping into all data sources. A "factory digital backbone' streams real-time shop-floor signals to AI agents that solve the operational issues that plague pharma. It's the nervous system enabling agentic AI to provide clean, trustworthy and continuous data flow for autonomous decision making in manufacturing. Eli Lilly, for example, is already turning raw telemetry into faster cycle times, higher yields and lower costs with predictive maintenance.
(Disclaimer: Eli Lilly is a Microsoft client.)
Nearly every high-growth therapy solution, from mRNA vaccines and CGTs to biologics like GLP-1, fails without a sustainable ultra-cold supply chain. Although forecast accuracy is important, a single temperature excursion can wipe out an entire lot in seconds and trigger shortages that no demand algorithm can undo. With increased focus on biologics and CGTs, temperature-controlled products grew 21% faster than non-cold-chain drugs between 2018 and 2024.
Regulators are digitizing. FDA Commissioner Dr. Makary outlined a goal of cutting approval times 'from 10 to 12 months to just one to two months (paywall)' using AI decision aids. Companies that align AI models and DSCSA data streams early can turn compliance from a monthly paperwork sprint into a minute-by-minute digital handshake with faster approvals and an iron-clad audit trail.
OT networks are vulnerable due to the amount of outdated technologies and the rapid digitization of factories that may open holes that didn't exist before. Gartner now warns that applications protected by public-key cryptography algorithms will be unsafe by 2029. NIST's final FIPS 203/204/205 standards give plants a certified toolkit for mitigating quantum computing risks.
Why All This Matters
Across all nine trends, one strategic mandate emerges: speed with trust. The winners of the next few years will run agentic AI on a real-time digital backbone with robust quantum-ready security. Understanding these trends will equip life sciences leaders with the knowledge to convert volatility into sustainable advantage.
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GPT-5 is an incremental advancement over the prior OpenAI models. It is better in some ways than competing marketplace LLMs, and it is also less capable in some ways when compared head-to-head to available LLMs. See my detailed assessment of GPT-5 at the link here. The bottom line is that if you already know about other popular generative AI and LLMs, you are already ready to use GPT-5. The same nature of prompting applies. The same concerns about what AI can and cannot do are still at play. All those facets are inherited into GPT-5. Boom, drop the mic. There are, though, various crucial changes and differences that are significant, especially in the context of seeking AI mental health advice from GPT-5. Let's get into those vital elements. Auto-Switcher And Processing Time GPT-5 is actually a conglomeration of several submodels that were previously in OpenAI's potpourri of generative AI products. They upgraded their popular LLMs such as GPT-4o, GPT-4o-mini, OpenAI o3, OpenAI o4-mini, GPT-4.1.-nano, etc. Those are essentially collected together under the umbrella of GPT-5. The reason this is important to know is that GPT-5 contains an auto-switcher that secretly examines each of your prompts and then computationally decides which of the GPT-5 submodels ought to be run. You have no control over this. It is entirely willy-nilly and up to the GPT-5 auto-switcher to route all prompts. It used to be that the user selected which of those available models to utilize. This involved knowing what you wanted to do and what each of those models could perform. It all depended on what you were looking to do. Some of the models were faster, some were slower. Some were deeper at certain classes of problems, others were shallower. Not only does the GPT-5 auto-switcher select GPT-5 submodels, but an aligned component determines how much run-time your prompt will be allotted. Maybe GPT-5 will guess suitably and give a prompt sufficient time to reach a solid answer. On the other hand, it is entirely possible that the run-time will be short-changed and, worse still, a less-than-best choice of the submodels will be selected. The answer generated could be woefully weak and incomplete. GPT-5 And Mental Health Analyses The new auto-switching and accompanying processing-time guesswork by GPT-5 is going to, at times, mess up the mental health analyses being performed by GPT-5. Again, I'm not saying that GPT-5 should necessarily be used for said purpose, and only pointing out that if it is used for therapy-related aspects, there is a wanton nature of what's going to occur. First, a submodel might be chosen by the auto-switcher that is less capable than a different one in the context of mental health. The compounding problem is that insufficient run-time might be allotted to the effort. The mental health advice that is generated by GPT-5 could be off-target or half-baked (of course, this is always possible, for all LLMs, in general). Second, the mental health advice could end up so varied that it becomes confusing to the user. They presumably won't realize that there are these underlying submodels being ping-ponged. Each prompt, even in the midst of a conversation or chat with GPT-5 will be getting this runaround. Plus, GPT-5 doesn't reveal its hidden shenanigans. The user might readily assume they are doing something that is causing all the mishmash. Here's the upshot for therapists and mental health professionals. If you are going to use GPT-5 as a tool for your own therapy-oriented double-checker or feedback mechanism, keep in mind that GPT-5 could be tossing you back and forth between various submodels. Be wary. You can also attempt to trick GPT-5 into using a particular submodel – see my prompting tips and techniques regarding GPT-5 at the link here. If you are going to advise your clients and patients to use GPT-5, or if they are doing so anyway, it would behoove you -- and them -- to inform them about this auto-switching boondoggle. They will then at least not be caught completely off-guard by a possible inconsistent set of responses. I'm sure they will be appreciative that you forewarned them. It also provides a kind of added benefit that you are looking out for their best interests and are up-to-speed on the latest AI. Writing Is Enhanced On the writing side of things, GPT-5 has improvements in a myriad of writing aspects. The ability to generate poems is enhanced. Depth of writing and the AI being able to make more compelling stories and narratives seems to be an added plus. I anticipate that written responses to mental health questions will likely be more robust. More involved. Often more complicated than they used to be under the prior OpenAI models. That's the good news. The somewhat bad news is that GPT-5 might produce rather dense and impenetrable responses, rather than being succinct and direct. Another possibility entails GPT-5 sliding into a poem-producing mode. Mental health advisement via poetry is probably not the best route to go. For therapists, you need to realize that a client or patient using GPT-5 is likely to come to you with a seemingly sophisticated mental health argument or narrative. The AI is doing its darnedest to try and look smart. You will need to carefully dissect what GPT-5 has generated and determine what makes sense and what is ill-advised. You can get GPT-5 to fall back to its old ways of writing. In a prompt, all that needs to be done is tell GPT-5 to write in the same manner as GPT-4 (see my prompting suggestion at the link here). It's up to you whether you want to tell your clients and patients about this. Some might find it useful to get GPT-5 to talk like it is GPT-4. Others might not care and will just proceed with the newly uplifted chatter. Lies And AI Hallucinations OpenAI claims that GPT-5 is more honest than prior OpenAI models, plus it is less likely to hallucinate (hallucination is a misappropriated word used in the AI field to describe when the AI produces fictionalized responses that have no bearing in fact or truth). I suppose it might come as a shock to some people that AI has been and continues to lie to us, see my discussion at the link here. I would assume that many people have heard or even witnessed that AI can make things up, i.e., produce an AI hallucination. Worries are that AI hallucinations are so convincing in their appearance of realism, and the AI has an aura of confidence and rightness, that people are misled into believing false statements and, at times, embrace its crazy assertions. See more at the link here. From a mental health angle, an ongoing concern has been that the AI might lie to someone about a mental health issue or perhaps generate a zany response due to encountering an AI hallucination. A person seeking therapy via the AI is vulnerable to believing whatever the AI says. They might not be able to readily figure out that the advice being given is bogus, or worse, harmful to them. A presumed upbeat consideration is that apparently GPT-5 reduces the lying and reduces the AI hallucinations. The downbeat news is that it isn't zero. In other words, it is still going to lie and still going to hallucinate. This might happen on a less frequent basis, but nonetheless remains a chancy concern. If you want to try and get GPT-5 to lie less of the time, and have hallucinations less of the time, you can use a two-step prompt that will sternly instruct GPT-5 on this (see my prompting suggestion at the link here). Please know that no matter what you tell GPT-5, there is still a chance of it lying and hallucinating. In terms of clients and patients, your best bet is to emphasize repeatedly that whatever GPT-5 says, and whatever any LLM says, they must remain stoutly wary and alert. I'd suggest you mention this crucial point each time that you have a conversation with a client or patient and when the topic of using AI comes up. Pound away at the disturbing underbelly that AI lies and makes things up. Personas Are Coming To The Fore I've repeatedly emphasized in my writing and talks about generative AI that one of the most underutilized and least known pieces of quite useful functionality is the capability of forming personas in the AI (see the link here). You can tell the AI to pretend to be a known person, such as a celebrity or historical figure, and the AI will attempt to do so. In the context of mental health, I showcased how telling AI to simulate Sigmund Freud can be a useful learning tool for mental health professionals, see the link here. OpenAI has indicated they are selectively making available a set of four new preset personas, consisting of Cynic, Robot, Listener, and Nerd. Each of those personas represents those names. The AI shifts into a mode reflecting those types of personalities. As a mental health professional, you ought to give serious consideration to making use of personas in GPT-5 for your own self-training and personal refinement. I'd recommend that you generally avoid the newly devised preset personas. Instead, create personas that have particular relevance to you. For example, you might craft a persona that will pretend to be a person with deep depression. You could then use this persona to hone your therapeutic prowess regarding depression in patients and clients. It can be quite useful. Plus, there is no danger in the sense that since it is just AI, you can try out various avenues to gauge what works and doesn't work. No harm, no foul. For my suggestions on how to prompt the invoking of a persona, see the link here. In terms of clients and patients, I would generally suggest that you do not lean them into using personas. Some people get lost in a persona and start to believe that the AI is real. That's not something to be encouraged. If a client or patient starts to use personas in GPT-5, it would be useful to have them indicate what the persona is and why they feel their activity is meritorious. Then, if feasible, wean them away from the use of personas. That's a case-by-case decision, and a consideration as a therapist that you'll need to studiously decide on. AI Mental Health Is In Flux I'll finish here with some hefty thoughts as a heads-up for you to consider. AI makers find themselves in quite a pickle. By allowing their AI to be used for mental health purposes, they are opening the door to serious legal liability, along with damaging reputational hits if their AI gets caught dispensing inappropriate guidance. So far, they've been relatively lucky and have not yet gotten severely stung by their AI serving in a therapist role. Meanwhile, new laws might put the kibosh on generic unregulated generative AI providing any semblance of mental health advisement. The recently enacted Illinois law restricting AI for mental health usage in Illinois puts the AI makers in quite a rough spot, see my discussion at the link here and the link here. Other states and the federal government might decide to enact similar laws. The Illinois law also applies stringently to therapists and mental health professionals. Overall, the restrictions come down to only allowing AI usage for mainly administrative chores by therapists, such as billing, scheduling, and the like. The use of AI as a therapeutic adjunct is almost entirely placed out of bounds. All-In Or All-Out Though I understand the logic of a veritable ban associated with therapists employing generic unregulated generative AI, doing so to protect consumers, I don't believe it is prudent. It seems like a head-in-the-sand proclamation and belies existing reality. It grossly belies the future growth in the global widespread adoption of AI that is inexorably barreling this way. As the famed trauma physician Gabor Maté astutely reminds us: 'The attempt to escape from pain, is what creates more pain.' Let's keep that in mind as attempts are made to deal with a complex problem in somewhat simplistic ways.
