Axtria Ignite 2026: Intangible factors to enable AI success
It takes a lot of things to make an AI deployment work: good technology, good data, the right use case. But, as pharma and medtech companies are finding out, culture, training, and strategy are equally important – and, in fact, may form the backbone of competitive advantage in an increasingly commodified AI world.
“There's a shared belief that technology is not the harder part,” said Asheesh Sharma, a principal at Axtria. “The harder part is the organisational rewiring, the workflow, how you reimagine it, the accountability, the trust – which you need to design and build. And the most important part: what we leaders do is shaping the culture.”
Last month, at Axtria Ignite 2026 in New Jersey, US, AI executives from top life sciences, pharma, and medtech companies came together to share their experiences and get to the bottom of what it really takes to create scalable AI deployments that bring real, measurable ROI. Speakers shared their insights about culture and training, governance and risk management, executive buy-in, organisational cooperation, and more. Here are some high-level insights from the two-day event.
Building a culture of curiosity
One thing that will set AI-capable organisations apart from those that struggle to keep up is culture – wide-scale buy-in and belief in AI, which allows companies to experiment and innovate at every level.
“Whenever we are looking at transformations like this and the culture shift, it puts a lot of stress on the leaders because what you have to do is to shape the culture,” Sharma said. “And it has to be done through a personal commitment, changing your own behaviours, setting an example for others to follow. Then you can see, when you're not in the room, the conversations are shifting.”
An executive from one pharma customer of Axtria stressed, “We do not want anyone to be left out in this process. We want AI to be democratised across the organisation so that there is an inclusivity in what we deploy.” That company deploys Claude licenses to tens of thousands of employees – everyone at the company – and encourages them to “get their hands dirty,” even if some experiments don’t work out right away.
But that creative energy must be tempered with clear goals and prioritisation, and clear guidelines for when to pivot or abandon a failed experiment.
“I'm really expecting a 50% rate,” said the pharma customer with the large-scale Claude deployment. “We are trying a lot of things. My expectation to the Board, to our C-suite members, is we went from having no- to low-AI products, and now we have a plethora of AI products. Yes, the strategy is that half will work, and I'm going in assuming half of them won't. So, the half that works, we will scale it. The half that doesn't work, we'll get learnings, and we will pivot.”
Creating trust in AI systems and leadership
Trust is another important piece – users need to be able to rely on the systems and, as Sharma pointed out, trust is “built in drops and lost in buckets.” Transparency, using good data, and having strong governance are all factors that can facilitate that trust.
“As you build your AI systems, do you have data that is AI-ready?” said Jaswinder Chadha, CEO of Axtria. “Second, do you have the semantic layer so that your AI agents can reliably answer the questions and deliver on what you expect them to do? Third, do you write the specs for your AI agents and have the governance in place in terms of validation? As AI agents or digital workers, do you first hire them as interns and let them actually perform? Because the trust has to be earned.”
It’s important to make sure that the training of the end users and the training of the AI team keep pace with each other, another executive said.
“If you only upscale the analytics AI teams, then you have a Ferrari ready, but people don't know how to drive it. On the other hand, if you have a great, really modern marketing team, but not an able analytics AI team, there will be frustration."
Governing proactively and managing risk
AI brings many benefits, but it can also hallucinate, and AI agents with broad capabilities can run afoul of privacy regulations or cause other legal issues. So, governance and risk management need to be prioritised.
“In 2014, you could get a self-driving car […] but they were still not prevalent,” one pharma executive said. “And it is governance, systems governance, where they failed. My take is that organisations that figure out this governance at scale are the ones that are truly going to accelerate.”
Governance boards need to stop operating as “conventional toll gates” and start working in purpose-built ways to respond to the risks of modern AI systems.
“I think there have got to be a lot of investments around agent observability, agent monitoring, and kill switches for agents. I don't think the industry has evolved governance at the tech level,” he said. “We apply the old technology governance to the latest technology. That is why we slow things down.”
A key piece of advice from one Axtria customer is to make sure that risk management is integrated, looking at information security, data privacy, and AI risks holistically. This can help streamline the process and keep risk management from becoming a major bottleneck to innovation.
Executive buy-in and organisational cooperation
While executives at this point are pretty well bought into AI in general, it’s still necessary to sell them on particular use cases.
“There's no new pile of money that exists in our organisation for AI,” one Axtria customer said. “You are taking it out of P&L to fund it, or you're making trade-offs against existing systems. So, that makes influence a necessity. You have to go and make a case for why you need to stop doing X, and you need to start doing AI or agentic AI.”
One key to that influence is to be a straight-shooter. Let higher-ups know exactly what benefit they can expect from a new technology.
“People are sick and tired of hearing about productivity transformation,” one speaker said. “Show me the bottom line to say which cost centres are reducing and what is the operating margin improvement. If it is top line, show me how it is enabling, directly or indirectly, the top line growth.”
Finally, as with any rapid organisational transformation, there’s a risk of siloes forming, where different teams build new systems that suit their own priorities, but don’t coordinate across the company. Or where new systems aren’t designed to consider what’s already in place.
“You have existing systems that are working. It needs to work with that ecosystem. So, how do you then work it all together?” said one Axtria Ignite guest. “I think that's where most of the pilots will go from being a pilot to a success. Otherwise, it will be very good for the first three months and then, in the end, you don't know how to scale.”
AI is here, and it’s being deployed on sales teams, field teams, and more in large pharma organisations. Now, the line between AI success and failure will increasingly come down to culture, governance, and systems-wide integration.
About Axtria
Axtria helps life sciences companies harness the potential of data science and software to improve patient outcomes by connecting the right therapies to the right patients at the right time. The company is a leading global provider of award-winning cloud software and data analytics to the life sciences industry. We’re proud to deliver proven solutions that help pharmaceutical, medical device, and diagnostics companies complete their journey from data to insights to action, enabling them to earn superior returns on their investments. As a participant in the United Nations Global Compact, Axtria is committed to aligning strategies and operations with universal principles on human rights, labor, environment, and anti-corruption, and taking actions that advance societal goals.
