After years of development and billions in R&D spend, the vast majority of drugs never make it to market. Failure is a near certainty, and there’s no shortage of dangerous pitfalls along the way. 

Here are three costly missteps that can derail the process of bridging a new drug to the market.

Drug development misstep 1: Blind spots in strategic planning

By some estimates, 10% of drug development failures result from poor strategic planning. This can include a wide range of problems such as sub-par clinical studies, changes in therapeutic focus or company mergers that make prior research untenable. Given that it generally takes an average of 14 years to bring a drug to market, it’s crucial that companies invest enough time in strategically planning the drug development process, from target identification through to (hopefully) approval. 

Is the projected ROI realistic? 

Investors care about the return on the investment, and they need to see facts and figures to back it up. This is especially true in a tight funding environment. But it is a major mistake to over promise in order to secure investment. That leads to overly ambitious timelines, and underestimated budgets. 

Setting manageable expectations from the outset is key to maintaining healthy investor relationships and suitable budget guardrails. 

Has product market fit been thoroughly assessed?

Before proceeding to the first step in drug discovery, fundamental questions about the project’s viability need to be answered. Companies should only pursue products that are likely to match a medical need in the market. 

Physicians and patients are the people to consider at this stage, before investors even enter the picture. Ideally, the product should be something that will have a significant impact on patient outcomes, with minimal adverse effects. It’s also worth considering whether the proposed product is a “me-too” drug – a drug that closely resembles an existing product – as these often have lower return on investment. 

How likely is the product to qualify for reimbursement after approval?

Reimbursement rates are important for long term profitability. If federal or private insurers are unlikely to reimburse for the product, it may not be worth pursuing. Insurance coverage is often overlooked in the early stages of the drug development process, which is a strategic oversight. To increase the likelihood that they will be reimbursed, companies should build a value proposition that demonstrates the clinical and economic benefits of the drug, before they embark on the discovery process. 

Drug development misstep 2: Not having a clear compliance and approval strategy

Once a drug has made it through clinical research, it needs to clear an FDA review (or comparable authority outside the US). To pass, drug developers must submit extensive documentation on safety and efficacy. FDA approval takes a wide range of factors into account, including:

• Treatments already available for the target condition:
  Reviewers weigh the risks and benefits of the drug to determine whether it is needed in the market. Risk tolerance varies according to the severity of the disease that the drug treats, and the severity of adverse effects. 
• Clinical findings:
  The agency expects a minimum of 2 adequately designed trials, and may demand more. To reach a decision, reviewers scrutinize all of these results and weight the benefits and risks for target populations.
• Risk mitigation measures:
  The agency assesses the risks for consumers, and may make recommendations such as an FDA-approved label. It may also require companies to implement a Risk Management and Mitigation Strategy (REMS).

The complexity of this process calls for specialized teams, which adds another layer of cost, which small to mid-sized pharma companies may struggle to absorb. 

In cases like these, partnerships and alliances can do a great deal to speed up the process and lower the cost. Strategic partnerships can help to expedite approval, by filling gaps in a company’s compliance expertise. Partners with experience navigating the drug approvals process can help with long-term submission planning, and handling questions that health authorities may ask. 

Drug development misstep 3: Failure to keep an eye on the landscape

Even with solid product market fit and an experienced team guiding the process, it’s vital to keep up with developments in the market. To support their own market positioning, drug developers need to stay in the know about what other companies are doing, both rivals and potential partners.

For example, news of a failed clinical trial can help R&D teams to steer the project in a safer direction. On the other hand, a successful trial conducted by another company may eliminate the need to design and run your own. 

All of this information is missed when companies fail to track their competitive landscape before and during the drug development process. This can easily lead to a lack of distinction in the market, hindering market access and reducing the quality of returns. By staying abreast of competitor information in real-time, companies can better identify viable white spaces, and allocate budget and resources accordingly. 

Avoiding costly pitfalls through AI-based insights

With these and even more challenges around every corner, the need for comprehensive and accurate data is more acute than ever. In the world of drug development, what you don’t know can kill you – and there’s simply too much data out there for humans to track and manage without the help of AI.

Similari augments research and business development teams, enabling them to make faster, more accurate decisions through intelligent insights. From identifying innovation white spaces to spotting promising partnership opportunities, Similari helps companies to maximize the value of their drug development process, and steer clear of potentially mission-ending pitfalls. 

Discover the future of drug development today, through a live demo of Similari’s next-generation capabilities.

In-person conferences are back in full swing, and the event schedule for pharma and related industries is already jam-packed. But with so many events to choose from, businesses need to think strategically. Which events are really worth traveling to and attending in person, and which ones can you afford to piece together through publications and press releases?

Here is our quick guide to the conference scene, and how to refine the list.

Why attending in-person conferences is good business sense

Firstly, conferences provide an opportunity to learn new strategies and gain knowledge from industry experts in real-time. They also offer a unique chance to network with like-minded people: founders, researchers, healthcare executives, and leaders. While we’ve all become accustomed to doing this virtually, being in the room physically has definite advantages when it comes to relationship building.

For startups, conferences are a great way to get up to speed with the latest trends and regulatory requirements in their industry. Startups can also showcase their products at turnkey exhibits, generating exposure to potential customers and investors. Some conferences even offer pre-scheduled one-to-one meetings with investors or strategic buyers.

How to whittle down the list

With dozens of events spread over the entire globe, business leaders need to be selective. It’s simply not possible to be physically present at any more than a handful of in-person events.

Begin by identifying your goals. Are you primarily interested in networking or gaining specific information? If it’s networking, are you hoping to rub shoulders with potential partners, or investors? Each conference will have a different audience, so research this in advance to determine if it matches your goals.

Next, consider the agenda and speaker lineup. Conferences usually advertise a core organizing theme to distinguish themselves from others. It’s important to evaluate this theme in light of your own business needs. For early-stage startups, it makes sense to attend an event that emphasizes strategic partnerships. A telemedicine company may benefit from hearing industry leaders talk about digital transformation and compliance. 

To help you make the most effective choices, we’ve compiled a list of 5 upcoming events that are attracting industry leaders and innovators, each with a distinct focus.

Pharma USA: March 28 – 29, Philadelphia

This event will feature talks from over 100 industry leaders, with over a thousand change makers and innovators in attendance. Major themes for this year’s conference include the value of partnerships, and the primacy of data and insights for innovation and business strategy. 

Global Pharma & Drug Delivery Summit 2023: April 24 – 26, Frankfurt

Just like it says on the label, this event promises to examine the most vital pharma trends and key issues in the drug development process. Its stated aim is to bring together masterminds, including researchers, practitioners and technologists to forge collaborations and learn from the industry leaders in the room. 

BIOMED Israel: 16-18 May, Tel Aviv

The BIOMED Israel Conference and Exhibition is the largest convention in Israel for life sciences, bringing together industry experts, manufacturing sectors, and leading companies throughout the supply chain. The conference program will explore trends and innovations that are shaping the future of the life sciences industry. 

Similari will be attending, and we are especially excited to see conference sessions devoted to discussing AI in biopharma.

BIO International Convention: June 5-8, 2023, Boston

The BIO International Convention is one of the largest global events in the biotechnology industry. Hosted by the Biotechnology Innovation Organization (BIO) that brings together leaders and stakeholders in the biotech industry to exchange knowledge and ideas. It includes keynote presentations, panel discussions, networking events, and an exhibit hall showcasing the latest technologies, products, and services in biotechnology. The convention attracts thousands of attendees from around the world and provides a platform for connecting with potential partners and exploring new business opportunities in the industry.

Similari will be attending, and we are especially excited to see conference sessions devoted to discussing Innovation intelligence biopharma.

CPhI Worldwide: October 24-26, Barcelona

CPHI is a pharmaceutical event that brings together more than 100,000 pharmaceutical professionals annually through exhibitions, conferences, and online communities to network and identify business opportunities. CPHI Worldwide is the world’s leading pharmaceutical exhibition, hosted at a different location every year. The event covers all aspects of the industry and provides a platform for the global pharma community to gather and discuss the latest trends and innovations in the industry.

Similari will be attending, and we are especially excited to see conference sessions devoted to trend detention usign AI in pharma.

Being virtually everywhere at once, with Similari

The reality is that missing out on any large industry event comes with a cost. The value of creating in-person connections and learning directly from expert presenters is hard to quantify. But there is a way to stay in the know, without missing a beat. With Similari, you can access the most relevant insights from top conferences you weren’t able to attend in person – and even the ones you weren’t aware of. 

Similari’s insights mechanism surveys thousands of data points in press releases, M&A announcements, clinical trials and articles, and extracts critical insights for human decision makers to interpret and act upon.

Our next-generation AI empowers you to be (virtually) everywhere you need to be. Get in touch with our team to learn more.

The legal cannabis industry has emerged from its recent slump and appears ready for massive growth in the near future. It’s been a rough ride getting here, and companies who have weathered the storm now have the opportunity to turn things around.

But what do piecemeal legalization and broad investor pullback mean for businesses? And how should these trends impact the decisions being made right now?

In this article, we’re exploring the case for optimism, pointing out some of the pitfalls, and recommending the best way forward. 

The case for cautious optimism: signs of recovery and opportunities for the future

The US legal cannabis industry took a beating in 2022, with the largest publicly-traded companies reporting collective losses of $550 million. But analysts are now expecting a rebound, with sales projected to increase to $50.7 billion by 2028.

This growth is expected to come largely from the piecemeal, state-by-state legalization that is now taking hold. New states that are now beginning to allow sales: Connecticut and Missouri for adult-use, and Mississippi for medical use. It’s likely that as these regulatory changes open new markets, the industry will eventually right-size itself relative to the level of demand. That could allow cannabis-lifestyle and therapeutic businesses to expand their operations into newly legal markets.

Opportunities for biomedical industries

It’s not just recreational or lifestyle use that benefits from a more relaxed regulatory environment. It also creates more opportunity for biomedical and pharmaceutical companies to investigate cannabis for therapeutic applications. Pfizer’s acquisition of Arena Pharmaceuticals was just one major signal of this increasing interest in cannabinoids. 

Cannabinoids are already used in a wide variety of therapeutic applications spanning pain management, immune diseases and improving patient outcomes in chemo-therapy. Legalization is making it easier for researchers to access and work with cannabis, even though at the federal level, significant obstacles remain. 

But overall, the industry looks ripe for product innovation and accelerated growth. 

Why the cannabis market isn’t fully out of the woods just yet

The legal cannabis industry has to reckon with the same broad investor pullback affecting almost every other industry. Investors are – understandably – demanding more proof of future profitability, which may be especially difficult for operators in mature markets where prices have leveled out. In addition, inflation and rising costs will continue to impact the bottom line. 

With funding and M&A on a downward trajectory that shows no signs of reversing anytime soon, the industry’s best chance lies in strategic partnerships. 

Better together: why partnerships are so crucial for cannabis companies

Partnerships have the potential to act as a force multiplier to differentiate companies, expand their reach, and navigate complex regulatory challenges. 2023 has already been hailed the “Year of Partnerships” by industry leaders due to the recognition that the industry will need to pull together to overcome challenges and make the most of the potential the market is showing. 

Partnering for compliant distribution: MSOs and co-manufacturers 

As we mentioned earlier, legalization is rolling out state by state, but federal prohibitions still make interstate commerce illegal. This is where Multi-State Operators (MSOs) play a pivotal role, enabling companies to expand their presence to other states, without running afoul of federal rules. 

 Co-manufacturing partnerships offer another way for companies to expand without overextending budgets. These deals enable companies to pool resources to enter new territories or start new product lines. This can also help to avoid the costs of setting up new manufacturing operations, leveraging partners’ facilities instead.

Accelerating biotech research through partnerships with authorized manufacturers

Partnerships are also enabling progress in biotech research and the creation of cannabis-based medicines. The recent partnership between biotech company Bioharvest Sciences and Royal Emerald Pharmaceuticals is one example of this type of partnership. Through this alliance, Bioharvest Sciences can more easily sell to research institutions federally, leveraging Royal Emerald Pharmaceutical’s ability to legally produce marijuana in the United States.  

Cut through the noise with AI-enabled insights

To succeed in an increasingly saturated market, cannabis companies will need to take a hard look at operational efficiency, and cut costs aggressively. But even more importantly, they will need to find ways to know what is happening in the market, moment to moment, in order to move in on opportunities as they arise.

Similari empowers companies with the always-on market intelligence they’ll need to make agile decisions in a constantly evolving commercial landscape. Through its next-generation insights mechanism, Similari gives researchers and innovators real-time updates on competitors, technological advances and critical changes to the regulatory environment, adapting to users’ needs and preferences over time. 

Schedule your very own demo with our team to see how Similari brings order out of chaos, with timely, reliable insights. 

It’s no secret that drug development costs are ballooning, while the number of products that actually make it market is decreasing. This phenomenon has become a kind of truism in the life sciences – so much so that it has spawned its own “law”. The so-called “Eroom’s Law” states that, adjusted for inflation, the cost of drug production doubles every 9 years.

That may or may not be technically true. But what’s undeniable is that managing the costs of drug development is now a critical priority for the pharmaceutical industry. Advanced AI holds the promise of shortening the timeframe and reducing the overall costs of drug production through in silico modeling. But a long process of research and ideation has to take place before an asset reaches that stage. 

And traditionally, that process has been resource-intensive, both in terms of cost and personnel. In this article, we’re taking a look at how this stage can be optimized and accelerated with AI-enabled search capacities that augment human research teams. By saving time and cost here, companies can move from white space to asset more quickly and more confidently than ever before. 

But what’s really new? Drug development has never been cheap

The high costs of drug development are nothing new. Neither are the factors that go into it: extensive research and development, clinical trials, regulatory approval, and marketing. Drug development has also always required investment in infrastructure and personnel. Back in 2021, the average cost of developing a single asset was estimated at just over $2 billion. 

But while the fundamental inputs that go into that cost haven’t changed, their relationship to market conditions have. 

Cost up, ROI down: the conundrum for Pharma

According to a Deloitte study, 2023 is putting drug developers between the rock of higher development costs and the hard place of lower sales forecasts. In 2022, costs rose to pre-pandemic levels, with sales dropping almost as low as 2019. Looking beyond 2023, pharmaceutical companies in the United States will also need to be agile enough to adjust their clinical and commercial strategies as the Inflation Reduction Act changes the commercial landscape for prescription drugs.

The perfect storm of scarce talent and tight budgets

Another factor complicating the question of cost is the prevailing state of the market. The economic outlook for 2023 is bleak, and companies across the board are being forced to trim the fat, and find ways to do much more, sometimes with much less. At the same time, employees still hold the upper hand in a historically tight labor market. Skill shortages are being felt everywhere from high-tech to marketing. 

For business leaders, the problem is made even more insoluble by global employee mobility and remote work: highly skilled professionals are able to be selective about who they work for, and they’re confident enough to strike out on their own in search of better conditions. 

Managing costs with artificial intelligence 

These challenges underscore the importance of leveraging AI solutions to manage costs, optimize processes, and make up for personnel shortages. Here’s how AI-enabled research impacts every stage in the development process.

Hit identification: automatically sorting the hits from the misses

The average cumulative cost for this initial stage is around $102 million. AI can help to control this cost by analyzing large datasets of biological and chemical data before it reaches human researchers. They’re then able to focus their efforts on only the most promising targets. This can radically enhance high-throughput screening, by accurately picking the best compounds for analysis.

Progressing hits to leads and optimizing them

Tools like Similari make it possible for researchers to instantly survey the landscape and technical literature to accurately assess the viability of leads. By automating away the bulk of their otherwise manual search time, these tools accelerate the hit-to-lead and lead-optimization phases. And by leveraging proactive analytics, researchers can respond to emergent threats or new findings, to adjust their strategy accordingly. 

Streamlining preclinical development & clinical trials

Always-on market surveillance allows researchers to “fail early” (and cheaply) by knowing the results of prior research. With the volume of clinical data growing exponentially year on year, AI offers a way for human teams to cut through the noise and identify salient insights that can make or break their innovation initiatives. 

Streamlining approvals: the last hurdle 

By the time an asset reaches the approval stage, the cumulative cost will have reached around $3 billion. AI has a crucial role to play in managing these ballooning costs, before, and during the approvals phase.

Before an asset gets here, AI like Similari can help reduce the costs of regulatory approval by predicting the likelihood of approval and identifying potential safety issues early on. This helps companies make better decisions about which drugs to invest in and reduce the risk of costly regulatory failures. 

Companies can also leverage market intelligence to identify potential partners with the requisite expertise and experience to achieve compliance. By entering licensing or partnership agreements with these (usually larger) entities, startups in particular stand to gain a great deal when it comes to expedited approval and go-to-market. 

Breaking free of Eroom’s Law starts with data

The way out of the bind is to improve the quality and the quantity of data available to decision makers. Similari puts thousands of clinical trials, along with millions of patents, articles and press releases at their fingertips. R&D professionals and innovation leaders are leveraging Similari’s next-generation AI capabilities to accelerate drug development, and improve outcomes with accurate and up-to-date insights, moment to moment. 

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.  

  Demystify the language of innovation intelligence. 

A

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).

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B

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

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

Biomarkers

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

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.

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C

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.

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D

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. 

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F

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.

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I

Innovation

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.

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L

Licensing

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.

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M

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.

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N

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.

Non-obviousness

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.

Novelty

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.

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P

Patentability 

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.

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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.

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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.

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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.

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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.

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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. 

Explosive growth, high demand and limited supply: these are challenging times for the life sciences industry. The talent crunch has hit the sector hard, and it could not have come at a worse time. In the midst of unprecedented growth in innovation and research, pharmaceutical and medical research companies urgently need to scale operations without scaling costs. And the traditional method of throwing more people at the problem is simply no longer an option in a tight labor market, with recruitment pipelines already running dry.

The challenge is twofold: how do companies get around R&D and data talent shortages? Perhaps even more urgently, how can they optimize business processes in order to empower their existing talent to spend less time on manual searches and more time making decisions and innovating?

In this article, we’ll outline the various ways in which AI and machine learning solve both problems at once, allowing companies to muscle in on the opportunities that rapid, ongoing growth provides.

Talent shortages in the life sciences industry: water, water, everywhere – but not a drop to drink.

In the wake of world historic levels of biopharma innovation during and after the COVID-19 pandemic, the industry has been left in a kind of paradox: growth and investment are up, but confidence is tepid. There is serious concern about whether this can be sustained, given the available crop of STEM talent in many developed countries. In the US, there are roughly twice as many job postings for life sciences positions as there were before the pandemic. Unsurprisingly, executives have been sounding the alarm: according to Randstad, 33% have identified talent scarcity as a major pain point, with 45% ramping up hiring to protect their businesses against staff attrition. 

The life sciences enter the global race for digital skills 

Part of the problem stems from the fact that the industry is so hungry for data and engineering skills – skills that are already in high demand across many other industries. And the urgency of this need is compounded by the skills gap that has been noted by the Association of the British Pharmaceutical Industry (ABPI): 43% of respondents in a recent survey said that digital literacy was a concern. These findings suggest that life science professionals are not currently receiving adequate training in digital and data skills, making it all the more urgent for companies to hire outside traditional boundaries. 

Bridging the gap and empowering people: the promise of AI

Advances in AI, machine learning and Natural Language Processing (NLP) are providing a much-needed way forward. One of the areas in which it’s making the biggest impact is in revolutionizing the way researchers work. In place of labor-intensive manual search, platforms like Similari offer smooth, automated processes that are more accurate and more scalable. 

By leveraging machine learning algorithms, researchers are able to sift through large volumes of data much faster than they could before, identifying patterns that would have otherwise been invisible. They can also avoid costly mistakes in clinical trial design by having unfettered, instantaneous access to results of relevant trials from around the world.

Faster, better drug discovery through enhanced technical data monitoring

AI also has the potential to revolutionize drug discovery and development. Data-driven algorithms and machine learning techniques make it easier to identify new drug candidates more quickly and accurately than ever before. Going even further, AI can be used to optimize existing drugs for improved efficacy and safety profiles. 

For example, AI can analyze large datasets at a much faster rate than humans can, making it easier for researchers to identify promising compounds. It can also help reduce costs associated with drug discovery by automating mundane tasks such as data entry and analysis. Even better, AI systems can learn from each iteration of the drug discovery process, enabling them to refine their strategies over time.  

AI in life sciences: unlocking human potential

But the benefits of AI go beyond simply reducing the need to grow R&D headcounts. The shift to AI-enhanced monitoring and research isn’t even about replacing people. Rather, it’s about augmenting human capital, and empowering human researchers to do their jobs better. And because AI-based systems can be “always on” in ways that humans cannot (and should not) be, they allow researchers to keep pace with a rapidly evolving environment.

Amidst dangerously high staff turnover levels, and the looming threat of burnout dogging the industry at large, it makes strategic sense to equip teams with tools that make their lives easier and their workflows more efficient. Companies who do so will enjoy higher talent retention rates, a priority in the current climate.

Similari: best-in-class software for R&D teams

In the world of life sciences research, AI has gone from nice-to-have to essential in a fairly short space of time. At Similari, we’ve brought together leading expertise in AI, machine learning and NLP to create the solution that the industry needs. With Similari’s always-on, intelligent surveillance of technical data, your teams can stay in the know at all times, while offloading up to 80% of the time they’re currently spending on research.  

To learn more about how Similari is changing the game businesses around the world, schedule a demo. Our team is ready to show you what lean, AI-enhanced research could do for your business. 

In 1965, Gordon Moore predicted that the number of components in complex integrated circuits would double every two years, for a CAGR of 41%. In particular, he was talking about the number of transistors on microchips. As it turned out, he was right, and his prediction became “Moore’s Law”. It’s become synonymous with a kind of confident technological optimism: we expect computational progress to continue unabated, and for processes to get better, faster, and easier over time.

Cut to the world of drug discovery, where we would really hope to see this taking place, and it just isn’t. Drug discovery is actually becoming harder, slower and more costly over time, so dramatically that researchers have coined “Eroom’s Law”, the inverse of “Moore’s Law” to describe how bad things really are. Proponents of this view estimate that the cost of developing a new drug doubles every nine years. Diminishing returns indeed. 

How do we flip “Eroom” back to Moore?

The answer to this – as with many 21st-century challenges – lies in enhanced research through AI, machine learning and natural language processing. In this post we’ll discuss the process of drug discovery and show how new technology offers a promising way out of mounting costs and inefficiencies. 

What is the actual cost of drug discovery?

Estimates vary wildly, but producing a new drug costs anywhere between $314 million and $2.8 billion. The time it takes to get a new drug to market ranges between 10 and 15 years. The clinical trials phase absorbs the bulk of this time and money, but costs mount at each stage along the way. And long before a subject reaches the clinical phase, companies must dedicate substantial time and resources to Research and Development. The median capitalized R&D cost for new drugs is around $1.1 billion. It has never been more urgent for pharmaceutical and life sciences research companies to trim costs and boost efficiency – ideally both at once. Thankfully, that’s exactly the win-win that AI can deliver.

Speed: machine Learning finds patterns in huge amounts of data, in an instant.

The number of relevant data points in the life sciences continues to balloon, outstripping humans’ ability to ingest, process and categorize huge amounts of data. Machine learning answers the need for rapid, accurate pattern recognition across millions of data points. Even more importantly, artificial intelligence of this kind doesn’t need to be explicitly programmed. Instead, it learns over time and adapts to the user’s preferences. 

The use cases within drug discovery alone are numerous. Researchers can use this technology to identify correlations between certain molecules and adverse clinical outcomes. Or they can analyze thousands of research records, clinical trial results and articles in minutes to identify salient trends for directing research away from dead ends. 

On target: improving accuracy in Research and Development 

Minimizing human dependencies in the R&D process doesn’t just make it faster – it reduces error. Through predictive analytics, sophisticated software can predict which substances are likely to have therapeutic value based on their molecular structure or other properties. This eliminates much of the guesswork associated with traditional drug discovery methods, resulting in more accurate results that can help reduce waste during later stages of drug development. Some studies have shown that machine learning models can replace clinical trials with simulations, or eliminate the need for a trial altogether by retrieving relevant results of an existing trial that may otherwise have been missed. 

A way to beat the “Throw Money At It” tendency

As Scannel noted, one of the main tactics companies have employed to accelerate drug discovery has been to ramp up hiring of R&D personnel, thereby “throwing money” at the problem to make it go away. This is risky in the best of times – and we don’t live in the best of times for high tech hiring. Amidst ongoing, chronic and global talent shortages, companies (even large ones) can not afford to solve their speed problem by exacerbating their cost problem. 

If they adopt intelligent software instead, they won’t have to. AI has the potential to reduce costs associated with drug discovery processes by increasing efficiency throughout each step, from early discovery right through to the regulatory and surveillance phases. By reducing manual labor required for data analysis tasks, and leveraging predictive analytics to identify promising compounds earlier in the process, companies can realize significant savings across each stage of a given project. 

Similari: at the forefront of AI-enhanced drug discovery

With the right approach to AI, the pharmaceutical industry can finally make a dent in the multi-billion dollar price tag of developing new drugs. At Similari, we believe that can be done, quickly, and without scaling costs. We have brought together expertise from the domains of AI, machine learning and NLP to create a solution that enables accurate, always-on surveillance of life sciences data, on a scale that was previously out of reach for most pharmaceutical companies. 

To learn more about how Similari’s state-of-the-art platform and industry-leading capabilities could push your drug discovery process to new heights, schedule a demo with our team. We’re really excited about the future, and we’re ready to show you how we plan to get there. 

It’s no secret that it’s hard to succeed as a pharmaceutical business within the life sciences industry, especially if you are a smaller organization or a start-up still chasing growth. The sector is fiercely competitive, with businesses often locked neck and neck to design, test, review and release new products and develop novel innovations in service and technology first and gain greater market share. 

Safeguarding innovations and protecting invaluable intellectual property through patenting is also a constant challenge, with businesses needing to navigate various forms of IP risk and monitor the global landscape for shifts towards and away from key technology trends and areas that could impact their portfolios. 

For smaller companies going up against global pharmaceutical enterprises, the odds of achieving growth and profitability can seem stacked against them from the start. Unlocking growth and revenue opportunities rely on being flexible, adaptable, and agile, something that start-ups and smaller businesses can leverage more easily compared to creakier, rigid legacy enterprises. 

By tapping into their innate agility and implementing smart, strategic decision-making as early as possible, small pharmaceutical companies can rub shoulders with the titans of the life sciences sector and enjoy consistent market and revenue growth – and they can do this by harnessing the power of competitive intelligence.

We’ll be examining competitive intelligence in more detail below and how it can help start-ups gain the edge they need to get ahead of competitors. 

How can competitive intelligence help smaller companies and start-ups?

Competitive intelligence refers to the process of collecting and analyzing information to better understand your competitors and your competitive landscape through extensive, ongoing research.

It provides the insight necessary to differentiate and improve your R&D methods and development processes to gain a competitive advantage as well as keep abreast of competitor products, services, and patents in the pipeline that could potentially pose a risk to your business. 

Through competitive intelligence, you can identify potential business opportunities before competitors do, detect threats and risks before they escalate, and forecast project expenses for better planning and more effective budgeting.  

The problems faced by smaller pharmaceutical businesses and start-ups

As a smaller company in a long-established industry, start-ups face an uphill battle of structural, technological, and procedural factors. Larger pharmaceutical firms and enterprises have the size, scale, speed, and operations in place that automatically gives them a competitive edge over smaller companies when it comes to end-to-end product and technology development.

Global enterprises within the life sciences sector are well-established and stable and often carry the invaluable weight of reputation on their side. They have the resources, processes, and personnel needed to research, develop, test, review and launch their products. 

Smaller companies and start-ups, on the other hand, often lack the resources and funding larger companies do. Because their primary business objective is achieving growth, their development is often stagnated by the limitations of their existing resources, putting them at a disadvantage. 

However, the size and scale of larger companies can be their Achilles heel just as much as their advantage. Larger pharmaceutical firms are complex, highly stratified organizations and their ability to pivot, transform and adapt is slowed down by the weight of their own scale and protocol. 

Start-ups and smaller companies, on the other hand, are lighter in structure and process making them more nimble and flexible, allowing them to change and reform processes more quickly in response to emerging trends and data findings.

It’s in this space where start-ups can gain an edge and close the gap on competitors by leveraging competitive intelligence with their ability to quickly adapt their processes.

Traditionally research for competitive intelligence insights has been conducted manually by internal teams grappling to stay on top of emerging new findings and datasets, caught in a never-ending game of playing catch up. 

AI has emerged as a means of augmenting and speeding up the research process. It reduces the time required from hours to minutes and is also able to glean and analyze massive data volumes from various sources at a faster rate. This results in accurate, actionable insights that can be harnessed for more agile planning and strategy execution.

As opposed to larger enterprises trying to retrofit their fixed existing processes with AI capabilities to tackle the snowball effect of compounding data, smaller businesses and start-ups have the advantage of augmenting their competitive intelligence research with AI and machine learning from the start. 

This early adoption of AI will allow them to grow at a more rapid pace, capitalizing on its easy scalability to fast-track their competitive intelligence and use the insights gained to rapidly identify business and licensing opportunities, build accurate budgets for R&D projects, forecast future trends and adjust existing strategies immediately to stay ahead of the tech and IP curve.  

Similari: scaling your competitive intelligence with ease

It’s a common misconception that investing in AI is costly or out of budget scope, especially for start-ups and small businesses. However, the return on investment brought from augmenting your research with an AI-powered platform capable far outweighs the risk and revenue cost of losing vital growth and development opportunities to competitors. 

Swift insights gleaned from a multitude of data sources allow you to make important decisions quickly and take a proactive, instead of reactive, approach to innovation and scaling. 

At Similari, we’ve helped businesses of all sizes, from start-ups to enterprises, harness the power of data gleaned from their competitive landscapes to enable more strategic decision-making, and agile, adaptive development.