Month: November 2022

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.

Success in the life sciences industry is driven primarily by both accuracy and speed. Businesses within this sector, no matter their size, are constantly on the lookout for ways to streamline and improve their operations to fast-track innovation development while also keeping abreast of IP monitoring and considerations for pipeline products. 

The challenges of clinical trials and IP management 

Within this process, clinical trials and testing remains one of the most costly and time-consuming stages. The staggered nature of clinical trials means that it often takes years before a product is approved for market release. 

Extensive research is required at every stage of clinical trials, from the pre-clinical development phase to the post-market surveillance phase. Outdated, manual research methods aiding clinical trials slow down the process even further, straining budgets and potentially costing millions of dollars in lost revenue. 

According to findings from PhRMA, a single product can cost a business up to $500 million to develop throughout its entire research and development lifecycle. 

However, delays to the trial process can cost a company anywhere between $600,000 to $8 million per day according to CenterWatch. 

When it comes to IP and patenting, dynamics can change fairly quickly, making it essential to monitor emerging IP trends and regulations to anticipate disruptions that could impact current trials and testing. Again, this becomes a mammoth task for research teams manually sifting through thousands of volumes of incoming data, costing businesses IP opportunities. 

Time is also of the essence when submitting documentation necessary for IP registration and maintenance, both of which are crucial for protecting licensing and distribution rights for hard-earned innovations. But the process is bogged down by excessive paperwork and documentation. 

AI’s role in effective IP and clinical intelligence management

For both clinical trials and IP monitoring, the problem of scale becomes apparent. The larger a business’s IP portfolio, the harder and more expensive it becomes to manage manually. The better the clinical trial intelligence is, the faster and more streamlined the process, the greater the research demands become, and, ironically, the slower the research process ends up being.

The only solution for solving these challenges of scale and cost is to augment and elevate the research process using AI and deep learning. 

An AI-powered insights management platform is capable of processing and analyzing vast amounts of incoming datasets from a variety of sources, condensing and presenting them as insights that drive swift, informed action, which can aid and speed both clinical intelligence and IP monitoring processes. 

5 things to look for when choosing insights management software

Not all platforms offer the same features that enable the accurate, competitive edge you’re looking for. We’ve compiled some key capabilities below that every insight and innovation management platform worth its weight should have.

Integration capabilities

The provider you opt for should offer seamless integration, offering a “plug and play’’ style of assimilation that allows the platform to integrate within your existing tech stack and other solutions. It should allow you the flexibility to utilize the end-to-end capabilities of the platform within your existing digital ecosystem, enriching your workflows and processes. 

Search and responsiveness 

An ideal management software solution should eliminate the need for you to constantly be chasing data – it should bring the data you need to you. It should be able to quickly and easily track, collect and analyze various forms of complex datasets and generate comprehensive, yet accessible reports and findings that can be instantly understood. 

It should also be responsive and take user feedback into account to tailor its search capabilities to user preferences. For instance, if there’s a source or data set included in your feedback that you don’t find useful, the platform should have a function that recognizes and incorporates your feedback using machine learning.

Ease of use

A solution that’s too complex or time-consuming to use is going to end up being an efficiency drain, no matter how advanced its search and analysis capabilities are. While the platform should be robust and agile, it should still offer an intuitive and user-friendly interface that’s easy and enjoyable to use, producing results within minutes.

Security and performance

With cyberattacks and ransomware becoming more of an ever-present reality, it’s vital to opt for a solution that has robust, up-to-date security compliance procedures protecting your data. Ensure it has a dedicated security team focused on safeguarding your data and actively working to continuously update and improve its security protocols. 

Implementation, and ongoing training and support

Any good insight management solution should be dedicated to your success. Onboarding, training, implementation, as well as ongoing support management, should be offered no matter the service bundle you opt for. 

However, the ideal tool should be offering more than support and maintenance services, it should be helping you elevate your ROI and maximize its value by offering virtual or in-person meetings where key stakeholders can help you derive more value from your tool’s features and capabilities.

Similari: bringing the clinical and IP intelligence you need to you

At Similari, we bring the competitive intelligence necessary to chase innovation and streamline your development to you. Our extensive, AI-powered platform is comprehensive, sifting through and analyzing millions of datasets from a variety of sources, yet accessible, presenting these findings as intuitive, actionable insights that drive strategic decision-making and optimize performance. 

Get the data you need at your fingertips within minutes and tailor your searches according to your feedback and preferences that the platform will recognize and incorporate using deep learning. 

Our customer service is unmatched, evidenced by our 95% customer retention rate, meaning we’re dedicated to your success every step of the way. 

Within the field of pharmaceutical research, there are an overwhelming number of variables to consider within research and development (R&D) as well as production and manufacturing. 

Between these considerations, navigating government and industry regulations, existing and impending medical patents, and staying ahead of competitors while keeping abreast of current trends that can impact trial results and outcomes make manual research a logistical nightmare for CI professionals and their teams. 

On top of this, the fight to find the data necessary to make swift, strategic decisions means it’s even harder for C-suite executives to ensure that R&D budgets stay on track. Projects extend beyond deadlines due to the never-ending snowball effect of vast new sets of incoming data and information that impact research and clinical trials and testing.

AI is reshaping the way data is curated and mined for insights

With such an overwhelming abundance of unstructured and unusable data that needs to be identified, extracted, and analyzed, it’s unfortunately far too easy for invaluable insights to slip through the cracks, setting teams back and potentially costing businesses millions in lost revenue.

The only scalable solution for efficiently managing vast amounts of incoming large-scale datasets is to leverage the power of AI and machine learning to streamline and automate the research process. 

Today, we’ll be making the case for why AI and automation should be front and center in your research strategy to speed up, simplify and reduce the costs of your research process, no matter your organization’s size or scale.

AI’s competitive edge in research capabilities

AI-powered systems have the ability to rapidly collect, analyze, interpret and huge volumes of incoming datasets, presenting them as accessible, actionable insights that enable swift and powerful decision-making. 

AI’s ability to trump human input when it comes to research and analysis output is not anecdotal or circumstantial. In a recent case study by Hubspot, researchers examined the effects of Intelligent Literature Monitoring (ILM) which augments literature searches with AI and Natural Language Processing (NLP) capabilities compared to a controlled study that was manually conducted. 

Compared to the manual research process results, AI-assisted ILM reduced research time by between 88% and 92%, while still achieving 99.8% sensitivity and 95% specificity. 

By utilizing AI and machine learning to power their research operations, CI professionals and teams can transform the way they conduct research, resulting in efficiency gains, greater research accuracy, and reduced expenses that ease strained R&D budgets. 

AI systems can quickly and efficiently mine enormous amounts of data from various medical publications, articles, press releases, research papers, and other sources. Using deep learning, it can accurately interpret both printed and handwritten text as well as chemical representations and figures, test results, scans, and imaging to glean essential data while filtering out unimportant data, presenting it as accessible insights.

Research teams no longer have to spend endless hours manually researching and interpreting relevant data to inform development strategies. 

With the data they need at their fingertips, they can reinvest this recovered time into strategic planning and modeling, such as identifying new business opportunities, managing potential risks to pipeline products, and analyzing competitor actions, all of which translates into more business revenue and allow for better budget guard railing.  

AI as a scalable research solution

Thanks to a complex interplay of 2020’s global recession coupled with ongoing rising inflation and curbed consumer spending, 2022 has seen a slew of layoffs across the tech sector. Companies looking to conserve their resources are increasingly streamlining and automating their operations where possible. 

A common misconception is that adopting AI and machine learning is a more costly investment decision as opposed to simply hiring more staff. While scaling your teams might seem like a more cost-effective strategy upfront, the ever-increasing data volumes they’ll be facing means that, for your teams to keep up, you’ll continuously need to invest in expanding your team, driving up overheads and stretching budgets.

AI is a viable alternative that is inherently scalable and self-sustaining, able to manage increasing data volumes without affecting its output, accuracy, and efficiency. Equipping your teams with a scalable research solution, as opposed to perpetually hiring additional staff members, solves both the underlying challenges of scale and cost.  

Similari: Your partner in research innovation

At Similari, we harness AI and deep learning capabilities to streamline and simplify your research operations to enable faster, more agile decision-making and proactive, instead of reactive, development strategies to keep you one step ahead of competitors. 

Our platform seamlessly tracks, consolidates, and analyzes enormous volumes of datasets in seconds to bring the data you need to you, eliminating the costly, time-consuming hunt for vital information necessary to take swift action, capitalize on opportunities and mitigate potential risks with ease.

For any business that deals with innovation in the life sciences or high tech, avoiding IP risk and optimizing expenses are critical priorities. To realize the full value of their innovation, patentees need a reliable way to insure their portfolios against various types of risk, without scaling the size of their teams. In this post, we will discuss the various forms of IP risk, and how AI-powered innovation intelligence answers the need for the accurate and scalable monitoring of technical data that enables sound decision-making.

In the process, we will demonstrate how, even for small companies and startups, the cost of investing in these next-generation technologies is negligible in comparison with the mission-ending costs that can arise from IP risk.

Categories of IP risk

The authors of Edison in the Boardroom identify three broad categories of IP risk:

• Intrinsic risks: those factors that are part and parcel of the patent process, and that companies can manage to some extent 
• Innovation risks: the inherently uncertain nature of creating a new product or service and making it publicly available.
• Environmental risks: conditions may change due to regulatory action or competitors’ moves. This type of risk is obviously difficult to manage but needs to be understood.

Unlike the other two, intrinsic risks can be minimized, if a company takes steps to improve the validity and enforceability of its patents. For example, IP professionals do extensive searches of existing patents to ensure that theirs is not a derivative of any patent that has already been filed. This is a necessary exercise because a finding of prior art can invalidate a patent, or even result in IP infringement. Only on the back of extensive research can any company be sure that its patent is fully valid. 

The obvious problem here is that ruling out prior art in this way is extremely time-consuming, even for companies with large teams (more on this later).

The trolls lurking under the bridge

Another ever-present risk to innovation is that of non-practicing entities (NPEs), pejoratively known as “trolls”. These are entities that use IP in bad faith to sue companies for infringement. Typically, this is done using portfolios of unused patents, with the goal of settling just below the cost of litigation. By some estimates, NPEs file around two-thirds of all patent disputes. And with patent trolling on the rise, it’s more important than ever before that businesses have a way to stay ahead of bad actors and protect their interests.

Costs of IP litigation

IP infringement lawsuits – legitimate or not – are expensive indeed. All told, the cost from discovery to disposition can easily run into the millions, with the average cost somewhere between $2 million and $4 million in the United States.

Conventional risk management

The key to staying ahead of all of these risks is knowing the landscape in sufficient detail to avoid missteps, and anticipate competitors’ moves before they happen. But that’s difficult to achieve using a conventional approach to IP management, which usually involves intensive manual searching and monitoring of technical data across multiple channels.

How AI helps

Expediting your FTO analysis with sound, up-to-date data gleaned from a multitude of sources is the only way to soundly and securely stay on top of current and emerging IP risks. The fastest, most-efficient way to do this is to fast-track your innovation intelligence with the help of AI and deep learning, allowing you to assess existing and potential IP risks and identify opportunities with zero latency.

AI-delivered insights bring the data you need to you, eliminating unnecessary manual research and enabling you to make faster, more agile decisions based on real-time patent and IP opportunities and mitigate the risks of high-potential business threats before they impact your bottom line.

Similari: your partner in innovation intelligence

At Similari, our mission is to make innovation easily accessible through smart, strategic AI, machine learning, and automation capabilities. Our platform is both robust and intuitive, able to track and consolidate millions of data points into condensed, actionable insights for quick, effective strategy generation and decision-making. 

Say goodbye to costly, mission-ending errors resulting from inefficient IP opportunity identification and analysis and hello to end-to-end efficiency and productivity gains that come from having all the data you need at your fingertips when you need it.