https://www.analyticssteps.com/blogs/ai-product-development-role-and-benefits
What is Product Development?
When a product is launched in the market, the R&D sector of the brand keeps the research on looking
for further improvement. It is the collection of all stages that are involved in the journey of product
from concept or idea to market launch and further.
It is hereby clear that product development is part of the entire product’s journey.
Standard stages involved in Product Development
Though, for different products and different brands the stages of product development are quite different
depending on its needs and requirements. But the common strategies involved in the process are as
follows:
1. Identify the market need
Products are launched to fulfill customer needs and market requirements. To keep your product updated
and improved, it is essential for the brands to identify market needs. This can be achieved by
maintaining healthy conversations with potential customers, surveys and other user research activities.
2. Quantifying the opportunity
Organizations need to find how much and how many people are willing to pay for their solutions.
3. Conceptualize the product
The designing team needs to put efforts and apply their creativity to devise how their product might
serve the different needs of the customers.
4. Validate the solution
After setting and conceptualizing the product. It is essential to validate the products. An early test is
always the best method to check if the particular product is worthy to pursue or not. If not, the owner
can reject the idea at base level avoiding heavy expenses.
5. Product roadmap
With a legitimate product concept in mind, the team should build out a product roadmap. This roadmap
identifies the themes and goals that are needed to solve and develop the most significant points related
to the product.
6. Develop the MVP
MVP stands for a minimum viable product; it is the initial version of the product. It just contains enough
functionalities to be used by customers.
7. Release the MVP beforehand in market
Do the practical test of your product by launching MVP beforehand in the market. These experiments
can gauge interest, prioritize marketing channels and test the sensitivity and packaging. The release of
MVP can bring ideas, complaints, and suggestions into the prioritization process and populate the
product backlog.
8. Iteration process
Continuous iteration based on user feedback and strategic goals is an essential strategy. Feedbacks given
by the customers are essential for enhancements, expansions, and changes related to product.
Mentioned above are the strategies involved in the improvement process. Though product improvement
is a never-ending process, over time only the product roadmap will evolve from time to time setting up
new goals and observations.
WHAT IS THE ROLE OF AI IN PRODUCT DEVELOPMENT?
With the advent of AI and ML algorithms, the product designing, and product improvement centers of
the enterprises have been facilitated. The businesses are employing several facets of AI and ML to
develop and launch new products as well as to improve the existing ones.
Unlike traditional data gathering technologies, AI and ML can provide best possible options to the
businesses. AI at the same time helps in gaining competitive advantages and leads to faster product
development.
In enterprises, startups and firm businesses, AI helps in product development in following ways:
1. Ensure company’s peers have a successful product integration.
2. The product development teams can utilize AI and ML algorithms in the development cycle.
3. Help in achieving greater economies of scale, efficiency and speed gains across the entire process of
product development.
4. Assist the three core areas of product development, i.e., digital prototyping, PLM and product profile
management.
5. Enable the organizations to find their optimal configuration out of the array of possible combinations.
6.
Give the organizations insights over their new product development on a large scale.
7. Assist with neural networks integration.
8. Perform a deep analysis of price elasticity and sensitivity.
9. Provide deep analysis to reduce energy costs and negative price variances of the product.
10. Help in cost management, strategic sourcing and procurement for the new product development process.
11. Provide a variety of customization options, thus, help in creating tune propensity models.
12. Empower next generation thinking and frameworks design.
13. Develop systems to reduce time to showcase while improving product quality.
14. Enhance product adaptability and catering to the needs of the customer.
15. Assist generative designs.
16. Help in identifying product design constraints.
17. Overall assistance in developing an optimized product.
Benefits of AI in Product Development
Benefits of AI in Product Development
Now let us study the benefits and advantages of AI and machine learning in product development. The
main list includes the following benefits:
1. Generative Design
Generative design involves a program that generates different outputs to meet the specified criteria of
the organizations. To generate a generative design designers and engineers input design goals and
parameters.
These include requirements such as materials, manufacturing methods, cost constraints and alternatives
designs. Later the software employs ML algorithms to learn from each iteration and understand the
concept. The AI and ML then generate a variety of options. It provides an augmentation to human work.
2. Assembly line optimization
AI helps in product development by optimizing assembly lines. Let us consider an example, if any
equipment operator shows signs of fatigue, supervisor and technicians get notifications. Thus, the
system automatically activates contingency plans and other reorganization activities.
3. Build virtual copy
AI helps in building a virtual copy of a physical production system. This includes both a machinery
asset or entire machinery system. These digital copies help in real-time diagnose, evaluation of the
process, prediction, tic, and visualization of product performance.
Data science engineers utilize AI and supervised ML algorithms to assist these digital copies. The entire
process takes place by processing historical history and unlabeled data gathered from real-time
monitoring. In short words, virtual models help in optimizing production scheduling, quality
improvement and maintenance.
4. Predictive maintenance
Forecast and prediction is the most basic advantage of AI. AI technologies help in identifying potential
downtime and accidents by analyzing real-time data.
AI helps the technicians and supervisors to forecast a failure before it actually occurs. Thus, AI can
improve the development efficiency by reducing the cost of machine failure.
5. Inventory management
ML and AI algorithms promote planning activities. AI tools provide more accurate results than any
traditional method. The better inventory control helps in reducing critical scenarios like cash-in-stock
and out-of-stock.
Inventory management is sometimes referred to as stock management. It is the process of ordering,
purchasing, selling and maintaining of company's inventory.
6. Quality assurance
Quality assurance is the end process set up to assure the quality of products and services. It is basically
the test to check the standards of the product delivered by the company.
In earlier times, quality assurance has been managed by highly skilled professionals and has been a
manual job since ages. Now, AI and image processing algorithms can technically validate if the item is
developed and produced in the right way or not. This real-time and automatic check has eased the
process of product development.
7. Quick decision making
Decision making is defined as the process of choosing the right combination among different existing
ones. AI and ML algorithms help in taking the immediate decision related to any product than traditional
methods.
8. Process optimization
Process optimization is basically done to set up sustainability of a product in the market. AI software
helps the organizations to optimize the production and development process at every level to achieve
sustainable production. It helps in identifying the bottlenecks in the operations and eliminating the
same.
CASE STUDY
1) In early 2020, Exscientia announced the first-ever AI-designed drug molecule to enter
human clinical trials.
Exscientia, a leading artificial intelligence (AI) driven pharmatech company, today announced the first
AI-designed molecule for immuno-oncology to enter human clinical trials. The A2a receptor antagonist,
which is in development for adult patients with advanced solid tumours, was co-invented and developed
through a Joint Venture between Exscientia and Evotec, including application of Exscientia’s next
generation 3-D evolutionary AI-design platform as part of Centaur Chemist®.
The drug candidate has potential for best-in-class characteristics, with high selectivity for the target
receptor, bringing together potential benefits of reduced systemic sides effects as well as minimal brain
exposure to avoid undesired psychological side effects. Preclinical data related to this project will be
presented at the American Association for Cancer Research (AACR) annual meeting to be held 9-14
April, 2021.
With this announcement, the company’s AI technologies and drug-hunting expertise are now
responsible for the first two AI-Designed drugs to enter Phase I testing, following on from Exscientia’s
previous announcement in 2020 (1). https://investors.exscientia.ai/press-releases/press-releasedetails/2021/exscientia-announces-first-ai-designed-immuno-oncology-drug-to-enter-clinicaltrials/Default.aspx
2) In July 2021, an AI system by DeepMind called AlphaFold predicted the protein structures
for 330,000 proteins, including all 20,000 proteins in the human genome. The AlphaFold
Protein Structure Database has since expanded to include over 200 million proteins, covering
nearly all cataloged proteins known to science- AlphaFold is our AI system that predicts a
protein’s 3D structure from its amino acid sequence. In CASP14, AlphaFold was the topranked protein structure prediction method by a large margin, producing predictions with
high
accuracy,
many
of
which
are
competitive
with
experimentally-determined
measurements.
We’ve partnered with Europe’s flagship laboratory for life sciences - EMBL’s European
Bioinformatics Institute (EMBL-EBI) - to create the AlphaFold Protein Structure Database to make
these predictions freely available to the scientific community.
In July 2021 we released predictions for 21 model organisms (~330k predictions), covering all of
the 20,000 proteins in the human proteome. In December 2021 we added 440k new structures
from Swiss-Prot, including key proteins of interest, manually curated and annotated by the
community. Finally, in January 2022 we added ~190k new structures, for 17 organisms for
neglected diseases and 10 antimicrobial resistant bacteria, based on priority lists from WHO. This
brought the total of available structures to nearly 1 million. https://www.deepmind.com/opensource/alphafold-protein-structure-database
3)
In January 2023, AbSci became the first entity “to create and validate de novo antibodies in
silico” using generative AI.- Absci Corporation (Nasdaq: ABSI), a generative AI drug creation
company, today announced the ability to create and validate de novo antibodies in silico (via a
computer) with the use of zero-shot generative AI — a major milestone for the biotechnology industry.
The ability to create de novo therapeutic antibodies in silico could potentially reduce the time it takes
to get new drug leads into the clinic from as much as six years down to just 18-24 months while also
increasing their probability of success in the clinic. This new advancement is a major industry step
change, unlocking the potential to deliver breakthrough therapeutics at the click of a button, for every
patient.
About Absci- Absci is a generative AI drug creation company that combines AI with scalable wet lab
technologies to create better biologics for patients, faster.
Our Integrated Drug Creation™ platform unlocks the potential to accelerate time to clinic and increase
the probability of success by simultaneously optimizing multiple drug characteristics important to both
development and therapeutic benefit. With the data to train, the AI to create, and the wet lab to validate,
we can screen billions of cells per week, allowing us to go from AI-designed antibodies to wet labvalidated candidates in as little as six weeks. Our vision is to deliver breakthrough therapeutics at the
click of a button, for everyone. https://investors.absci.com/news-releases/news-release-details/abscifirst-create-and-validate-de-novo-antibodies-zero-shot
4) In February 2023, the FDA granted its first Orphan Drug Designation to a drug discovered and
designed using AI; Insilico Medicine plans to begin a global Phase II trial for the drug “early”
this year.- The U.S. Food and Drug Administration granted Insilico Medicine Orphan Drug
Designation to its experimental idiopathic pulmonary fibrosis therapy INS018_055, a drug in which
artificial intelligence was used to both identify a novel target and to generate a novel small molecule
candidate against it.
Idiopathic pulmonary fibrosis (IPF) is a chronic lung disease that causes progressive and irreversible
decline in lung function and represents a significant unmet medical need worldwide. As the disease
progresses, damage to the patient’s lungs increases and respiratory function is compromised, often with
severe consequences. bINS018_055 is a potentially first-in-class small molecule inhibitor discovered
by Insilico’s generative AI platform, Pharma.AI, for the treatment of IPF.
INS018_055 is the first wholly owned program of the company in which AI was used both to identify
a novel target and to generate novel small molecules. The enrollment for the INS018_055 phase 1
clinical trial was initiated in New Zealand in February 2022 and in China in May 2022. Both of the
clinical trials were completed in 2022. Top-line data from the phase 1 clinical trial indicated that the
drug candidate has a favorable safety, tolerability, and pharmacokinetic profile. Insilico plans to initiate
a global multicenter phase 2 clinical trial in early 2023.
The FDA’s Orphan Drug Designation program supports the development and evaluation of drugs that
address rare diseases which affect fewer than 200,000 people in the United States. Receiving orphan
drug designation from the FDA facilitates the subsequent development and commercialization of
INS018_055 that comes with the designation, including eligibility for federal grants, tax credits for
qualified clinical trials, prescription drug user fee exemptions, and a seven-year marketing exclusivity
period upon FDA approval.
AI TO DEVELOP NEW FORMULATIONS
When developing a new formulation, AI-based optimization algorithms such as Bayesian optimization
can significantly speed up the process.
Here are the typical steps of an AI-based optimization project:
1. You perform a small number of initial experiments (as few as 2),
2. A machine learning model is built using the experiment results. This model is usually a
Gaussian process, which is able to provide an uncertainty with each of its predictions.
3. Using the model built at step 2, the algorithm will choose the next experiment(s) to run by
combining the exploitation strategy (trusting the model and running experiments that are
predicted to give good results) and the exploration strategy (running experiments for which the
model uncertainty is high, in order to reduce that uncertainty and improve the model quality).
4. You run the experiment(s) proposed by the algorithm.
5. You add the result of that new experiment to the dataset.
6. You go back to step 2.
This optimization algorithm stops when you obtain a formulation that satistfies your objectives or when
you have spent your experiments budget.
As you can imagine, at each iteration of this algorithm, the quality of the machine learning model
improves (since you are adding data that the algorithm requested) and the algorithm becomes more and
more powerful. We can say that you are capitalizing on your experiment results.
AI FOR REFORMULATION
Now, let's say you want to replace one or several raw materials in an existing formulation. The reason
for that could be: to change your supplier, to replace the ingredient with a bio-based alternative, to
replace it with a cheaper ingredient, etc. Of course, you want to obtain a new formulation that has the
same performances and stability (or better) than the original formulation.
Starting with your existing formulation and the formulation data that you have acquired while
developing that existing formulation, you can use AI-based optimization algorithms (e.g. Bayesian
optimization) to reformulate your product in a minimum number of experiments. You will use the same
process as if you were developing a new formulation from scratch (see "AI to develop new
formulations"), but starting with a larger dataset (all the formulations you have tested while developing
the existing formulation). The mission of the optimization algorithm will be to adapt the initial
formulation to the new ingredient(s).
HOW MUCH FORMULATION DATA IS IT NECESSARY TO HAVE IN ORDER TO USE AI?
It depends on which approach you are using:

If you are using the "traditional" machine learning approach (accumulate data, build a model,
use the model to make predictions), we recommmend to have at least five times (and if possible,
more) as many experiments as you have composition variables and process variables.

If you are using Bayesian optimization (or a similar approach), you can start with as few as 2
experiments, and use the optimization algorithm to design the next experiments. Overall, you
will need to perform fewer experiments than with the traditional machine learning approach,
because the Bayesian optimization algorithm requests to perform the experiments that are most
useful to improve the quality of the machine learning model.
How to be successful at using AI to develop formulations
During our collaborations with formulation scientists, we give them plenty of advice. Here, we share
with you a few tips that will help you be successful in applying AI techniques to your formulation
projects.
Tip 1: Use modern optimization methods that exploit machine learning (also called sequential
learning or even sometimes active learning). This ensures that the data you acquire while doing
experiments is the most useful for the machine learning algorithms you use.
Tip 2: Don't wait to accumulate data before starting using machine learning (as a direct
consequence of Tip 1). Start using machine learning-based optimization as soon as possible in your
project, and let the optimization algorithm suggest you experiments that are needed to improve the
machine learning model quality.
Tip 3: Use a modern system to store your formulation data. Paper notebooks and Excel are
insufficient for long-term capitalization of experiment results. Using an electronic lab notebook (ELN)
or a database system to store and centralize formulation data ensures that you will never waste or lose
valuable results. It also means that when you want to retrieve historical data to apply data science or
machine learning techniques, you can retrieve that data with minimal effort.
Tip 4: Favor data diversity. For example, if you have numerical variables (e.g. representing the
amounts of each ingredient in your formula), acquiring data with diverse values of these variables (and
not only two different values for each composition variable) will produce more robust machine learning
models and increase your chances of success. Machine learning-based optimization can help you
increase the diversity of your dataset by using a strategy called "exploration".
Tip 5: Integrate as much domain knowledge as possible in the optimization or model building
process. Domain knowledge is involved in the choice of ingredients and process variables, in the choice
of constraints applied to the optimization problem, in the choice of the optimization objectives (and the
way of combining them), etc. We believe that it is the combination of machine learning and domain
knowledge that will bring the most acceleration of R&D projects in the next years.
Tip
6:
Collaborate
with
companies
which
are
specialized
in
AI applied
to
formulations/chemistry. When the company you are collaborating with is able to understand your
language (because they have a chemistry/formulation background), they can propose the most adapted
AI solutions to your formulation problems. Understanding the data and how it was generated is key to
be successful in applying AI techniques.
https://chemintelligence.com/ai-forformulation#:~:text=AI%20to%20test%20your%20ideas,learning%20than%20AI%2Dbased%20opti
mization
AI-Powered Tools for Literature Review
SEMANTIC SCHOLAR

SCIENTIFIC LITERATURE SEARCH ENGINE.- Example - nursing student mental health
"scoping review"
The 4000+ results can be sorted by Fields of Study, Date Range, Author, Journals &
Conferences

Save the papers in your Library folder. The Research Feeds will recommend similar papers
based on the items saved.

Semantic Reader- It "uses artificial intelligence to understand a document’s structure and
merge it with the Semantic Scholar’s academic corpus, providing detailed information in
context via tooltips and other overlays."

Skim Papers Faster
"Find key points of this paper using automatically highlighted overlays. Available in beta on
limited papers for desktop devices only."
ELICIT.ORG

FINDING SEED ARTICLES AND CONCEPTS-Elicit is a research assistant using
language models like GPT-3 to automate parts of researchers’ workflows. Currently, the
main workflow in Elicit is Literature Review. If you ask a question, Elicit will show relevant
papers and summaries of key information about those papers in an easy-to-use table.
CONSENSUS.APP

LITERATURE EXTRACTOR & AGGREGATOR

"We are a search engine that is designed to accept research questions, find relevant answers
within research papers, and synthesize the results using the same language model
technology."
ChatGPT

CONVERSATIONAL LARGE LANGUAGE MODEL 3.5 by OpenAI

Potential applications for teaching, learning and doing literature reviews.

The knowledge cutoff for the ChatGPT 3.5 is September 2021. Currently, there are
no available plugins for connecting ChatGPT 3.5 directly with Google Scholar. However,
individuals may consider alternatives such as Consensus, Semantic Scholar or Elicit for
doing real-time literature review.
Bing AI

CONVERSATIONAL LLM 4.0 + SEARCH ENGINE

It builds on GPT4 technology and connects to the Internet. Better use it with Microsoft Edge
browser.
AI-enabled risk detection
•
Quartic.ai and Sparta Systems are collaborating on an AI-enabled risk detection software for
pharmaceutical manufacturing operations, intended to increase both product quality and
process efficiency. Their goal is to come up with solutions that can help detect risks early in
the pharma manufacturing process, reducing product quality impact and enabling near-realtime product release.
•
risk detection is an integral part of the pharma manufacturing process. According to the
principles of Quality by Design (the US Food and Drug Administration’s quality standards),
risk needs to be pushed as far away from the patient as possible. This means protecting them
from any flaws in the manufacturing process or raw material. And that means finding those
flaws as early as you can.
•
Unfortunately, this can be a labour-intensive process, requiring a number of complicated
control systems in combination with manual reporting and deviations
•
It was for this reason that Quartic.ai decided to team up with Sparta Systems on a new AI-based
risk detection tool. The software will identify risks early in the pharma manufacturing process
and give users quality insights in real time.
•
“Fundamentally what you’re doing is you’re moving from quality control to quality assurance,”
says Anand. “That means a greater confidence that you’re getting it right the first time.”
How the software will work
Quartic.ai, an industrial AI software company, was set up in 2017. Its flagship product, the Quartic
platform, helps engineers deploy their own AI solutions without any training in data science.
Sparta Systems, meanwhile, is a leading provider of quality management system (QMS) software for
the life sciences industry. Together, the two companies are creating a system that collects data ‘on the
shop floor’, learns from this data to detect emerging deviations and abnormal process behavior
(Quartic) and creates proactive quality assurance (Sparta).
Users will continue to work with Sparta’s TrackWise digital platform, which has modern, digital
QMS workflows in place. This platform will communicate with Quartic’s IIoT and AI platform,
which will be trained to spot anomalous events.
Once the algorithm is fully ‘trained’ and the solution is deployed commercially, the AI will be able to
flag up any deviations from the norm. If performance drift is detected, the digital QMS workflows
will alert users with corrective instructions and measure the impact of corrective action with evidence
from the manufacturing floor.
“We’ll take that real-time data from the unit operations, and combine it with the previous deviations
that have been captured by the QMS platform – that’s how the two capabilities will come together for
proactive quality assurance,” says Anand.
CRITICAL QUALITY ATTRIBUTES

Pharmaceutical development was focused on those CQAs that could be impacted by a realistic
change to the drug product formulation or manufacturing process. The proposed generic
Daptomycin for Injection 500 mg/vial referred as test product in this report has CQAs such as
Description, Description of reconstituted solution, pH of the reconstituted solution, Assay,
Related substances, Reconstitution time, Particulate matter, Uniformity of dosage unit, Water
content, BET, Sterility, Color of solution, Clarity of solution, Container content & Leak test.

The manufacturing process was developed in order to minimize degradation of Daptomycin in
solution and lyophilized form by controlling/reducing its degradation in bulk solution. Upon
completion of lyophilization cycle, the vacuum is neutralized with sterile filtered nitrogen thus
controlling oxygen content in drug product in the vial head space.

The product is a lyophilized product and is sensitive to heat. Terminal sterilization using moist
heat will degrade the product and therefore is not feasible. So, sterile filtration followed by
aseptic processing is employed as the method of choice for sterilization.
QUALITY TARGET PRODUCT PROFILE
Dosage form, Dosage strength, Route of administration, Dosage and administration, In-Use Storage
condition, Reconstitution fluids, Description/Appearance, Identification, Assay, pH, Related substances,
Reconstitution time, Water content, Particulate matter/Particulate contamination, Uniformity of dosage units
(By weight variation), Sterility, Bacterial endotoxins, Color and clarity of the reconstituted solution,
Completeness of solution, Residual solvents, Extractable leachable, Container closure system, Elemental
Impurities, Nitrosamines impurities
CQAs of Daptomycin
Description, Description of reconstituted solution, pH of reconstituted solution, Reconstitution time, Particulate
matter, Leak testing (Container Closure Integrity test) , Container Content (extractable volume), Identification,
Assay, Uniformity of dosage units (by weight variation or by mass variation), Water content, Related
substances, Residual solvents, Elemental Impurity, Nitrosamine impurities, Extractable & Leachable