Navigating Pharma’s Staff and Budget Cuts: Strategies for Innovators

Shockingly, many layoff trackers exist in the pharma world. Each paints the same picture: patent cliffs and turbulent market conditions have led to pharma budget cuts, which means staff cuts. Novartis, Merck KGaA, Amgen, Sanofi, GSK, Bristol Myers Squibb and Takeda are just a handful of the hundreds of businesses making budget cuts in pharma. But does this mean we must pull the handbrake on innovation?

Read on to discover why artificial intelligence (AI) sits at the core of every strategy for innovators in the face of tightened budgets.

The pharma landscape: budget cuts are necessary

The world has a grim economic outlook this year. Economist after economist predicts subdued growth. Multiple factors come into play, from the war in Ukraine precipitating food and energy crises to debt-tightening since the pandemic. As such, no business can escape the need to cut budgets.

We are also seeing budgets have additional pressures.

The cost of pharmaceutical manufacturing is skyrocketing. There are reports that the cost of some active pharmaceutical ingredients (APIs) has increased by over 100% since pre-pandemic. 

The cost of aluminium foil, used in tablet blister packs, has seen jump after jump. 

Clinical trials are becoming prohibitively expensive (not to mention the timescales involved in typical drug development). 

These are just a few examples of the pharma-relevant budget pressures before factoring in that the industry is on the precipice of a patent cliff, with many companies losing market exclusivity for top-selling drugs in the next five years. 

And when companies cut budgets, an easy area to lose is innovation. But this would be a dire mistake. Instead, innovators must get savvy to level up their innovation while accepting their budgets will be tight.

The innovation backdrop: here lies our strength

Senior Vice President at Medidata AI, Arnaub Chatterjee, says, “If you look back at the past five years, it’s arguably one of the most innovative periods in drug development and the launches that have taken place in recent memory. So, we’ve seen booms in biotech and drug development, but also in parallel, there’s real advancements in technology that really is across all of the drug development life cycle, from finding a target to designing the trial, to getting a drug approved and launching the drug itself.”

We need only look at the Covid mRNA vaccines to recognise that innovation is our greatest strength in times of crisis and budgetary pressure. And artificial intelligence is the key to that innovation now. Research predicts that the AI market in healthcare will go beyond $20.6 billion this year, growing at a CAGR of 37% until 2030.

AI innovation in drug discovery, drug development, clinical trials, business model efficiency, forecasting and supply chain functions and more areas will enable innovation to lead growth. In addition, new partnerships will emerge (and already are emerging) that show AI is the heart of survival innovation.

If AI for innovation in pharma lies at the heart of managing budget cuts, what strategies should innovators deploy in practice?

AI innovation strategies in a time of budget cuts

1. Innovation is the way out of the crisis

Number one, don’t stop innovating. Get savvy and strategic with innovation investment, but don’t stop altogether. History tells us that businesses prioritising innovation in a crisis weather that crisis better, usually coming out the other side more robust and more profitable. AI is integral to innovating today but it is not a panacea. We also need future-proofed business models. 

Focus innovation efforts on adding value through gaining efficiency (improving the core business), building sustainability into the business function and uncovering exciting new growth opportunities. For all of these, leveraging AI can help.

AI innovations are a remarkable boon for efficiency, sustainability and growth. It’s where AI adds value in significant ways that positively impact both revenues and savings. Deploying AI solutions can produce high returns fast.

2. Balance short-term gain and long-term success

In a climate of budgetary cuts, innovators must demonstrate they can gain results quickly by grasping low-hanging fruits, but not at the expense of long-term innovative success. Short-term innovations can use AI to create efficiencies in different processes and functions. However, long-term innovations still require investment, and innovators should retain a slice of the innovation budget pie for this.

This approach to AI innovation requires an AI blueprint. It’s a case of evaluating options and knowing which innovations will produce the best returns. Hence, determining how and where to implement AI solutions in the pharma business is complex and requires a strategic AI plan. 

3. See budget cutting as a gift of focus

When budgets are bountiful, there is inevitable waste, procrastination and lack of innovative focus. Conversely, when innovation budgets are tight, it’s an opportunity to see the wood for the trees. What really matters? Where are the greatest gains? Which existing project is on the threshold of delivering worthwhile returns? Tight budgets force innovators to deeply, intuitively and measurably assess options which can help underpin greater success. Innovators who embrace the refined focus of tight budgets find they enhance their company’s value proposition while cementing the credibility of future innovation. I read recently that companies that have vast funds often fail more than lean companies that are under-funded so don’t let small budgets stop you. It may be your greatest gift!

4. Innovate now for levelled-up growth

Crises are a hotbed for innovation. It’s easy to run and hide from innovation in the crisis of budget cuts, believing it to be the safer option, but this would be a mistake. Innovations born of crises typically have the power to level up future success and sustainability. We saw it during Covid, and we can see it again now.

5. Use AI innovation in all parts of the value chain

a. Focus on AI-driven drug discovery

AI-driven drug discovery is the future and has made significant waves since the first report by Exscientia in early 2020. More and more clinical trials for AI-driven discovered drugs are underway. Again, AI can do things even the best world-class researchers can’t. From searching for new biological targets to utilising new techniques to find new molecules, AI has incredible scope for innovative success in drug discovery. Read more here.

b. Use AI innovation in Clinical trials

AI is able to improve clinical trials in so many ways.  I will highlight a few of many examples here. It can drive more innovative ways of collecting clinical-trial data and reducing reliance on in-person trial sites (e.g. capturing data from body sensors and wearable devices such as bracelets, heart monitors, patches, and sensor-enabled clothing), and by doing so researchers can monitor a patient’s vital signs and other information remotely and less invasively. AI algorithms, in combination with wearable technology, can reveal real-time insights into study execution and patient adherence. In addition, coupling AI with robotic process automation can harmonize and link data across different modalities of data collection can drive efficiencies. It can also assist in trial design, patient recruitment, data analysis and so much more.

c. Use AI innovation in the supply chain

Every pharma company is wincing at the increased supply chain costs they currently face. Again, artificial intelligence is the solution. Appropriately selected AI tools can increase efficiency in supply chains in myriad ways, cutting costs, time and waste. Supply chain sustainability must be a priority for pharma boards facing budget cuts as it’s an area ripe for change.

d. Use AI innovation in regulatory

The pharmaceutical space is a tightly regulated one, placing significant stress and risk on pharmaceutical companies. AI excels at the kind of laborious, manual work hitherto performed—more slowly and less accurately—by humans, like collating daily guidance and regulations and monitoring news networks and journals. Likewise, much of the data processing work can be done by AI. For example, redaction of personally identifiable data from documents can be done at a much greater pace by an AI, and fully compliant CSR can be generated, 90% complete and in just one hour. Regulations are complex and ever-shifting, but ultimately amount to nothing more than a series of intersecting rules, which AI algorithms have no problem parsing and applying to documents and data. It can also be used to scan (and render searchable) audio, image and video files, including text in images and videos or dialogue in audio that may pose a regulatory problem. This, in addition to always-on news monitoring, greatly reduces risk.

e. Use AI in forecasting 

There’s a burning need right now to create operational efficiency and sustainability in pharma, and AI is the solution. AI’s capabilities to radically transform forecasting will allow pharma companies to cut costs by banishing legacy processes, managing big data effectively, and navigating complex demand volatility. In addition, the right AI tools can spot trends and create predictions in a way humans cannot compute because they can manage real-time data from multiple big sources and, through machine learning, benefit instantly from the insights. We are seeing all sorts of data being used in pharma forecasting with AI including real-time mobile data automatically feeding into the forecast – which is pretty amazing when you think about it.

f. Use AI innovation in market access

Examples of where AI is currently innovating market access are:

Price optimization: As a largely data-driven and analytical process, it’s unsurprising that we’re already seeing powerful AI solutions for price optimization combining hundreds of data sets in real-time and calculating value pricing effectively.

Time to reimbursement: Artificial Intelligence gives us the means to uncover and correct the inefficiencies and get the reimbursed faster. It is possible to gather insights based on past industry-wide experience and information on policy changes and process alterations. 

Instant insights: No longer do we need an analytics team crunching numbers to answer important market access questions. Enter a combination of NLP and ML to do just that! 

Read more about AI in market access here.

g. Use AI innovation in medical affairs

Fortunately, breakthroughs in artificial intelligence (AI)—and especially natural language processing (NLP) and machine learning (ML)—have led to the creation of more sophisticated approaches to medical literature monitoring. Automated medical literature monitoring (AMLM) can increase the efficiency of experts in identifying and processes ICSRs and AEs by close to 90%, leading to more timely and accurate PV reports, safer conditions and better outcomes for patients, and reduced costs for pharmaceutical companies. Read more about this here.

h. Use AI innovation in strategy

The purpose of strategic planning is to prepare an organization for the future by setting goals and a process for achieving them. This requires capturing data and insights and then synthesizing that into a strategy. The data typically fall into four key areas:

1. What the company/organization offers (resources & competencies)

2. Who the company/organization serves (markets & customers)

3. Who competes with the company/organization (competitors)

4. The market environment the company/organization operates in (laws & regulations, economics, technologies, demographics)

Collating all relevant available data is the first step in an effective strategic plan. For healthcare strategic planning, this includes a wide range of disparate data sources. Enter AI. Read how to do it here.

i. Use AI innovation in market research

Market research in healthcare has already greatly benefited from the digital revolution, and artificial intelligence (AI) is already being used to reshape market research, from recruitment and questionnaire design to data organization and analysis. In addition, AI-powered disruptors are dismantling all areas of the value chain in market research, taking revenue away from incumbent healthcare market research agencies.

AI is being used for recruitment, conducting primary market research, analyzing and collating insights from research data (primary and secondary). To read more about this, read my more in-depth article on this here.

j. Use AI innovation in sales and marketing

Sales Force

Artificial intelligence (AI) is revolutionizing sales processes in industries around the world, allowing for a most customer-centric and data-driven approach. Pharmaceutical companies that are able to fully leverage AI, machine learning (ML), and other digital tools will benefit greatly, reversing the deleterious effects of COVID-19 and market disruption and coming out the other side stronger than ever. AI is being used in numerous ways in sales teams from predicting behaviour (physician behaviour in this case such as switching), precision physician targeting, customised sales messaging by individual physician, scheduling and automation, filling in forms, and much more.

Imagine you could quantify and reproduce the expertise and wisdom of your most experienced sales reps, those whose years of experience have allowed them to develop the intuition and insights to know the next best course of action to take with any lead. AI is now able to do just this—and make that knowledge available to every sales representative on your team. This approach leverages many of the same AI tools as those used to improve the customer journey, but on a far more granular level, providing representatives with the right content for the right channel delivered at the right time and to the right customer, for thousands of customers. Top-line sales has been shown to increase as much as 30% as a result of successfully implementing next best-action programs.

Marketing

I’ve written about many of these topics before but as a top line AI can be used to do things such as identifying rare disease patients, predict and modify patient adherence, put your content marketing on steroids, segment your customers by their dynamic behaviour and personalize the marketing by individual, create true omnichannel personalized marketing, map your customer journey, conduct analysis of social listening, enhancing customer call centre output through intelligent NLP and automation, KOL/TL mapping, to name but a few.

Read about using AI in sales and marketing in more depth here.

Collaboration is innovation

It’s not enough to realise that AI is the solution. As said, an AI blueprint is needed. Central to any AI blueprint is creating a clear plan to lead the company from where it is now and a clear path to take the company to achieving the goals using AI.

If we look at some of the pharma names listed early in this article for their recent layoffs, we can also see how many are already collaborating to innovate through AI. For example, Sanofi is working with four drug discovery companies, Exscientia, Insilico, Atomwise and Adagene. Amgen is collaborating with Generate Biomedicines. Bristol Myers Squibb is collaborating with Envisagenics.