Aditya Bhavsar 45

In the scientific publishing industry, the Improvement Specialist (often focused on Lean Six Sigma, operational excellence, or workflow optimization) is moving from a role of "process improvement professional" to "AI-system architect." In a field historically bogged down by long peer-review cycles and manual formatting checks, AI isn't just a tool; it's the new infrastructure. Evolution of the Improvement Specialist (2025–2035) Over the next decade, the career path for an Improvement Specialist in scientific publishing will shift from incremental efficiency to intelligent transformation. 1. The Shrinking Tasks (Automation) Manual audits of "time-to-publication" or identifying bottlenecks in the peer-review queue will disappear. Automated dashboards will now provide real-time, predictive analytics. The specialist will no longer spend time finding the waste; they will spend time redesigning the system that produced it. 2. The Expanding Tasks (AI Governance) The specialist’s scope will expand into Ethical AI Integration. They will oversee the "integrity workflow"—ensuring that AI tools used for detecting image manipulation or plagiarism are optimized for accuracy (minimizing false positives) while maintaining a high speed of processing. 3. Emerging Hybrid Role: The "Human-in-the-Loop" Orchestrator We will see the rise of the AI Operations (AIOps) Excellence Manager. This role combines domain expertise (understanding the sanctity of the peer-review process) with the technical ability to tune AI models to ensure they don't compromise scientific rigor for the sake of speed. The Evolving Career Ladder Stage Current Definition AI-Enabled Future (5–10 Years) Entry Level Data collection and basic process mapping. Prompt & Workflow Engineer: Optimizing LLM chains for editorial triaging. Mid-Level Leading Green/Black Belt projects to reduce "waste." Systems Integration Specialist: Managing the hand-off between AI reviewers and human subject matter experts. Senior/Lead Strategic operational excellence across journals. Chief Integrity & Automation Officer: Balancing publication velocity with rigorous ethical AI standards. Capabilities Defining AdvancementTo progress in this AI-embedded environment, an Improvement Specialist must master three key pillars: Algorithmic Auditing: The ability to audit an AI's decision-making process to ensure it isn't introducing bias into author selections or geographic representation. Data Literacy & Prompt Design: Moving beyond Excel to understand how to structure data for machine learning and how to "program" AI workflows using natural language. Change Management in Virtual Teams: As AI handles the "hard" data, the specialist must focus on the "soft" side—helping editors and researchers trust and collaborate with automated systems.

In the world of scientific publishing, especially when you’re handling a massive volume like 500,000 articles per year, the transition from manual labor to AI-augmented workflows isn't just a convenience; it’s a total transformation of our DNA. This is a fascinating shift that we are living through right now. Here is how we have evolved our hiring criteria and internal operations to meet this new reality. From "Typesetters Factory" to "Quality Guardians" In the past, our typesetting department was a numbers game. We managed thousands of typesetters whose primary value was speed and high-volume manual entry. We hired for "raw processing power"—the ability to turn a manuscript into a PDF as quickly as possible. Today, everything has changed. We have integrated AI that handles more than half of the standard typesetting tasks. We no longer need a "factory" of thousands; instead, we have pivoted to a leaner, elite team of specialists. Here is how we've adjusted our hiring: 1. Shift from Data Entry to Domain Expertise We used to hire for technical proficiency in layout software. Now, we hire for domain knowledge. Since AI handles the "easy" layouts, our humans only touch the complex cases—articles filled with intricate chemical structures, multi-line LaTeX equations, and high-resolution spatial data. What we did: We stopped testing for typing speed. Now, we test for the ability to spot a misplaced radical in a complex equation or a misaligned axis in a scientific chart. 2. Prioritizing "Human-in-the-Loop" Oversight We realized we cannot risk our brand value on fully autonomous AI. A single error in a medical journal or a physics paper can derail research and damage our reputation. What we did: We redefined the typesetter’s role as an Auditor. We hire people who have a "skeptical eye"—those who can verify AI output against the original source material with 100% accuracy. We look for high-level judgment rather than just execution. 3. Hiring for AI Orchestration Our modern typesetters need to be "AI-fluent." They aren't just using tools; they are guiding them. What we did: We started looking for candidates who understand how to "frame" a task for the AI and identify exactly where the automation is likely to fail (like in non-standard symbol rendering). We need people who can troubleshoot the technology, not just work alongside it. Why Quality Over Quantity is Our New Mantra, Factor Old Hiring Criteria (Pre-AI) New Hiring Criteria (Post-AI) Primary Skill Manual Speed & Software Proficiency Scientific Literacy & Technical Auditing Focus Throughput (Pages per hour) Accuracy (Zero-error tolerance) Education General Technical Background Domain-specific (STEM) backgrounds Role Executioner of tasks Overseer of AI-generated workflows The "Risk Mitigation" FilterWe are extremely candid with our new hires: We cannot risk the business with AI alone. Because we are responsible for the integrity of global research, we’ve made "Ethical Responsibility" a core hiring pillar. We hire people who understand that their role is the final firewall between a machine-generated error and the scientific community.

I am from scientific publishing Solution company and to answer this question I would like to talk about one AI solution which we recently implemented in our customer Service Department. Customer service in scientific publishing domain is mainly for resolving Authors queries for their menuscripts and articles. Through customer service we try to resolve author queries which may be sometimes very Complex queries like doing some corrections or type setting related changes in the articles or they could be very simple such as providing them the information about the stage of their article or the timelines or expected date of publishing the article. Earlier author query resolution process was entirely manual but recently we have introduced AI (Vendor) automation there and now AI is handling most of the Author Queries. I would say almost around 80% of other queries are now being resolved by AI and automation. Changing the metrics of success: So while process was manual to handle all the author related queries the common Metrics we used to have (AHT)Average handling time and volume of tickets. Since we have introduced AI and automation over there, We had to look for other metrics. Instead of looking for everything kn twrms of handling time, which is already resuced significantly by AI, now average handling time is not relevant Metrics but instead of that we need to focus on the Accuracy of the response by AI. And since AI is not able to handle all the kind of queries few areas or few category still need the human intervention and for those again being the most critical one now the average handling time is also changed because to handle these queries manually, this will need huge mount of time now. So earlier if we use to have two to three days of Average Handling Time, now for these special cases now the average handling time is more to 5 days and that's how we started tracking the revised matric. Importantly, in scientific publishing industry we really need to be very careful about what kind of content we are approving what kind of details we are sharing with the authors, we really can't relay upon automation and AI 100% so for that human in the loop will always be there and I would not say that that the metric we introduced but yes we are still identifying the percentage of touchless responses so let's say earlier we use to track volume of tickets so now we are tracking how many percent of that volume is handked using AI and thats the another metric we have introduced in the new system when we move from manual to AI system. So to conclude this question I would say that with the introduction of AI the conventional manual work related Matrics have been changed, new AI oriented Matrics are introduced. And for the areas where manual work is still required, the conventional metrics are still there but now target values have been changed.

I am working in a Scientific Research Publishing company. Our reputation and the Brand value is entirely built on the accuracy with which we are producing the scientific articles, this accuracy then results in acquiring the trust of the authors and as well as the audience from the research community who are reading and consuming our content Today's question is very interesting and for that I would like to explain a scenario we experienced in our organisation. Scenario: currently we have deployed an AI based solution to check the content correctness and completeness as well as to check the Plagarism and any kind of text fabrication which is been done by the author These checks are performed using the AI based automation and AI calls that particular manuscript pass or fail. And after that we send it for the actual even best beer review system where they analyse the scientific content, equations and chemical compositions. They perform those relevant steps before publishing the manuscript but the Plagiarism and the content typesetting and corrections are checked by using AI Recently we were using AI, which is supporting the editors in their work faster and efficient. But in few cases, when we obseved that AI failed to work. The AI failed to capture the plagiarized content and as per the process the manuscript was published. After that few authors or researchers they came back to us mentioning the article they are referring is published by us is having copied data, the research is not unique and it looks like it is the fabricated one. Our changed view towards the AI usage : Here in this case we can clearly say that AI fail to perform the required task and in a way we can say it has lied to us. AI mistakenly accepted those articles which we shouldn't have a accepted if checkes manually. Though AI has given incorrect output that doesn't mean AI could be held hundred percent responsible, because we are in scientific publishing domain and we are the get keepers here who are responsible to analyse the data and the content and we are responsible to publish the right content everytime. Hence, in our case here AI is just an enabler which was helping us and our editor to perform their task effectively. This doesn't mean that whatever answers in produce using AI is 100% accurate. Being the scientific content publisher, we cannot put the entire blame on the AI system. We can consider AI as our helper or junior editor assistant which is there to help us. AI if that commits mistake then we should take the responsibility of that mistake, because it's always the journal manager who is there to sign and accept the article at the end of the review. The final review is always lying with the human touch. To conclude this, we can say, AI can give us evidences but the humans can provide the judgements. While considering the AI based solution, we can delegate the task to AI but we can not delegate the responsibility to AI.

Type of industry - Scientific publishing company. The AI project I am going to discuss about is ensuring AI enabled auto type setting process which continues to work as intended. Type setting process in any publishing industry is very critical process because that ensures the correctness and completeness of the publication. Small wreor in the typesetting process will have direct and huge impact on brand image as well as the research results from the manuscripts. But automating such critical process have become the need of the time because the increased volumes of the submissions made it very difficult to manage the type setting process manually. How we ensure AI enabled auto type setting process which works without a drift :- In our company we are using AI to support the auto type setting in which the AI is performing checks and making changes in the content. But the type setting itself is an critical task if an AI fail to identify any symbol or a I just put a single decimal value here and there, this will going to have a huge impact in the entire scientific research, so it becomes very important for us to track and monitor the performance of the AI that we have deployed and for that we have defined taken measures. 1.Defined criterias and metrics - With this define criterias we are continuously tracking this AI performance to identify any kind of signs of the performance drift. We are frequently tracking and monitoring the outcomes of AI by sampling and checking typesetted articles. 2.Keeping human touch for complex tasks - while AI is handling the significant proportion of the type setting work we have kept production editors and type setters actively involved in working on the complex layouts and highly technical content which is not expected or let's say it is impractical to automate using AI. For this specialised work the highly skilled and experience humans are allotted and AI model is trained in away that it identifies such scenarios then that particular article will directly routed to this skilled people. E.g. Article with Latex is a case of comolex typesetting. Hence whenever this scenario occurs, the articles are removed from the automation que and they are sent to the typesetters and content checkers for the manual work. 3.Final stage- We are continuously monitoring the versions of AI models . We monitor the layouts and templates provided and each update is tested against the historical as well as the ongoing articles and manuscripts to make sure that the outcomes are improved and better than the previous versions,like wise we are also continuously training and monitoring the AI models. Key learning - AI is capable of handling such a critical task like Typesetting in scientific research publication. But this involves high risk because every researcher, student, company or any consumer of article demands high accuracy and have zero tolerance towards mistakes. Hence it becomes very important to keep updating and monitoring the AI model so that smallest drift or change could be caltured and resolved promptly.

Talking about DMAIC methodology in the era of AI definitely the execution part got affected by AI but DMAIC will continue to be the backbone of the continuous improvement. With introduction of AI DMAIC project's speed of execution has improved tremendously. In the Scientific Publishing industry majorly we want to improve the production turnaround time where these scientific articles are getting published once they are accepted and in those cases if I need to analyse the DMAIC methodology now and before invention of AI then I could clearly see the difference right from the define till control phase that we now started using AI extensively the problem identification journey of data have become very easy to define the problem and best part is from continuous improvement side we are the responsible people for most of the activities in DMAIC framework and we are guiding and mentoring the project leads so before AI and with AI now I could see that AI have made her life very easy when it becomes mentiring and guiding the project, right from the define phase of the project, project leads are also using the AI to draft the project charter to analyze the data and make the data inferences .In measure phase also AI is helping user to collect the data effectively rather than depending on the MIS to collect the data at the end of month. Right now AI is helping us to generate the data at very micro level and on real time basis. AI also helping us in building the assumptions running some equations and simulations to support these assumption to identify the root causes. Same it goes for analyse and improve phases. And recently most of the solutions right now we are generating through the DMAIC project are related to the AI or some quick automation fixes were also AI is helping. Same goes to the control phase, we can we can set automation based governance over there in less time without so much dependency with which DMAIC methodology and the project would be executed faster and more effectively with reduced dependencies on the IT and tech teams. This has helped to reduce the delays in solution executions as well. Therefore,DMAIC will be the backbone of the improvement journey and AI will act as helping tool to execute the projects faster.