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Author: Amy Saunders

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Use cases are sexy – or at least they should be.

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Recently, I co-led a workshop series on omnichannel orchestration use cases and was reminded how pivotal they are to both business and user success. Beyond defining requirements, use cases cut through ambiguity, align teams, and accelerate value realization. The group agreed resoundingly that gaining a deeper understanding of how different functions contribute to the overall solution helped in decision making about alignment. To me, the rare level of shared vision, commitment, and engagement was a great example of how important cross-functional collaboration is to project success.

At a Microsoft + EPAM event on AI that I attended, three main foundational friction points were called out: infrastructure readiness / maturity, having well defined user/customer experiences as a compass, and aligning on the right use cases.  

So what are the ‘brilliant basics’ of use cases? 

Use Case Visual_IxDF

Why: Use cases can be “sexy” because they transform abstract concepts into tangible, high-value solutions. They help translate business needs into actionable requirements, bridge the gap between technical and business teams, and provide clarity on priorities, sequencing, and desired outcomes.

What: A use case describes how people (“actors”) interact with systems (technology / data / AI) to achieve a specific goal. Through both visual and narrative formats, use cases reduce complexity, validate real-world viability, and turn vague ideas into documented business, data, and technology requirements. They provide enough detail for planning and decision-making without getting lost in implementation specifics.

When: Early and often. Complex solutions require thoughtful planning, making early collaboration essential. Bringing stakeholders together at the start helps avoid rushed decisions, misaligned priorities, and suboptimal adoption. Regular check-ins keep teams focused on user needs, business KPIs, and desired outcomes. 

Who: People are the secret sauce. Successful use cases require stakeholders to align on objectives and expectations – failing to do so often creates friction and weakens outcomes.  Development teams gain a clearer understanding of the business problem they are solving, helping to prioritize and design more effectively. Business teams gain insight into technical considerations, enabling smarter investment decisions and more realistic planning. The result is a stronger partnership focused on shared goals.

Asking the right business questions is always the best place to start, which then shapes effective use cases.  Taking the time to bring cross-functional teams, and leadership, along in the journey is equally important. Having internal talent that are fluent in user stories and use cases, and that understand customer experience, is key. Including change management and optimization processes as part of execution and continuous learning helps in achieving sustained, repeatable and scalable solutions.

Buyer Beware – Contract Clarity

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Does this sound familiar? “What did we buy? What does our scope include? Why aren’t we getting what we need? Who owns this contract? What are the renewal terms and costs?”

Often asked for feedback on various technology platforms, I have encountered an assumption of vendor fault or the flawed technology. Invariably I push back and ask if there is clarity over what was bought, why and how it will be used (use cases), and by whom… then I ask if there is a designated relationship lead or SME connecting across relevant stakeholders.  Lots to unpack here, but the original contract is often a root cause of missed expectations, frustration, or scope creep / complexity.  This is even more of an acute issue when AI is a part of contract scope. I love contracts – truly, and I spend time to get them right in order to avoid pitfalls and scope creep later. Here are my top ‘tips’ (aka scars):

Contract scope:  it’s critical to spend time on the MSA and scope of work (SOW) with the 3rd party – this may significantly impact (positive or negative) long term needs and complexity. If you inherit vendor or agency relationships, take the time to actually read all the agreements! Some basic things to think about on technology / data / AI related scoping: 

  • Deliverables and services:  clearly define what you are specifically ‘buying’ or contracting, associated deliverables, timing, license feels and/or renewal terms.  Ideally build in incentives or disincentives so that everyone has ‘skin in the game’ and is accountable.  Require business reviews and status reports.  Ask for visibility into. and feedback on, feature roadmaps if applicable.
  • Ownership: define who owns what – process, content, data, source code, assets, etc.! Understand if the vendor is including anything proprietary that won’t be included when / if you severe the relationship. Understand what may happen if the vendor company is sold.
  • Stakeholder review of MSA and contacts:  it is critical to involve the right SMEs during the scoping process, which may include input from data / data compliance, customer service, and technology groups in addition to the ‘regular’ stakeholders such as legal, finance, and the end users of the service or product/platform. 
  • Team:  this may seem basic, but require an allocation by function, role and level from the vendor as well as transparency to where and when they potentially offshore or white-label outsourced work.  
  • Support: how many customer service / training hours does the vendor provide to the team, if applicable?  How are SLA tiers (service level agreements) defined? What is the crisis management and/or escalation process? What’s the frequency of team meetings, etc. included in the scope? What reporting or status updates are included and at what frequency?
  • Integrations:  will the new partner systems natively integrate with yours, or are they different from your internal systems? Think about customer experience management related platforms (e.g., analytics, CRM, marketing automation systems like DAM, data/AI modeling and reporting platforms/solutions, etc.).  If it’s not a native API, then negotiate a cap or flat fee for this – for example, native APIs may cost $0-$50k+, but custom work may run $250k+ per integration.  
  • Data, data, data:  it is vital to ask who owns and / or has access to the data, and how will the data will be used, transmitted, or mined.  Make sure your company is ready to receive the data (e.g., your company’s data lake or ‘data estate’ is set-up) and determine if any custom configuration, remediation, or integrations are needed vs. native API’s. Other data related questions: will the Compliance function report on / monitor this data, and if so, is this included in the SOW? Will the provider include opt-out suppression with their database if applicable?  How are they keeping the data compliant and clean? Has the vendor ever had any data breaches? Etc. 
  • Exclusivity:  if there is an investment stake, how exclusive is it?  What benefits will you get that others do not?  How are competitive conflicts handled?  Will competitors have access to randomized data and insights that includes your programs? What does the revenue-share look like over time and what is the tiering?  Will you get first right to test and adopt new roadmap features as well as visibility and input into the partners roadmap?
  • Scale:  if applicable, how transferable is the initial program or pilot to the next use case?  What part of the program systems, content, and infrastructure can be templated?  What’s the cost to scale and are there tiered discounts?). What is expected of local market resources, headcount, and technology?
  • Compliance:  define the local / global compliance process, risk, and accountability.  How will the vendor confirm and comply with your systems, policies, and reporting?  If patient related, how will the partner monitor community or social media comments for AE’s/SE’s? Require the partner to comply with local government or healthcare system compliance requirements, depending on relevancy in the project scope.  
  • Assets:  my top complaint in contracts is that companies neglect to require the vendor to automatically provide copies of associated source code/files as permitted contract wise, downloads of all content and creative, etc. at the conclusion of development (as applicable or allowable).  When related to content, this should no additional cost (often companies end up not being able to track this down or paying for it again later).  Require that the vendor automatically upload all content assets into a digital asset management (DAM) system per their taxonomy and inclusive of all rights management terms / documentation.  Not doing this can result in delays, legal risk, and added cost. 
  • Stability:  understand how the contracting partner is funded and how secure they are financially. Be clear on implications and accountability if the company goes under, merges, or is acquired.

Yes, the above is a long and ‘dry’ list – but the devil is in the details.  Accountability is on both sides – the client or purchaser and the vendor or supplier.  Taking the time to get the minutia right will set up for a positive outcome and partnership.  What can you do in addition to the above tips?

Use AI to: scan and synthesize prior RFPs and/or contracts – in your area but ideally across your company, inventory existing technology and/or software/SaaS based licenses to the RFP use cases you are targeting, align user requirements and prioritize use cases. Carpe diem!

Think of AI as a ‘Swiss Army Knife’, and not a shiny penny

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AI and GenAI have begun to deliver value across functions, enabling (per Deloitte) “medtech companies to achieve cost efficiencies of 6% to 12% of their total revenue in the next two to three years.” Yet of the 90% of companies invested in AI, only 40% saw gains over three years and only 60% were heavily invested – according to a U. Penn / MIT study. 

And yes, AI holds infinite promise and opportunity and will only constantly increase and evolve.  As with innovative advances though, the technology is a ‘tool and not the goal‘, and often going all-in does not guard you against complexity, risk, and sub-optimal return.  Professor Kartik Hosanagar, Co-Director, AI at Wharton, Professor of Marketing notes: “…instead of exhaustively looking for all the areas AI could fit in, a better approach would be for companies to analyze existing goals and challenges with a close eye for the problems that AI is uniquely equipped to solve.”  

This is the ‘Swiss Army Knife’ approach – being nimble and efficient and practical… and not chasing a ‘shiny penny’ that may bring slim impact, poorly use cases, or requiring change that organizations are ill-prepared to meet or sustain.

Narrowing in on marketing, key use cases for ‘pragmatic AI’ include:

  • Anticipatory analytics and optimization:  shifting from manual and/or predictive content to machine learning anticipatory based design driven campaigns through data modeling across content types, formats, markets, and messaging to constantly optimize and project the highest quality and performing customer engagement elements tailored to the right combination of market factors / audience stages.
  • Accelerated content production:  leveraging deep learning models at global scale to radically simplify the content supply chain and go-to-market timeline while decreasing cost / complexity and increasing effectiveness. And yes, integrating ChatGPT where it makes sense to jump start the process.
  • Creative testing:  the real-time rapid assessing of (and conduct back propogation finetuning) visual creative and copy elements including live pre-testing, current and historical campaign analysis, and cross-product / campaign performance sounds simple, but would be a huge and constant win.
  • Translation and transcreation:  this should be a no-brainer and a huge solution to a glaring pain point – adapting content across languages to be culturally accurate and relevant while eliminating complexity.
  • Med, Legal, Regulatory (MLR) review expedition:  gutting the ‘spin’ cycle of MLR via AI reviewed claim information, common language (and variances) in promotional copy and visuals across markets, tiering potential risk and corresponding solutions.
  • CRM holy grail:  and with the above, being able to truly have authentic, hyper-personalized, and optimized CRM across all channels including field force, while keeping up with the speed of culture and identified customer preferences and attribution models – wow!
  • Agentic AI customer service:  this warrants a dedicated posting, but the possibility of intersecting customer service concerns, feedback, or barriers to care represents exponential opportunity and value.

The deployment of AI in the life sciences brings a move towards the automation of routine tasks at scale with increased quality and decreased risk/complexity.  As I learned in the Wharton Online ‘AI for Business’ curriculum, the seismic pace of AI also helps organizations achieve this with less resource needs in the more repetitive or complex data-based modeling aspects, but also will necessitate new types of talent such as humanistic technologists that can pick up where automation falls short. 

The salient outcome is in enabling organizations and healthcare professionals to focus on more complex patient care needs, access, and positive outcomes. 

Beware of the Shadow (Org)

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As Punxsutawney Phil’s groundhog prognostication nears on February 2nd, I thought it fitting to focus on ‘the shadow’ (org) conundrum which for many re-occurs as its own vexing ‘groundhog day’.

Perhaps one of the sure-fire signs of looming gloom & doom (and potential failure), is the shadow organization.  This can take several forms from passive to aggressive, from good intentions to being a harbinger of ‘death’ for teams, tasks, and organizations. I’ve rarely seen a positive outcome from shadow orgs or efforts.

WHAT is a shadow org? A shadow organization is a hidden part of an organization that is not widely known / discussed or necessarily aligned to. It can be made up of informal relationships, unwritten rules, and hidden influencers. And/or, they can be large scale efforts that are embedded formally in ways of working or possibly in parallel to current (and likely duplicative) processes and operations. In all cases, they often include alternative systems, ways of working, and talent or solution providers.

WHY are they employed? Aside from confidence issues, generally shadow orgs exist because results, speed, or alignment are lacking (or all three) and are often spun-up with consultancies working ‘on’ and not ‘in’ the business, often for exorbitant costs and resources. On a smaller scale, specific people, teams or functions may use non-sanctioned partners or technologies to circumvent barriers to achieve goals. Again, this can be for good results/intent and not necessarily bad, such as standing up what ‘good looks like’ or achieving urgent go-to-market / launch needs. It is critical to make the distinction from pilots, which are vetted, aligned, and ideally embedded initiatives designed to prove out a use case and solidify KPIs, strategy, and operational imperatives for adoption and scale.

The WHO…is often the key problem. Unlike pilots or tests, shadow orgs can be fatally flawed for many reasons rooted in inadequate management and governance, which is a derivative of people:

  • Insufficient (or non-existent) alignment, process / workflow design, or talent
  • Not reporting to the appropriate functional lead/team
  • Lack of vetting, inclusion, and traction with key stakeholders
  • Being designed by consultants or groups lacking in org or capability expertise
  • Murky commitment or understanding of what it takes to be successful, and how long that may take

HOW are shadow orgs disruptive? Without thoughtful design, governance, and proper management shadow organizations can be disruptive to company life, performance, and culture – not to mention impact. ‘Negative norms* can run rampant within companies as a result, often undermining company culture, performance, and potentially talent retention. As boundaries are crossed, and functional roles and systems are duplicated, a ‘downward spinning spiral‘ can emerge, mystify, and cause complexity. At a simplistic level, the existing or origin function or process can become obscured or lost – and ultimately may conflict with the shadow org intent.

WHEN to take action: The best ‘Rx’ is to contract within teams and leadership to avoid this pitfall and know when to spot shadow orgs or dynamics early and diffuse or contain them with systemic and sustainable solutions.  Urge leadership to have confidence in, and support, the longer-term picture with an ‘above the line’ path to get there with proper resources. This may indeed require outside support (not substitution) and change which is by far less scary and likely more effective in having a desired impact ‘stick’ and scale.

Transformation and ‘change resilience’ requires people working openly with a collective success, grounded in data-driven performance. This includes identifying and upskilling the talent that will operationalize and scale the capability or function. Not doing so may bring, in groundhog-day terms, may bring more long winters than early springs, as well as that ‘groundhog day’ deja vu!

BMW Credit Card

Connecting the Dots

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One of the most annoying Cx ‘leakages’ I find is not connecting to existing value – which is more than a missed opportunity, it is unrealized impact. After focusing on the right questions to solve for the business and customers, it is vital to connect (all) the dots along the customer journey.  One of my top non-pharma offenders, which is also one of my top personal brands, is BMW.  This is a brand with a high lifetime value, ample resources, and enduring if not hallowed brand love – so why can’t they get out of their own way and have a consistent and connected customer journey?  

A recent example:  people are literally blown away that my BMW credit card reward points can be applied to car payments (brilliant!) – we haven’t had to actually make a payment in two years!  A friend actually purchased a BMW for the first time because of this, but when she went to several dealerships for test drives – no one knew about it and she literally had to ‘Google it’.  BMW does very little cross-marketing of this, not even at the point-of-sale, much less as an acquisition driver. The credit cards are (in theory) a shining example of product relevance – tailored offerings for cars vs. motorcycles, discounts or credits on service, offers for performance driving courses or classes, but the potential is obscured and under-realized.

Other personal examples:

  • Promo mailers for service about cars no longer owned.
  • Financing offers on cars purchased outright.
  • Knowing (far) more about a new model than dealership staff.
  • BMW loaner cars only being given after purchasing a new BMW, and not after used BMW car purchases with higher mileage and in need of trade-in (even though my husband has restored and/or bought about 50 BMWs).  
  • Dealerships not knowing about or understanding European delivery (no longer offered), even though it is/was one of the highest forms of brand love and (fantastic) immersive brand experience.
  • When asked to test drive a manual (stick shift) car, the sales reps replied, “Oh you don’t want that, it’s a lot of work”, even though I brought up the BMW ‘art of motoring’ brand narrative.

What’s the fix? The brilliant basics, but across and inclusive of all touchpoint silos internally and externally. This means: thorough content audit and customer journey mapping, and in particular – aligning your data into a cohesive data lake with a well thought our analytics / AI model. This takes: a champion with visibility and ideally governance across silos, capabilities, and Cx engagement planning inclusive of content execution and ongoing optimization.

Look, I love this brand and even almost worked there so I could tackle these challenges.  Consumer goods customers should in theory never expect to know more about a product offering than the front-line sales representatives.  Looping back to the patient perspective, patients don’t generally expect to know more about products and conditions than physicians or pharma co’s, so we have to work much harder as an industry to help them and to connect the dots to all the value we can provide beyond the product itself.    

Cracking the CX Code: Transforming Customer Experience in Pharma and Med Devices

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Why is Customer experience (CX) so allusive? Why has the healthcare industry always lagged in connected end-to-end CX outcomes? Which companies or brands are fearless in CX actualization and innovation, and what can we learn? Why is sustained and systemic organizational change so hard?

Of the 30% of companies that achieve the intended company or customer transformation, only 5% hit it out of the ballpark1. Compounding this stark reality, according to BCG2, “the share of innovation-ready companies was 20% in 2022; and was 9% in 2023”.  When patient outcomes are at stake, curating a personalized, meaningful experience in the micro moments that matter is an overdue cost of entry. Studies show that improved CX can lead to a 15-20% increase in sales conversion and up to 3x shareholder value growth. However, pervasive challenges impede progress, particularly in healthcare.

Eighty percent of healthcare professionals attribute impact to CX, but with 89% of U.S. patients turning to Google before consulting a doctor3, the importance of connected, meaningful customer experiences has never been more critical. Many organizations face relentless pressure to do more with less, underperforming campaigns, and an inability to scale. The pharma industry, for example, has consistently lagged in achieving end-to-end CX transformation.

I’ve experienced firsthand the never-ending barriers obstructing lasting progress: agencies often work on the business but not in it, underutilized technology systems, operational silos, ‘anti-bodies’ to organizational change, and talent challenges. To break through, leaders must embrace a holistic strategy combining talent, systems, and innovative capabilities to truly crack the CX code and drive results. Many companies do achieve success in Cx, but generally this may be partial (e.g., campaign or audience specific) or under-realized in impact.

I’m a continual learner, and seek to apply what’s working well and obsess in optimization. In starting this blog I hope to share (and learn) pragmatic, prescriptive, and precise customer experience paths in bite-sized, focused topics and examples. Let me know your thoughts!

Sources:

1 McKinsey: https://www.mckinsey.com/capabilities/transformation/our-insights/common-pitfalls-in-transformations-a-conversation-with-jon-garcia

2 BCG:  How Companies Can Get Their Innovation Mojo Back – https://www.linkedin.com/pulse/how-companies-can-get-innovation-mojo-back-boston-consulting-group-mtase/

3Eligility: https://ictandhealth.com/news/4-tips-for-doctors-who-see-patients-already-diagnosed-by-dr-google#:~:text=According%20to%20a%20report%20by,to%20do%20with%20medical%20knowledge.

Asking the right questions

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Customer experience can be a bottomless pit of opportunity and complexity.  Getting to the bottom of it (or going in the right direction at least) is directly tied to asking the right questions early and often – so let’s start there. Using pharma terms, there is a ‘fair balance’ product label implied in the efficacy, side effects and adverse events that can come with large scale CX transformation:

  • Efficacy:  What are the clear benefits? Did you conduct enough clinical trials and testing before launching? How will you measure or prove success? Will it keep working as intended?  Are people (companies/customers) taking it as directed? Is your team focused on outputs and not outcomes?
  • Side effects:  Is there a clear, consistent understanding of what customer experience is and what it takes? Is the internal gauntlet of alignment or delivery preventing or delaying success?  Do you have a good plan or strategy but lack the funding, resources, or technology/data to deliver? Is right talent in place, and do they believe in the vision? 
  • Adverse events:  Is the transformation required to achieve targets or needed change causing new symptoms or a fatal lack of confidence? Are key stakeholders or leaders developing ‘antibodies’ to the process? Are local market teams going ‘off-label’ and prescribing their own solutions or ways of working? Does leadership at the top see the tangible gains or is impatience germinating ‘org-design over-haul syndrome’?

So many customer experience moments and outcomes (good and bad) are shaped by the questions asked, or not asked, early on. The well-known Einstein quote applies here: “If I had an hour to solve a problem and my life depended on the solution, I would spend the first 55 minutes determining the proper question to ask… for once I know the proper question, I could solve the problem in less than five minutes.” Asking the right questions seems easy, but it’s the most common mistake I have seen from small to large companies – it is a skill set that takes EQ, and not just IQ, and hands-on experience.

Upcoming post topics:  change resilience, defining customer experience, marketing as a science, connecting the dots with data and technology, putting the customer at the center.