How AI is Reshaping the Pharmaceutical Industry

 

 

Introduction

With the advent of artificial intelligence (AI), every aspect of humans and business is facing the impact. AI is steadily changing how industries work.  The pharmaceutical sector is no different; AI is bringing new ways to solve old problems. From speeding up drug discovery to making clinical trials more efficient, AI is creating real improvements. But it's not all smooth. There are issues around data safety, regulations, and the role of human workers. In this blog, I will try to explain how AI is affecting pharma today, with insights from experts.

Pharma Is Slow, Expensive, and Risky

One of the biggest issues with the pharma industry, which is also the core also is that developing a new drug is not easy. On average, it takes around 10-15 years and costs more than billions to bring one drug to market. Secondly, most drugs fail during the clinical trial phase. Only about 10% of drugs that enter clinical trials actually get approved.

Other operational issues include:

• Delays in patient recruitment for trials
• Long and manual processes in research and manufacturing
• Difficulty in predicting how patients will respond to treatments
• Rising costs with little room for error

These challenges make the industry slow to innovate and unable to meet urgent health needs quickly. One of the biggest examples we have seen is during the COVID-19 pandemic. Where countries faced shortages of the COVID-19 vaccine.  

The Consequences

When drug development is slow, patients suffer. During the COVID-19 pandemic, traditional methods wouldn't have delivered vaccines in time. Quick turnaround was possible only because companies used AI tools to speed things up.

• Patients with rare diseases often go untreated for years due to limited research.
• Pharmaceutical companies spend millions on failed trials.
• Delays in drug approval can lead to higher mortality and public mistrust.

On top of that, the amount of data in healthcare is growing fast. But making sense of it without AI is nearly impossible. Manual analysis isn't enough when you're dealing with millions of patient records, genetic data, and clinical notes.

 AI Brings Practical Tools to the Table

AI helps make sense of large data sets, find patterns, and make predictions. It doesn't replace scientists or doctors, but it supports them in making better, faster decisions.

Let's explore where AI is already making a difference.

1. Drug Discovery

What AI Does?

• Analyses data from past research to predict which compounds might work
• Simulates how new drugs will behave in the body
• Reduces the need for trial-and-error in labs

Example:

• Benevolent AI helped identify baricitinib, an arthritis drug, as a potential COVID-19 treatment. This was achieved in just a few days using AI algorithms.
• DeepMind's AlphaFold predicted the 3D structure of over 200 million proteins, a task that would take humans years.

Impact: Drug discovery timelines are cut by years, and costs are reduced by up to 70% in some cases.

2. Clinical Trials

What AI Does?

• Matches eligible patients to clinical trials using electronic health records (EHRs)
• Predicts which patients are more likely to respond to a drug
• Flag potential safety issues early

Example:

• Pfizer used IBM Watson to match patients to oncology trials. This reduced the time taken for recruitment significantly.

Data Point: Around 80% of clinical trials are delayed due to problems with recruitment. AI can cut this delay by up to 30%.

3. Personalised Treatment

What AI Does?

• Analyses genetic, lifestyle, and environmental data to recommend treatments
• Helps doctors make more informed decisions

Example:

• Tempus uses AI to provide oncologists with data-driven insights for treating cancer patients based on molecular and clinical data.

Impact: Improves treatment effectiveness and reduces the risk of adverse reactions.

4. Manufacturing and Supply Chain

What AI Does:

• Predicts equipment failures before they happen
• Optimises supply and demand planning

Example:

• Novartis has been using AI to monitor its production lines, reducing waste and improving efficiency.

Data Point: Predictive maintenance using AI can reduce unplanned outages by up to 50%.

The Advantages: Why AI Makes Sense

• Faster time to market: AI tools cut months or years from R&D timelines.
• Better decision-making: AI helps identify patterns that humans might miss.
• Improved accuracy: Reduces human error in data analysis and prediction.
• Cost savings: Automates tasks that would require large teams.
• Scalability: AI systems can handle growing amounts of data without losing speed or accuracy.

It's Not All Smooth Sailing

Despite the progress, using AI in pharma isn’t without its issues.

1. Data Privacy and Security

Patient data is sensitive. AI needs large datasets to work well, but this increases the risk of breaches.

Concerns:

• Compliance with HIPAA, GDPR, and other data protection laws
• Need for anonymisation and encryption
• Who owns the data? Patients or companies?

2. Regulatory Hurdles

AI tools must be approved by regulators before use. But current frameworks were built for traditional methods, not algorithms.

Challenges:

• Lack of clear guidelines
• Need for "explainable AI" that regulators can understand

3. Trust and Human Expertise

Doctors and researchers may hesitate to rely on AI, especially when they don’t understand how it works.

Issues:

• Black-box models offer predictions but not explanations
• Need for training to help staff use AI tools properly

4. Bias in AI Systems

If the data used to train AI is biased, the results will be too. This can lead to wrong or harmful outcomes.

Fix: Regular audits and diverse data sets are necessary to avoid these issues.

Expert Insights: What the Leaders Say

Eric Topol, MD, says, "AI won't replace doctors, but doctors who use AI will replace those who don't."

Vas Narasimhan, CEO of Novartis, calls AI "essential for the future of medicine."

FDA Commissioner Robert Califf has spoken about the need to modernize regulation to keep up with AI's growth in healthcare.

What's Next for AI in Pharma?

1. Smarter, Explainable AI

Future AI tools will be designed to explain how they work, building trust among users and regulators.

2. Wider Use in Developing Countries

AI could help bring modern medicine to areas with fewer resources by speeding up diagnosis and treatment.

3. Closer Collaboration with Regulators

Regulatory bodies are starting to work more closely with companies to create safe and effective AI tools.

4. AI-First Biotech Startups

New companies are being built around AI from day one, giving them an edge in speed and innovation.

Conclusion

AI is not going to solve all the problems that industry is facing; However, it's a strong tool that can help fix some of the biggest problems in pharma. By accelerating drug discovery, enhancing trials, and delivering personalised care, AI yields tangible, measurable benefits. However, it also brings new responsibilities, including protecting data and ensuring fair use. Companies that embrace AI thoughtfully and responsibly will be the ones that lead the way.

The future of pharma isn't just digital. It's smarter, faster, and more connected. And AI is at the center of that change.

 

How Generative AI Is Helping Salesforce CRM

 Sales have never been easy; today, sales and service teams are loaded with tedious tasks. A sales rep might spend half the day drafting emails and logging data, and only a few hours selling. A support agent might hunt through a dozen systems to answer a simple customer question. This is incredibly inefficient. Salesforce reports that only about 28% of sellers expect to hit their quotas — a sign that too much time is wasted on data recording and reporting. Marketing teams face similar struggles, cranking out copy and reports instead of focusing on strategy. The result? Missed opportunities, unhappy customers, and exhausted employees.

The pain is real. When teams are tied in routine work, deals slip away, and customers grow frustrated. Without help, employees spend hours on low-value tasks, leaving little room for creativity or relationship-building. A Salesforce survey found that 71% of marketers believe generative AI could “eliminate busy work,” saving them roughly five hours per week. In other words, they feel crushed by manual chores and see AI as the only way out. Without that extra help, companies risk falling behind more agile competitors.

Fortunately, a new kind of assistant is on the scene. Salesforce has integrated generative AI — the same smart tech behind ChatGPT — directly into its CRM products. Now, your CRM can create content and insights on the fly. Think of it as an AI co-pilot that lives inside Salesforce. It knows your customers and data, and it can draft messages, summarise information, and even suggest actions. For example, Salesforce says its Einstein GPT can “generate personalised emails for salespeople,” “specific responses for customer service professionals,” and “targeted content for marketers.” In practice, this means you could log into Salesforce and ask it to write a follow-up email to a client, and it will produce a customised draft in seconds. This isn’t a generic answer from the internet; it’s built on your own CRM data and company knowledge.

Generative AI acts like a smart assistant built into Salesforce. Instead of writing everything from scratch, you tell it what you need and it writes a first draft for you, all based on real customer data. This frees employees to focus on strategy and relationships rather than busywork. Salesforce calls Einstein GPT “the world’s first generative AI for CRM,” combining its own AI with advanced language models to make every employee more productive.

Generative AI for Sales Teams

Sales reps are experiencing significant relief from all of these tools. Salesforce’s latest Sales AI capabilities can auto-generate emails, schedule a meeting, and set next steps for you. Just say to your CRM, “Send an email about cross-selling product X to company A,” and a few seconds later, receive a ready-to-send draft with the correct tone and specifics. AI is also able to scan previous calls and notes, extracting important details, and it saves time and effort. For instance, Einstein AI Insights transcribes and summarises sales calls in a matter of moments, alerting you to customer objections, competitor names, or crucial pricing conversations. It even examines your pipeline: generative AI reviews open opportunities and other factors to determine which deals are likely to close. The result? You’re spending less time guessing which accounts to pursue and more time engaging high-potential buyers.

In reality, those AI assistants shift things. Salesforce finds that with Einstein GPT, reps are spending less time on data entry and more on actually selling, which equals faster outreach and more closed opportunities. For example, Einstein GPT leverages real-time data to create “highly personal, relevant prospect emails” so teams can spend less time typing and more time engaging with clients. It also qualifies leads automatically, so you only speak with the right contacts at the right time. In fact, industry analysts point out that companies using AI are already opening up 30% of employee time for value-added activities. Sales teams are free to use that extra time developing key relationships or coming up with winning plans instead of fighting with tasks that AI is designed to perform.

Intelligent Customer Service

Service teams also have an advantage. Support agents usually get repetitive queries and complicated case histories. Now, with generative AI, support agents are given an assistant. Einstein GPT for Service is capable of authoring knowledge base articles based on previous case notes and even “automatically generate personalised agent chat responses” to clients. In short, rather than beginning from scratch, an agent is provided with a customised reply within a matter of seconds and is able to modify it according to their needs. This accelerates assistance and makes responses consistent.

The numbers are already remarkable. Salesforce’s AI-based service tools reduced response times by a huge margin at Pentagon Federal Credit Union (PenFed). PenFed integrated its core banking infrastructure and member data into Salesforce and deployed AI-powered chatbots and virtual agents. Now, 60% of member queries go through those AI-based channels, so members receive instant answers on their own. Chats and bot interactions surged 223%, and first-call resolution increased by 20%. In a nutshell, customers receive answers sooner, and human agents handle tougher issues. PenFed’s experience demonstrates what’s possible with generative AI for transforming the support centre into a smooth-running, largely self-service operation.

Machine-Driven Marketing and Personalisation

Marketing teams are constantly being pushed to create content and campaigns — an ideal application for AI’s imaginative capabilities. Einstein GPT for Marketing can “dynamically generate personalised content to connect with customers” for email, social, web, and ads. Require three alternative subjects for a series of customer segments? AI will write them. Need a draft of a promotional text or advertisement copy? AI can do that too. Because it’s based on your Salesforce data, the tone and promotions can be customised based on actual customer interest. That’s a massive time-saver. Salesforce’s research established that 71% of marketers believe that generative AI will remove busywork for them. On average, they estimate it will save more than five hours a week — essentially a full month every year — allowing them to return to strategic and creative tasks.

Early indications confirm such optimism. Several businesses have conducted AI-based A/B tests and experienced improved engagement. And since Salesforce integrates all of this into Marketing Cloud, content generation is tied directly back into campaigns and analytics. You receive tailored emails or ads nearly instantly, then shape them based on real-time input. More simply put, marketing teams have an AI copywriter and strategist at their disposal, enabling them to deliver a message at the right time to the right individual.

Increasing Team Productivity

Generative AI is for more than just customers — it benefits internal teams, too. With Salesforce owning Slack, they also embedded AI into Slack chat. AI within Slack can summarise lengthy channel conversations, dig through previous messages for answers, and even pull relevant CRM information from Salesforce — simply by asking plain-English questions. This addresses that old “I missed that meeting, now I need to read 500 messages” challenge. Salesforce estimates that AI features within Slack save every user nearly 97 minutes every week. That’s about two additional workdays back in your team’s hands every month!

Real customers validate massive benefits. SpotOn, a retail software firm, reported to Salesforce that during a pilot of Slack AI “Slack AI has accelerated our employees’ work exponentially,” with “significant productivity benefits” for their staff. Anthropic, an AI firm, discovered that users of Slack AI were a “huge productivity increase” — staff could immediately find answers and work on high-value activities. Even within Salesforce CRM, new Copilot functionality allows a sales representative to request a personalised close plan or deal analysis within Sales Cloud, rather than sifting through reports. In all instances, there is a consistent theme: routine internal tasks are taken care of by AI so individuals can work smarter.

Case Study: PenFed Credit Union’s Success Story

PenFed’s experience is a prime demonstration of these advantages at work. As the second-largest US federal credit union, PenFed required quick service and secure data. They leveraged Salesforce’s Data Cloud and MuleSoft to unify customer data, then created AI chatbots within Financial Services Cloud. The chatbots serve up AI-driven responses based on actual account data. The result was staggering: 60% of member complaints are now self-service, courtesy of the chatbots, with total chatbot usage increasing 223%, and first-call resolution rising by 20%. In everyday terms, a member interested in moving money or checking a balance is immediately able to receive assistance from an AI assistant, as opposed to waiting around. This change benefited member satisfaction and also freed up PenFed’s staff to focus on more challenging cases and strategic pursuits.

Conclusion

Generative AI isn’t a magic wand — it depends on quality data and oversight. As the Salesforce AI leader said, “Data is fuel for AI — without high-quality, trusted data, it becomes ‘garbage in, garbage out.’” Many marketers remain cautious; 39% say they don’t know how to safely use generative AI, and many worry about accuracy. That’s why Salesforce includes safeguards like the Einstein Trust Layer and Data Cloud, ensuring AI outputs are based on secure, private data, not random web content. AI suggestions should be treated as first drafts, reviewed and refined before use. With strong data governance and training, the benefits outweigh the risks.

We’re entering a new era of CRM — moving from passive data systems to AI-enhanced platforms that assist creatively and analytically. Companies already use these tools to save time and boost engagement. The key: start with a secure, focused rollout (e.g., email drafts), then scale once results are clear. As CEO Marc Benioff puts it, every company will undergo an AI transformation. Generative AI in Salesforce empowers people — it helps sales, service, and marketing teams spend less time on busywork and more time delivering value.