The ‘Activation Gap’: turning AI daydreams into real patient outcomes

Introduction

AI is undoubtedly changing the healthcare and pharmaceutical landscape, but for many teams, it still feels like a distant or overwhelming prospect. While some organisations may be beginning to experiment with AI tools, many others remain unsure how - or even whether - to begin.

It's not a lack of ambition that holds organisations back. More often, it’s a fear of complexity, confusion around regulation, or uncertainty about where AI would actually add value. Indeed, the path from curiosity to practical impact can seem anything but clear.

But meaningful progress doesn't have to start with bold, transformative moves. In fact, the most effective approaches to AI are often the most grounded:

We help organisations move from abstract ambition to concrete application - connecting AI’s potential to patient impact in ways that are focused, safe, and smart.

The hesitation trap

The hesitation around AI isn’t unique to healthcare. Across industries, many organisations express strong interest in AI, but only a fraction take meaningful steps toward implementation.

This underscores a consistent pattern: intent is high, but follow-through remains limited.

But healthcare and pharma often lag even further behind. While the stakes are high and the opportunities compelling, many teams remain stuck in a cycle of hesitation – interested and willing, but unsure how to begin. This pattern isn’t new. The industry has historically been slow to adopt new technologies, from electronic health records to cloud-based systems, where regulatory complexity and operational risk slowed uptake despite clear long-term value.

AI is seen by some as highly technical, poorly regulated, and risky - and in heavily regulated industries like healthcare, that perception is amplified. Concerns around data protection, confidentiality, and intellectual property compound reluctance, but much of this is misplaced. In reality, many of the necessary regulation already exists and can be applied to AI-related initiatives. The real challenge lies in understanding how to apply them.

Another common barrier is the uncertainty around tools and use cases. Teams often don’t know which AI solutions to choose or how to integrate them into current workflows. This creates a pause point - a kind of cognitive bottleneck - that delays progress. Even where enthusiasm exists, execution feels overwhelming.

This challenge is known as “metacognitive load”: the effort required to not only solve a problem, but to figure out how to solve it, which tool to use, and why. It’s a major hurdle in AI adoption, and one that vendors are now trying to address - some by introducing AI systems that help users select the right tools automatically, based on the task or question they’re facing.

On top if this, practical hurdles, like procurement cycles, cost pressures, and lack of in-house expertise make AI feel high-stakes and low reward. And for many organisations, the underlying digital infrastructure simply isn’t in place, making it difficult to deploy AI in any meaningful way.

While these challenges are real, they’re not insurmountable. With the right support, a clear strategic lens, and a manageable first step, AI can shift from an intimidating ambition to a practical, powerful tool.

Putting purpose before the platform

One of the biggest misconceptions about AI adoption is treating it like a standalone initiative – a technology to be ‘added on’. However, the most successful initiatives begin with a clear strategic focus – not with the technology itself.

Whether it’s improving patient engagement, accelerating internal workflows, or uncovering insights from data, the real value of AI comes from how well it supports these broader goals, not from novelty or speed of adoption.

This is where AI readiness becomes more than a technical checklist. It’s not just about having the right data or systems in place - it’s also about whether the organisation has the foundations to support AI in a meaningful way. Are key processes already digitised? Is data accessible and usable? Are governance structures in place to manage risk responsibly? Without this groundwork, even the most sophisticated AI solution will struggle to deliver.

Equally important is cross-functional alignment. Effective AI adoption requires input and buy-in from medical, marketing, digital, regulatory, legal, and compliance teams. When each function is working from a different set of assumptions, momentum stalls. But when everyone is aligned behind a shared strategy - grounded in patient outcomes, commercial goals, and practical constraints, the right opportunities become much easier to identify and activate.

We work with clients to shift the conversation from “What AI should we use” to “What do we want to achieve, and how can AI help us get there?” With the right framing, AI becomes less about tech and more about transformation: a tool to amplify what your organisation already does well and prop up areas that need greater support.

AI in action

AI doesn’t require radical reinvention. In fact, it is already delivering measurable results in healthcare, often in ways that go unnoticed. You might be surprised to learn that some of the most effective applications are already being used to solve common, practical challenges: accelerating content reviews, streamlining compliance workflows, analysing existing data more intelligently, and improving the way patients navigate digital tools.

These aren’t experimental technologies - they’re already embedded in real-world practice. Tools like Suki and Nuance Dragon Medical One help reduce administrative burden and improve documentation accuracy by transcribing clinical notes. Generative AI platforms, such as Jasper and Copy.ai, are supporting medical and marketing teams create content faster and uncover key insights more efficiently.

Chat-based tools, including Ada Health and Babylon, are improving how patients access information and self-assess symptoms, while AI-powered analytics platforms like Qventus are offering predictive insights and actionable recommendations to guide real-time decisions.

What unites these examples is that they are:

For many organisations, this is the smartest way to start: using AI to improve what already works.

We work with clients to identify use cases that are strategic, safe, and scalable - starting with what matters most to your organisation, your teams, and your patients. From improving website usability to supporting medical content review or deepening data analysis, we help organisations pilot AI in ways that are tangible, manageable, and aligned with their broader goals.

Getting started with AI

One of the biggest myths about AI adoption is that it requires major commitment from day one. In reality, the best way to build confidence with AI is to simply start - not with sweeping transformation, but with focused experiments that generate insight, not overwhelm.

For many organisations, that means identifying pilot projects where AI can demonstrate clear utility, without heavy lift or risk. These might include using AI to simplify internal reviews, automate data queries, or prototype patient support tools.

Taking early action gives your organisation something essential: momentum. A well-designed pilot helps teams learn quickly, build alignment around what’s possible, and generate evidence to support broader adoption. It also helps develop internal capability: many organisations find that each pilot gives rise to an in-house AI champion, someone who grows into the role through hands-on experience.

Crucially, early pilots also signal to both internal and external stakeholders that AI can be implemented safely, responsibly, and with purpose, without overwhelming your systems, budget, or compliance framework.

We help organisations navigate this first wave. Our approach balances ambition with pragmatism - aligning pilot programmes to brand goals, regulatory frameworks, and operational realities. Because when you move early, thoughtfully and strategically, you’re not just trying AI - you’re shaping how it will serve your organisation in the years ahead.

Lead with purpose. Deliver with confidence.

AI doesn’t need to be daunting or distant. The most impactful uses of AI in healthcare aren’t about overhauling systems or chasing hype. They’re about improving what already works, exploring new possibilities with purpose, and staying aligned to what matters most: outcomes, efficiency, and clarity of communication.

Scepticism is understandable. The technology is evolving fast, and the regulations can feel unclear. But that’s exactly why now is the moment to engage - deliberately, strategically, and with the right guidance. Waiting for perfect conditions often means missing the chance to shape what’s coming.

If you would like to discuss the content of this article or a potential AI project then please do not hesitate to contact Claire at claire.dobbs@solarishealth.com

Disclaimer
We use AI to push creative boundaries, deliver impactful work and improve efficiency. We are committed to transparency around AI use and will always use it in line with the terms of client contracts. We use pre-vetted AI tools, audited under the Mission AI Acceptable Use and Guidance Policy, to meet our intellectual property, data protection and client confidentiality standards. If any work is in part generated by AI, we will let you know and advise you of any limitations in fidelity, resolution, adaptability, reproducibility or licensing.

References:
1. Fintech News. (2022) NewVantage Partners Releases 2022 Data and AI Executive Survey. Available at: https://www.fintechnews.org/newvantage-partners-releases-2022-data-and-ai-executive-survey/ (Last accessed: May 2025)