Healthcare executives implementing AI face a fundamental problem: how do you plan effectively when the technology is changing faster than your planning cycles? As Dr. Nigel Smart puts it, “We’re going to generate more technology in the next year than we’ve developed in the last 100.” Traditional planning approaches simply can’t keep pace with this rate of change. To address this challenge, Dr. Smart proposes a different approach he calls “Future Back Thinking.”
What is Future Back Thinking?
“I like to use a technique which we call Future Back Thinking,” explains Dr. Smart. “Future Back Thinking helps you develop foresight so you can come out with new, more interesting strategies going forward.”
Most strategic planning starts with today and projects forward. That approach breaks down when technology changes faster than you can implement your plans. With AI capabilities “doubling every 90 days,” according to Smart, traditional planning becomes nearly impossible.
Future Back Thinking flips this around. You start by imagining yourself several years in the future looking back, then figure out what decisions and capabilities you needed to succeed.
The Mechanics of Future Back Thinking
Dr. Smart outlines the core methodology: “If you were five years in the future and you could look back to today, what type of information would you tell yourself? You’d say, ‘Don’t go down there’ or ‘Think about this. Don’t discount this.'”
This mental time travel exercise provides strategic clarity through three fundamental questions:
- WHY are we pursuing this implementation? (Purpose and vision)
- WHAT specific capabilities and outcomes are we targeting? (Strategic objectives)
- HOW will we develop and deploy these capabilities? (Tactical execution)
The power of this approach lies in its ability to separate signal from noise. By anchoring strategic thinking in a future perspective, healthcare leaders can more clearly identify which present-day AI technologies and implementation approaches have genuine long-term value versus those that might simply represent transient trends.
Developing Wisdom Before Experience
One of the most compelling aspects of Future Back Thinking is what Smart describes as “developing wisdom by asking, ‘Why did we do that? What did we do, and how did we do it?'”
This framework acknowledges a fundamental truth about strategic decision-making: wisdom typically comes after experience, often summarized in the phrase, “I wish I’d known 20 years ago what I know now.” Future Back Thinking attempts to reverse this sequence, creating a form of “pre-experience wisdom” through structured imagination and critical questioning.
For healthcare organizations contemplating significant AI investments, this approach offers a way to identify potential pitfalls and opportunities before committing substantial resources. It creates what might be called a “wisdom shortcut”—allowing leaders to benefit from lessons they haven’t yet directly experienced.
How Healthcare Organizations Can Use This Approach
What does Future Back Thinking look like in practice for healthcare AI? Here are real applications based on Dr. Smart’s framework:
1. Getting Ahead of Regulatory Changes
Instead of just meeting today’s regulations, ask: “What will regulators require five years from now?”
A smart healthcare organization might recognize that AI transparency requirements will likely increase. Rather than implementing black-box algorithms that work well today but might face regulatory scrutiny tomorrow, they’d choose more explainable approaches that might require more initial effort but won’t need to be replaced later.
2. Workforce Evolution Planning
Instead of focusing exclusively on how AI can replace current tasks, Future Back Thinking examines: “In five years, what will be the most valuable human contributions in an AI-enhanced healthcare environment?”
Smart emphasizes this forward-looking workforce perspective: “What we have to do now is detach ourselves and focus on where our gifts are.” This might lead organizations to invest not just in AI systems but in parallel programs that develop uniquely human capabilities like complex judgment, empathy, and creative problem-solving that will complement rather than compete with AI.
3. Anticipatory Workflow Design
Rather than simply automating existing workflows, Future Back Thinking asks: “What entirely new workflow models will emerge in a mature AI environment?” This might reveal opportunities to fundamentally reimagine care delivery models rather than incrementally improving current approaches.
Smart illustrates this with his experience in pharmaceutical manufacturing: “We’ve done quite a bit of work on using smart screens so that things that need to go back to manufacturing can be communicated digitally and visually from the lab.” This integration represented not just a digitized version of existing communication but an entirely new workflow paradigm.
4. Strategic Resource Allocation
Rather than spreading AI investments across all operations, Future Back Thinking helps prioritize: “In five years, which AI applications will have proven most transformative for patient outcomes and organizational sustainability?”
This future perspective might reveal that certain applications—perhaps predictive analytics for preventive interventions or AI-assisted diagnostic systems—deserve disproportionate investment compared to other uses cases that might seem equally promising from a present-day perspective.
Making It Work: A Four-Step Process
Here’s how to put Future Back Thinking into practice, based on Dr. Smart’s approach:
Step 1: Picture Your Future
Gather your team and imagine it’s five years from now. Be specific about:
- Which AI tools actually delivered value and which were hype
- What new regulations have emerged
- How staff roles have changed
- What surprised you along the way
This works best with diverse voices – doctors, nurses, IT staff, administrators, and even patients. Their different perspectives will help you spot both opportunities and pitfalls that specialists might miss.
Step 2: Backward Mapping
For each future scenario, map backward to identify:
- Key decision points that led to positive or negative outcomes
- Critical capabilities that enabled successful implementation
- Early warning signs that indicated which pathways would prove viable
- Resources and partnerships that played pivotal roles
Smart emphasizes the importance of asking: “What am I going to do now instead?” This question forces consideration of alternative pathways rather than simply accepting apparent inevitabilities.
Step 3: Present-Day Implications Analysis
Analyze the backward maps to determine immediate implications:
- Which current initiatives should be accelerated, reconsidered, or redirected
- What capabilities need development priority
- Which partnerships and resources require cultivation
- What governance structures need establishment
- How to sequence implementation to build momentum through early successes
Step 4: Adaptive Implementation
Develop an implementation approach that incorporates:
- Regular reassessment of future scenarios as new information emerges
- Clear metrics for evaluating progress against the backward map
- Deliberate experimentation to test key assumptions
- Mechanisms to capture and distribute learning across the organization
Smart emphasizes the importance of creating “dopamine hits” through early wins: “We gave them easy wins… Why? It gives them a dopamine hit… And if you give them enough hits like that… they can feel proud of what they’re doing.”
This recognition of the human psychology of change represents a crucial element of successful implementation. Future Back Thinking doesn’t just inform what to implement but provides guidance on how to implement in ways that build organizational momentum.
AI-Enhanced Future Back Thinking
Interestingly, AI itself can enhance the Future Back Thinking process. As Smart observes, “We try to do permutation analysis when we do problem solving, and we use chat GPT, which is super simple for anybody to use. And you can use that to generate permutations so that you have options.”
This suggests a recursive relationship between AI implementation and Future Back Thinking:
- Use Future Back Thinking to develop AI implementation strategy
- Leverage AI to enhance the capability for Future Back Thinking
- Apply these enhanced strategic capabilities to further refine AI implementation
This virtuous cycle represents a sophisticated approach to technological change management that leverages both human foresight and artificial intelligence.
Navigating Uncertainty: The Test-Learn-Adapt Cycle
Smart recognizes that no future projection will be perfectly accurate. He advises: “You’ve got to have conscious awareness of what’s changing around you, and then you’ve got to be adaptive to that new environment. But ultimately, like anything else, test, test, test.”
This emphasis on testing acknowledges the inherent uncertainty in any future projection. Future Back Thinking doesn’t eliminate the need for experimentation—rather, it provides a strategic context that makes experimentation more targeted and meaningful.
The approach incorporates what might be called “strategic humility”—the recognition that even the most thoughtful future projections will require ongoing adjustment as new information emerges. This adaptability distinguishes Future Back Thinking from rigid long-term planning approaches that often fail in rapidly changing environments.
The Wisdom to Shape Technology, Not Just Adopt It
Dr. Smart’s Future Back Thinking gives healthcare leaders a practical way to stay strategic about AI rather than just reacting to each new development. By imagining future success and working backward, organizations make better decisions today about where to invest their limited resources.
Smart shares a telling story about this mindset. At a car club meeting, he met a 100-year-old man who was answering Facebook messages on his phone. This man had lived through the invention of cars, airplanes, television, computers, and the internet. When Smart asked him how he adapted to so much change, the man’s answer was simple: “Accept everything. Absorb everything. Don’t resist. Enjoy it, because you cannot stop it.”
But accepting change doesn’t mean passive adoption. Future Back Thinking gives healthcare organizations the tools to shape how technology serves their mission rather than just riding whatever wave comes next.
For healthcare leaders drowning in AI hype and possibilities, this approach creates something invaluable: the clarity to distinguish between what’s possible and what’s actually worth doing. This transforms AI from a confusing technical challenge into a strategic opportunity to build the healthcare system patients truly need.
Overcoming Change Resistance: Strategies for AI Implementation in Legacy Pharmaceutical Systems
“People don’t like change. It’s uncomfortable,” says Dr. Nigel Smart, pharmaceutical industry veteran with over 40 years of experience. His observation cuts to the heart of why many pharmaceutical companies struggle with AI implementation despite its clear benefits. Even with potential industry-wide savings estimated between $60-100 billion annually, resistance to new technologies remains the primary barrier to progress.
The “Not Invented Here” Roadblock
When attempting to introduce AI systems into established pharmaceutical operations, Smart has repeatedly encountered what he calls the “not invented here syndrome.” This manifests in responses like:
“We don’t do it that way.” “That’s not the way we do things here.”
“If you go around any of the established, larger companies, it really doesn’t matter which one you pick, they have a folklore and a style of how they do things,” Smart explains. “They don’t like change.”
This resistance isn’t unique to pharmaceuticals, but it’s particularly pronounced in an industry built on standardized procedures, regulatory compliance, and risk management. Companies that have spent decades perfecting their processes understandably question why they should adopt new, unproven technologies.
Why Traditional Change Management Fails
Traditional change management approaches often fall short when implementing AI in pharmaceutical operations for several key reasons:
- Perceived threat to expertise: Scientists and quality specialists who have mastered existing systems may feel their expertise is being devalued.
- Regulatory concerns: Fear that AI integration might compromise validation processes or create compliance risks.
- Workflow disruption: Concerns that implementation will interrupt established workflows and productivity.
- Trust issues: Questions about whether AI can match human judgment on critical quality and safety decisions.
The result is what Smart describes as “pushing water uphill” – expending enormous effort only to make minimal progress.
Smart’s Dopamine-Driven Approach to Change
Rather than focusing solely on executive mandates or technical arguments, Smart advocates for a psychological approach to change management. His method centers on creating what he calls “dopamine hits” – small, early wins that generate positive emotions and momentum.
“We gave them easy wins. We gave them opportunities to get quick wins. Why? It gives them a dopamine hit,” Smart explains. “People like getting a dopamine hit because it makes them feel good, and they feel they’ve had a win.”
This approach recognizes a fundamental truth about human motivation that many technical implementations miss: emotional rewards drive behavior change more effectively than rational arguments alone.
A Spider Web Strategy for Implementation
Smart employs what he calls a “spider diagram” approach to implementation. Picture a spider in the middle of its web:
“You have the first ring, the second ring, the third ring… We gave them easy wins in that first ring, where the spider is.”
This concentric circle approach structures change in manageable phases:
- The inner ring: Small, low-risk implementations with high probability of success
- The middle rings: Increasingly significant applications building on established success
- The outer rings: Transformative implementations that might initially have met resistance
The beauty of this approach is that by the time you reach the outer rings, you’re no longer “pushing water uphill.” As Smart puts it: “You’ve crossed over that bridge, and now you’re not punching any longer. You’ve got a whole bunch of champions all for you for the implementation of the more difficult things.”
Case Study: Transforming a Failing QC Lab
Smart shares a powerful example of this approach in action with a quality control lab that was failing in a major contract manufacturing organization (CMO).
“We remember going in and sharing a plan. We went through with management, and then we sat with everybody – about 30 or 40 people in the analysis lab. We showed them what we were going to do and how it was going to roll out over the weeks and months ahead.”
The initial reception wasn’t promising. “One supervisor came to me afterwards and said, ‘You know, I don’t agree with this, Nigel, but we’ll give it a go.'”
The team began with simple workflow improvements that delivered immediate benefits to lab personnel. By focusing on what Smart calls “low-hanging fruit,” they generated quick wins that built credibility.
The transformation happened faster than expected. “This was on a Monday morning. By Friday afternoon, he came up to me and said, ‘I was wrong. I’m on board. I want to be one of your internal champions to help get this message to everybody else.'”
In just one week, a skeptic transformed into an advocate. The key was giving people tangible experiences of success rather than simply telling them about potential benefits.
Measuring Resistance: The Line Tool
Smart uses a diagnostic tool he calls “the line tool” to assess resistance levels and track progress:
“We use a tool called the line tool… It’s a cross that has powerful, fragile, anti-fragile, resilience. And you grid it one to ten on both axes.”
This assessment helps pinpoint where different teams and individuals fall on the spectrum from fragile (threatened by change) to anti-fragile (strengthened by change). Smart notes: “You find out where people are. You take the temperature in their current state, and then you say, ‘What do we need to do to get us from a minus two to a three?'”
This granular understanding of resistance patterns enables targeted interventions rather than one-size-fits-all approaches. As Smart explains: “I tell people, look, seven is average, and I want people at eight and nine.”
Beyond Fear: Building “Can-Do” Spirit
Successful AI implementation ultimately requires what Smart calls “the can-do spirit.” This means fostering a mindset where teams are:
- Comfortable with uncertainty
- Willing to experiment
- Focused on possibilities rather than limitations
- Ready to learn new skills
“You have to get used to being comfortable with being uncomfortable,” Smart advises. “I know that sounds like an oxymoron, but you’ve got to be comfortable with the fact that you’re going to have to change very frequently.”
This psychological preparation is especially important for AI integration, which represents ongoing transformation rather than a one-time change. As Smart points out, AI is “doubling every 90 days,” creating a context where adaptation must become continuous rather than occasional.
Practical Strategies for Pharmaceutical Leaders
Drawing from Smart’s approach, here are practical strategies for pharmaceutical executives facing resistance to AI implementation:
- Start with workflow pain points Instead of focusing on AI itself, identify specific workflow frustrations that technology could address. Smart’s example of using digital screens to communicate lab results to manufacturing demonstrates how targeted solutions to existing problems build support more effectively than technology for its own sake.
- Build a “show, don’t tell” culture Create demonstration projects that people can experience firsthand. Smart emphasizes: “Staff on the shop floor, once you get them motivated, they love it. But initially, you’ll get that ‘it’s a management flavor of the month’ response.”
- Establish clear metrics Choose metrics that matter to different stakeholders. Smart shares how one implementation “could actually increase two more batches a year out of the same plan, just by having things digitally integrated.” Production increases are tangible benefits that resonate across the organization.
- Create internal champions Identify respected voices who can influence their peers. The supervisor who initially resisted but then became a champion illustrates how converting influential skeptics creates powerful momentum.
- Address the human value proposition Directly address fears about job displacement by clearly articulating how AI enhances human work rather than replaces it. Smart’s framework of “AI with human agency” emphasizes that “AI can’t tell you how or what type of analysis strategy you want to perform. That is the human agency.”
When Resistance Becomes Opportunity
The most powerful insight from Smart’s approach is that resistance isn’t merely an obstacle to overcome – it’s valuable feedback that can improve implementation. When people resist, they often reveal important considerations that purely technical approaches might miss.
“We involved them in the day-to-day decision making,” Smart explains. “One of the things was, how do we improve operations around the lab? What are the problems?”
By treating resistance as a form of engagement rather than opposition, implementation becomes a collaborative process that produces better results. The people who initially push back often become the most valuable contributors once they feel heard and see early evidence of success.
The Future-Back Perspective
Smart connects his change management approach to a broader strategic framework he calls “future-back thinking” – imagining successful future states and working backward to identify the steps needed to achieve them.
“If you were five years in the future and you could look back to today, what type of information would you tell yourself?” he asks. This perspective helps teams focus on long-term benefits rather than short-term disruptions.
For pharmaceutical organizations implementing AI, this future orientation provides crucial context. The temporary discomfort of changing established processes becomes more acceptable when connected to a compelling vision of enhanced capabilities, improved outcomes, and competitive advantage.
Conclusion: Change Management as Competitive Advantage
As Smart observes, change resistance isn’t just a barrier to specific AI implementations – it’s a fundamental constraint on organizational performance. Companies that master the human side of technological change gain a substantial competitive edge.
“My passion is how do we get more efficient? How do you get more productive? How do we get to high performance?” Smart explains. The pharmaceutical companies that answer these questions most effectively will be those that have built organizational cultures where technological adaptation is embraced rather than resisted.
By applying Smart’s psychology-based approach – creating dopamine hits through early wins, building out implementation in concentric circles, and cultivating a can-do spirit – pharmaceutical organizations can transform resistance from an implementation blocker into a valuable source of insight and momentum.
The question isn’t whether pharmaceutical companies will eventually adopt AI – they must to remain competitive. The real question is how much value they’ll capture and how quickly. Those that master the human dynamics of change will maximize their share of the industry’s projected $60-100 billion in annual efficiency gains while strengthening rather than straining their organizational culture.
