Stronger Together: Leveraging AI to Build Strategic B2B Life Sciences Partnerships

We’re now in the midst of what many have called “The Decade of the Ecosystem”. Partnerships look set to become the most important revenue generation channel – and for many, they already are. Forging strong partnership ecosystems is now a critical priority for life science startups, faced with a tough fundraising climate, and the “triple squeeze” of economic pressure, talent scarcity and disrupted supply.

But before startups can reap the benefits of belonging to these ecosystems, they need to find a way into them, or even build them. The good news is that the same AI capabilities that enable deep competitive intelligence can just as easily be used to identify and pursue high value partnerships. In other words, AI systems like Similari are as good at finding friends, as they are at keeping tabs on foes. For life sciences startups, finding opportunities for partnership or licensing agreements is every bit as critical as knowing what a competitor is doing.

Here, we’re taking a look at exactly why it is so important, especially in the current moment. Then, we’ll outline a use case for startups seeking to strengthen their partnership networks through AI-enabled discovery.   

What’s driving the shift towards partnerships – and why startups should embrace the change

At this year’s J.P Morgan Healthcare Conference, partnerships eclipsed the usual focus on fundraising. Digital health funding in particular is now 48% lower than the 2021 peak. It’s clear that the pandemic-era investment boom is over. For startups, that has spurred a renewed interest in optimizing costs and revising go-to-market strategies.

It’s also driving partnership ecosystems into the spotlight. For a wide range of reasons, this is actually good news. 

Partnerships can remove roadblocks to distribution and sales

Partnerships with established medical device distributors and sales organizations can give startups access to larger customer bases and sales channels. The partnership between Pfizer and BioNTech is a good example of a partnership that amplifies market reach and distribution. In 2020 they announced a partnership to develop, manufacture, and distribute BioNTech’s COVID-19 vaccine. The partnership enabled BioNTech to scale up production quickly, and Pfizer’s distribution capabilities helped to make the vaccine widely available.

Clearing regulatory and compliance hurdles through alliances

Life science startups may partner with companies who know how to navigate complex regulatory environments. They can use these partnerships to achieve compliance and accelerate approvals. This is particularly important for biotech and cannabis startups, who frequently encounter obstacles to FDA approval. Medical device startups, too, can benefit from partnering with more established firms to achieve compliance, and to build recognition and trust. 

Cross-pollinating technical expertise in life sciences partnerships

Partnerships with large companies in the medical device industry can provide startups with access to technical expertise in engineering, manufacturing, and quality control. And sometimes, the flow of expertise is mutual, like GRAIL’s 2021 partnership with GlaxoSmithKline. The partnership combines GRAIL’s expertise in early cancer detection with GSK’s expertise in diagnostics and oncology to develop new products.

“Build-to-buy” partnerships balance the interests of startups and Big Pharma partners 

These startups may also enter build-to-buy partnerships with larger companies. These are partnerships to co-develop and co-commercialize a medical device. The larger company provides funding and resources for the development of the device, while the smaller medical device company provides expertise and intellectual property. In a build-to-buy partnership, the larger company typically has the option to buy the smaller company or its assets once the device is developed and commercialized.

The startup partnerships dilemma: there’s no time (and no money) for traditional partner discovery 

So how should startups proceed with building robust partnerships that coalesce into thriving ecosystems? This is where it gets complicated.

Traditionally, partnership management is a complex business function, with dedicated personnel who seek out and engage prospective partners. Partnerships teams use trade fairs, industry networking events and online partner networks to meet and evaluate prospects. Behind the scenes, they have to invest extensive time and resources in researching each prospect’s business model, reputation and customer base to determine whether they fit their Ideal Partner Profile (IPP). 

Accelerating partnership discovery with a little help from AI

Much of that workflow can (and should) be automated. And while the world of Partner Relationship Management (PRM) is forging ahead with digital transformation, less attention is paid to the initial partner discovery phase, where AI can make a huge impact. Instead of manually searching across a wide range of networking platforms, partner managers can repurpose AI-powered intelligence tools to spot viable partners in any industry. In many cases, they can even rely on the same metrics that R&D teams use to evaluate competitor performance: market reach, customer base and current innovation activities. 

Using Similari, even cash-strapped early phase startups without a partnerships department can tap into a dynamic picture of their space and identify relevant, high quality partnership leads. A new biotech product that you could license, or an upcoming medtech company whose niche expertise could fill non-core competencies that you need – it’s all easily discoverable using Similari’s advanced analytics and proactive monitoring. 

Book a demo to find out why so many life science startups are turning to Similari to help them hone in on partnership and licensing opportunities, and building ecosystems that will weather the storms just over the horizon.  

Innovation intelligence: your go-to glossary

  Demystify the language of innovation intelligence. 


Advanced analytics 

Machine learning, data mining, and statistical analysis can be used to deliver advanced analytics. These make it possible to extract insights and make predictions from complex data. With advanced analytics, businesses can discover hidden patterns and relationships in their data and make more sound decisions as a result. 

Artificial Intelligence (AI) 

This is a broad field that involves creating machines or systems that can perform tasks in a way that usually calls for human intelligence. AI applications can be used for problem-solving, decision-making, and learning. Examples of AI techniques include rule-based systems, expert systems, Natural Language Processing (NLP) and Machine Learning (ML).


Big Data

Big Data refers to extremely large sets of data that are too complex to be analyzed using traditional methods. Big Data can be structured, semi-structured or unstructured and often comes from multiple sources. It requires specialized tools and techniques to process and analyze, such as distributed computing and machine learning.


Biologics are medical products made from living organisms or their products. They include products such as vaccines, gene therapies, and monoclonal antibodies.


Biomarkers are characteristics that can be measured and used as indicators of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention. Biomarkers play an important role in clinical trials by providing a way to measure the efficacy of a drug or treatment being tested. They can be used to track the progression of a disease, monitor the response to a treatment, and predict the outcome of a trial. Biomarkers can be either physiological, such as changes in blood pressure or heart rate, or molecular, such as changes in gene expression or protein levels.


Biotechnology is the application of technology to the study of living organisms, cells and biological systems. It involves the manipulation of biological systems to create new products and therapies.

Build-to-buy Partnership

A collaboration between a biotech or pharmaceutical company and a larger company, in which the biotech company is responsible for developing a product or technology, while the other company provides funding and resources. In exchange, the larger company has the option to purchase or license the product or technology once developed.

In this partnership, the biotech company, known as the “builder”, focuses on the R&D and clinical development of the product, while the “buyer” company provides expertise in regulatory affairs, sales, and marketing. This allows the biotech company to bring the product to market more quickly and efficiently, while the buyer company can secure exclusive rights to the product or technology under its own brand.

Smaller biotech companies can leverage these partnerships to bring their products to market, while the bigger companies can secure exclusive rights to new products and technologies to expand their portfolio.


Claim Scope

Claim scope refers to the scope of protection provided by a patent. It defines the boundaries of the protected invention, and is determined by the language of the claims in the patent. The claim scope determines what others are allowed to do without infringing the patent.

Clinical Development

Once a drug or treatment clears the preclinical testing phase, it can proceed to clinical development. This stage is tightly regulated, because it involves human testing. A drug must go through multiple phases of clinical trials to test its safety and efficacy.

Clinical Trial Registry 

A publicly available database that contains information about ongoing and completed clinical trials. It allows researchers, patients, and the public to search for and find information about clinical trials for a specific disease or condition.

Clinical Trial Protocol

A clinical trial protocol is a document that outlines the plan for a clinical trial, including its objectives, design, population, interventions, outcome measures, and procedures for monitoring and reporting results. The protocol serves as a guide for the conduct of the trial and is used to ensure that the trial is conducted consistently and in compliance with regulations. It is reviewed and approved by institutional review boards or ethics committees before the trial begins.

AI-powered clinical trials intelligence can speed up and enhance the process of creating trial protocols, reducing manual search time and refining trial design to ensure success. Similari surveys tens of thousands of trials to surface key strategic insights.

Cloud BI 

This is a type of business intelligence that uses cloud computing to deliver BI capabilities and data to users over the internet. This allows users to access their data and analytics from anywhere and at any time, eliminating the need for on-premises hardware to store data.


Data mining

Before data can be useful, it needs to be mined for insights. Data mining is the process of extracting useful information from large data sets. This is done through algorithms and statistical techniques that identify patterns, trends, and relationships in data. Data mining can be used for a variety of purposes, such as market research, fraud detection, and customer relationship management.

Data Visualization

Data visualization is the process of representing data in a graphical format, such as charts, graphs, and maps. It allows users to quickly understand and interpret large amounts of information. Data visualization is essential to hypothesis testing and decision making.

Deep learning (DL) 

This is a subset of ML that relies on neural networks with multiple layers. The goal of DL is to make systems capable of identifying patterns and making predictions. DP is used for image recognition, speech recognition and natural language processing.

Digital Health Startup

A digital health startup is a company that uses technology, such as software and telecommunication tools, to improve healthcare delivery and patient outcomes. These startups can be focused on various aspects of healthcare such as remote monitoring, electronic medical records, telemedicine, mHealth (mobile health), and health data analysis.

Drug Development 

The drug development process involves several stages, including drug discovery, preclinical development, clinical development, and regulatory approval. It can take several years and requires the coordination of many different teams, including scientists, doctors, and regulatory agencies.

Drug Discovery 

Drug discovery is the process of identifying and developing new drugs to treat disease. The process begins with the discovery of a target, and the development of a drug. The interaction between the target molecule and the drug is then evaluated and tested, before the candidate drug can proceed through preclinical and clinical development. 


FDA approval process

This is a series of steps that a drug must go through before it can be marketed and sold to the public in the United States. It includes preclinical testing, clinical trials, and regulatory review.



Innovation refers to the process of creating new ideas, products, or technologies. To innovate, companies apply creative solutions to problems to develop new products, services, or business models. Innovation can also involve the improvement of existing products, services, or technologies.

Innovation Intelligence 

Innovation intelligence involves gathering and analyzing data to identify new opportunities and white spaces. Companies invest in innovation intelligence to stay ahead of their competitors by identifying new trends and technologies, understanding customer needs and preferences, and pinpointing the most viable opportunities for innovation.

Integrated BI 

When different business intelligence data sources are combined and analyzed together, this is known as integrated BI. By integrating data sources, decision makers can get a more comprehensive view of their business.

Intellectual Property (IP) 

Intellectual property refers to the legal rights that protect intangible creations of human intellect. These include inventions, literary and artistic works, symbols, names, images, and designs. The most common forms of IP include patents, trademarks, and copyrights.



In the life sciences industry, licensing agreements often involve the transfer of rights to a particular drug or technology, such as a patented drug compound or a proprietary manufacturing process. The licensor company may be a biotech or pharmaceutical company that has developed the product or technology, while the licensee company may be a larger pharmaceutical company that has the resources and expertise to bring the product to market.

There are different types of licenses, such as exclusive licenses, in which the licensee is the only company that has the rights to use the product or technology, and non-exclusive licenses, which allows multiple companies to use the product or technology.


Machine learning (ML) 

A subset of AI that uses algorithms and statistical models to enable systems to improve their performance with experience. Machine Learning trains machines to make predictions or take decisions based on data, without being explicitly programmed.


Natural Language Processing (NLP) 

NLP is a branch of AI that deals with the interaction between computer systems and human language. NLP is used to enhance language translation, recognize speech, summarize texts, and analyze sentiment.


If an invention would be obvious to anyone with ordinary skill in the relevant field, then it does not fulfill the requirement of non-obviousness. If an invention is not novel or would be obvious to someone with ordinary skill in the field, it is not eligible for a patent.


If an invention is novel, it is new and has not been previously disclosed or made available to the public. For any invention to be patented, it must be novel and not obvious to someone with ordinary skill in the field.



To be patentable, an invention must be eligible for a patent. To satisfy that requirement, it must be novel, non-obvious and useful. It must also fall into one of the categories of inventions that are eligible for a patent, such as a process, machine, manufacture, or composition of matter.

Patent Portfolio

A collection of patents and patent applications owned or controlled by an individual or organization. A portfolio can include patents and applications in a variety of technology areas and can be used to protect and monetize inventions. Companies often use a patent portfolio to protect their innovations and gain a competitive advantage in the market.

Patent Search

Patent search is the process of searching for existing patents and patent applications to determine the novelty and non-obviousness of an invention. Patent search can be conducted to identify potential patent infringement, to assess the potential for licensing a technology, or to identify potential partners for a joint venture.

As the volume of patent applications continues to mushroom, patent searches that rely on manual or reactive methods cannot deliver the timeous, accurate insights that IP professionals need to navigate around patentability issues. Similari surveys over 150 million patents, automatically generating critical business insights.

Patent Landscape

A patent landscape is a visual representation of a particular field of technology, showing the number of patents and patent applications, the types of claims, the assignees, and other information. Patent landscapes can be used to identify trends, competitive activity, and opportunities in a particular field.

Patent Map

This is a visual representation of a patent portfolio, showing the technology areas covered by the portfolio, the types of claims, the assignees, and other information. Patent maps can be used to identify gaps in a portfolio, to identify potential licensing opportunities, or to identify opportunities for partnerships.

Patent Thicket

A patent thicket refers to a situation where many patents or patent applications cover a particular field of technology, making it difficult for new companies to enter the market or for existing companies to innovate without fear of infringement. 

Phase I Trials 

This is the first step in testing a new drug or treatment in humans. Phase I trials involve a small number of healthy volunteers and are used to determine the safety and investigate the pharmacology of the drug.

Phase II trials 

These trials  involve a larger number of patients with the disease or condition the drug is intended to treat. The goal is to determine the drug’s efficacy and identify potential side effects.

Phase III Trials 

These involve even larger groups of patients than Phase II trials. They help to confirm the drug’s efficacy, monitor side effects, and compare the drug to treatments that already exist on the market.

Phase IV Trials 

After a drug has been approved and marketed, they are subjected to Phase IV Trials. The goal of these trials is to gather additional information about the drug’s safety, efficacy, and optimal use.

Preclinical Development

The stage of drug development that takes place before human testing. It includes laboratory and animal testing of a drug candidate to determine its safety, efficacy, and pharmacokinetics.

Prior Art

Any information that has been publicly disclosed or made available before a patent application is filed is known as prior art. This can include published patents, publications, and other forms of public disclosure. Prior art is used to determine if an invention is novel and non-obvious.


Real-time BI 

This is a type of business intelligence that provides up-to-the-minute data and analytics to users. This allows businesses to make decisions based on the most current data, and to quickly respond to changing conditions.

Redundant Trials

A redundant clinical trial is a study that is conducted when the results of previous trials on the same topic are already available and the new study is unlikely to provide any new or significant information. These trials may be deemed unnecessary or a waste of resources because they do not add to the existing body of knowledge. Redundant clinical trials may also be referred to as “duplicative” or “unnecessary” trials. 

AI-powered intelligence solutions like Similari help to reduce the risk of engaging in redundant trials.

Research and Development (R&D) 

R&D professionals specialize in conceptualizing new ideas, products, or technologies. The goal of R&D is to create new solutions, test them, and get them to market. The research and development process can be greatly enhanced through the adoption of AI-powered market intelligence. This enables R&D professionals to stay abreast of relevant market trends and opportunities, and channel resources towards the most viable projects.


Semantic search

Semantic search uses natural language processing and knowledge graphs to understand the meaning of search queries and return relevant results. It allows users to perform natural language queries and get more accurate and relevant results.


Tech Partnership

These are strategic alliances between two or more companies, organizations, or individuals, in which they collaborate to achieve a shared goal or objective, typically related to technology. Tech partnerships can take many forms, such as joint ventures, licensing agreements, and collaborations to develop new products or services.

Tech partnerships can bring together companies with complementary skills, resources, and expertise to create new products, services, or technologies that would be difficult or impossible for either party to develop alone. They can also help companies to enter new markets, access new customers, or reduce costs.

Text mining 

The process of extracting relevant information from unstructured text data is known as text mining. This relies on AI techniques such as NLP and ML. Text mining is useful for a wide range of applications such as sentiment analysis, topic modeling, and named entity recognition. Text mining enables organizations to glean insights from large amounts of unstructured data.


White Space

These are areas where there is an opportunity to create new products, services, or technologies that can fill a gap in the market. Finding white spaces is a priority for R&D and innovation, because they may offer a chance to gain a competitive advantage or create new revenue streams.

2022 in Review: a Year of Resilience and Adaptation

2022 is now firmly in the rearview mirror, but many of its most important trends are still playing out. Here, we’re picking out of a few of the most significant highlights and lowlights, and discussing what they could mean going forward.

Necessity: the mother of invention? 

One of the prevailing themes of 2022 was “doing more, with less”. From the very beginning, business faced historic talent shortages, exacerbated by “The Big Quit”. Geopolitical destabilization made a bad situation worse, disrupting access to Ukrainian tech talent. All in all, 2022 was a rough year for hiring, and it’s still looking very much an employee’s market in Q1 of 2023.

But it’s not all doom and gloom. Valuable lessons were learned, particularly when it comes to optimizing for innovation. Companies have turned to AI solutions to alleviate the pressure of R&D talent shortages and adapt to a leaner, more agile way of working. Those who have adapted in this way have begun 2023 on a stronger footing, and are better equipped to keep innovating in an uncertain climate.

Turning over a new leaf: sustainability, prioritized across the board

While it has been – and still is – hotly contested, there were major developments in the ESG space last year. 2022’s proxy season saw a record number of ESG proposals, primarily focused on environmental issues, but also social causes.

This renewed focus on sustainability goals was also reflected in M&A activity. IBM’s acquisition of Envizi signaled a commitment to move towards more sustainable business practices. Envizi’s software enables companies to coordinate and consolidate the hundreds of data types needed to analyze and manage their ESG goals. By integrating Envizi with IBM’s asset management, supply chain and environmental intelligence software, companies will now be able to automate much of this workflow and scale their efforts.

The view from life sciences and pharma: M&A down, partnerships up

While M&A deals in life sciences dropped off, licensing partnerships saw a small but significant increase, from $178 to $179 billion, an early harbinger of what is to come in 2023. As funding dries up, and life sciences companies face uncertain economic conditions, collaboration and ecosystem building have become critical priorities.. 

Through 2022, buyers also demonstrated a growing appetite for licensing agreements that spread the risk, rather than outright acquisition. This cautious approach was exemplified by Roche’s strategic collaboration with Poseida Therapeutics, combining Poseida’s novel cell therapy techniques with Roche’s development and commercialization capabilities. 2022 set the tone that 2023 is following: partnership and licensing agreements are key business development priorities that call for next-generation solutions. 

Life sciences companies have yet to make DnA a part of their DNA

But when it comes to adopting those solutions, the life sciences lagged behind other industries throughout 2022. Mckinsey notes that just over half of Digital and Analytics (DnA) leaders in pharmaceutical companies report having implemented digital applications at scale. The situation was even worse for MedTech. By their estimate, full adoption of digital solutions could bring the industry up to $190 billion in gains through the life sciences value chain, from streamlining clinical trials to enhancing drug discovery.

Generative AI and the future of BI

No review of 2022 would be complete without a nod to the rise of generative AI. 2022 was a bumper year for AI, across the board. But Openai’s GPT-3 took center stage towards the end of the year. The potential use cases for business intelligence are still unfolding, but it’s already being used for a variety of applications:

  • Data analysis: GPT-3 can help analysts by quickly summarizing large amounts of data, generating reports, and performing exploratory data analysis.
  • Predictive analytics: GPT-3 can be used to generate forecasts and predictions based on historical data and business trends.

Similari knew all of this before we did (and much more).

From a human point of view, 2022 felt like a whirlwind – even one and a half months later. But for Similari, it’s all been processed neatly and accurately into readily accessible data – data that business leaders can tap into for reliable and actionable insights. 

To find out how Similari helps businesses find certainty in uncertain times, schedule a demo with our team. We’ll guide you through Similari’s key features and show you how intelligent market surveillance and proactive analytics equip companies to tackle the future with confidence. 

Innovation Through a Recession: No Time to Slow Down

When an economic downturn hits, one thing is certain: there will be winners and there will be losers. The winners are those who are able to find the silver lining that others miss. But is that silver lining really there? 

Here, we’re going to explore what a gloomy 2023 economic outlook could mean for innovation, and how businesses can prepare to not only survive, but thrive, through adverse conditions, and in the postcrisis phase that will inevitably follow. 

Finding silver linings: recessions create innovation opportunities 

In short: it may be counter-intuitive, but a time of crisis is not the time to slow down on innovation. We’ll be considering recent research, as well as notable examples from history to make the case for forging ahead with new initiatives, even (maybe especially) against economic headwinds.

Cost-cutting: an opportunity for collaborative spring cleaning

In times of stress, the C suite starts looking for costs to cut. That’s understandable, and it’s usually regarded as a necessary evil. But if it’s done right, it can be turned into a unique opportunity, because it can force vital conversations and alignments that may not even come up when the going is good.

For example, consider an innovation team with a large tech stack built up over a long period of growth. Some of those may be crucial to the future innovation projects, others are probably contributing to bloat. This is an opportunity for the team and CSO to take a hard look at their processes, identify which of their tools are indispensable for innovation, and trim the fat without losing the muscle.

Capitalizing on industry-wide shakeups through innovation

We only need to look back a few short years to find an example of companies innovating boldly into economic and social chaos – and winning as a result. At the beginning of the COVID-19 pandemic, General Motors partnered with a ventilator company to produce ventilators to address predicted shortfalls. It was a risky move, forcing GM into a completely new field, and amidst lockdowns, layoffs and turmoil. 3 years later, and GM has been able to repurpose the expertise they acquired to develop the Hummer EV.

The takeaway here is that businesses who tackle uncertain times head-on stand to gain in the long term. Those who wait for the moment to pass, and take an overly conservative approach to cost-cutting, may end up losing more than they gain. 

Out with the old, in with the new: being ready to meet radical shifts in customer demands

Economic disruption forces a reevaluation of everyone’s business-as-usual. Even companies that had product-market fit on their side in the past may find themselves in need of new answers to new questions. When business and customer needs shift suddenly and substantially, this creates new pain points crying out for solutions. 

Becoming recession-proof by innovating: lessons from recent history

According to Mckinsey & Co, companies who innovated through the 2009 financial crisis, the so-called “through-crisis innovators”, outperformed their competitors by 10% through the crisis itself. They then went on to outperform the market by upwards of 30% for the next 3 to 5 years after the crisis. 

Almost a decade before that, Apple famously leveraged innovation to pull through the 2001 recession and cement its place as a global household brand. And they did it by entering the music market with iTunes and the iPod. The risks were clear: they were innovating during a downturn, and they were innovating directly into a crowded category, with expensive products. The key to their success was the innovation that stood behind the strategy.

A note of caution: this time round, things are different

So far, we’ve painted an optimistic picture of the value of innovation in a recession. But it’s worth considering the unique mix of factors that will make 2023 especially challenging for businesses. 

Geopolitical problems, supply chain disruptions and inflation – or perhaps even worse, 1970’s-style stagflation – make for a particularly unwelcome blend. And ongoing tech talent shortages are certainly not helping. As we’ve pointed out before, these shortages are making it increasingly difficult for even large companies to keep throwing personnel at R&D and innovation challenges. 

What’s needed is a way for businesses to ensure that their innovation initiatives are not among the “sinking ships” that proliferate during a downturn. According to Bain & Company, we can expect to see 89% more of these in a recession. According to the same source, though, “rising stars” also increase by 47% – and that’s the category every business leader, CSO and R&D lead wants to be part of.

Innovating in a recession with purpose-built AI solutions

To make it through, businesses will need to act in the ways we’ve discussed here: identify changes in customer expectations, look for new gaps (across industries, even), and cut costs surgically, to avoid dampening innovation. And through it all, they’ll need to guard against the risk of wasting precious, limited resources on new initiatives that end up on the scrap heap. 

Next-generation AI solutions are already enabling businesses to take these bold steps, and successfully identify innovations opportunities and spot critical threats before they even materialize. To find out more about how AI can boost R&D and innovation with comprehensive, intelligent market surveillance, schedule a demo with the Similari team.