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

Written by Amy Saunders on . Posted in Uncategorized. No Comments on Use cases are sexy – or at least they should be.

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.

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

Written by Amy Saunders on . Posted in Uncategorized. No Comments on Think of AI as a ‘Swiss Army Knife’, and not a shiny penny

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.