Michael Wong is an Emeritus Board Member of the Harvard Business School Healthcare Alumni Association.

Michael Wong

Stephanie L. Woerner, PhD, is a Director and Principal Research Scientist at the MIT Center for Information Systems Research. With over 20 years at MIT, Dr. Woerner focuses on how companies use technology and data to create more effective business models and manage the organizational change caused by the digitization of the economy. She received her PhD and MA from Stanford University.

Stephanie L. Woerner, PhD, is a Director and Principal Research Scientist at the MIT Center for Information Systems Research.

 recently shared, “For the past two decades, the biopharma sector has witnessed the shedding of hundreds of thousands of jobs, and all signs point toward an acceleration of these losses.”

 As many of their executives are leaning into emerging technologies, like generative AI, as turnaround panaceas, what are the realities facing their employees?

: As one of your Q&As pointed to my MIT colleague's earlier findings, “...large companies are securing only 5% of their revenues from new digital value propositions;”

 that should temper their board of directors' expectations of such assertions.

Instead, C-Suites, and of equal importance, their boards, should remember that the goal is not digital transformation but rather a profound business transformation. Specifically, if your employees do not have quantifiable value propositions, such as accessing new prospects, retaining existing clients, reducing costs—or other offerings that are valued by external or internal customers—frankly they should not be pursued in 2024.

While this recommendation might be challenging to accept, the good news is that there have been some companies which have focused on outputs and outcomes versus just inputs; and they have created valued business transformation via practical digital strategies. For example, Bayer's Climate FieldView offering provides a simple solution for farmers to make data-driven decisions to maximize their return on every acre.

The FieldView platform provides a central hub of information and visualization tools for farmers to leverage, and this includes connections to dozens of platform partners—from aerial imagery, to insurance, soil analysis, and more. Bayer also recognized that end users do not want expensive and complicated tools that are challenging to integrate into their existing operations. Their starter kit is under $500 and works with over 80% of equipment types. As a result, it is not surprising that this Bayer offering has helped to contribute towards the 15.6% growth that their Crop Science division contributed in 2022. Value creation and simplicity of adoption were two key themes of Bayer's business transformation.

So, for your biopharma readers who have seen challenges in their sector, the great news is that if they can remember that it is not just doing digital but being digital, they can create new sources of value, which are larger than the 5% metric which you had mentioned earlier. And by being digital, I am referring to how employees have a laser focus on creating new digital assets that the target end users will be willing to pay for in terms of external invoices or internal budget transfers. Within the biopharma vertical, an example is AstraZeneca, where the CIO seems to have created this mindset as part of their culture. 

www.workboard.com/results-astrazeneca-cindyhoots/

: Like other Fortune 500 verticals, legacy M&A transactions have created complex fiefdoms across some of these organizations. As a result, wasted resources and the ultimate failure to realize the value from digital in their bottom lines have been common. Given this context, what are your top three recommendations for the readership?

: First, based upon my 20-plus years at MIT and working with leading organizations, build a common language around the various existing and emerging digital terms. With the complexities of global firms, especially those with legacy organizations, employees need to have a common language and agree upon assumptions around these terms. Without this common language, one will likely see the continued failed transformations that my team and I have observed over the years.

Second, to avoid the common pain point of dormant multi-million dollar digital assets that reside within Fortune 500s because employees are not aware that they exist and/or the culture focuses on starting projects from scratch, CIOs along with their CEOs need to take a more disciplined approach towards their digital strategies and readers can learn more about four potential pathways that my colleagues and I recently shared here, 

www.europeanbusinessreview.com/four-pathways-to-future-read-that-pay-off/

Finally, by being digital, most biopharmas need to change their ways of working. The recurrent challenge that many Fortune 500s have with friction between IT and commercial continues as the sales and marketing teams often perceive their tech counterparts as blockers to the speed of transformation. As a result, the inevitable shadow IT systems and duplicate efforts continue.

Per the recent research that my colleagues and I conducted, which included over 1,000 firms, one key observation was how smarter firms enabled sales and marketing teams to be empowered for their unique needs but tempered with a CIO and CEO co-sponsored message that upcoming efforts need to leverage the firm's existing platforms. As CIOs are often peers to their C-Suite colleagues, my last recommendation is for CEOs to take an active role in communicating such expectations.

While this CEO does not have to be an expert in generative AI and other potential technologies, this person is accountable for helping to recalibrate her or his workforce's mindset so that limited resources can enable the firm to be ambidextrous in terms of innovating to engage and delight customers while simultaneously reducing costs by means of readily available technologies. 

Click for more: Update On The Four Pathways To Future Ready.

Michael Wong is an emeritus board member of the Harvard Business School Healthcare Alumni Association.

Mass layoffs continue across the biotech sector, what does this mean for pharma’s future? 

, August 23, 2023. Accessed November 13, 2023. 

https://www.forbes.com/sites/ritanumerof/2023/08/22/mass-layoffs-continue-across-the-biotech-sector-what-does-this-mean-for-pharmas-future/?sh=1d68a4343fa1

2. Wong M. Planning Ahead with Dr. Jeanne Ross. 

. Published March 25, 2020. Accessed November 13, 2023. 

https://www.pharmexec.com/view/planning-ahead-dr-jeanne-ros

Articles

Stephanie L. Woerner, PhD, director and principal research scientist at the MIT Center for Information Systems Research, discusses the outlook for next year among biopharma firms.

Expert: Digital Transformation is Key Part of Biopharma C-Suite Goals for 2024

Michael Wong: As the MIT Sloan Center for Information Systems Research (MIT CISR) has shared, “The goal of a digital business transformation is to improve customer experience and be more efficient; we call the companies that have learned to do both "’future ready.’" These future-ready companies are top performers, reporting estimated average revenue growth of 17.3 percentage points, and a net margin of 14 percentage points above their industry average—a big premium. As you lead the MIT CISR data research, along with the center’s Data Research Advisory Board, what steps should readers take to build such a future-ready culture and organization?

 C-suites need to take the lead by recognizing and communicating that their data assets need to be used in ways that produce financial returns. In other words, they need to ensure that their company’s data assets are monetized. The term data monetization is not a bad term, but a neutral one. All companies—from Fortune 500s to non-profits—are amassing loads of data, and it would be irresponsible not to create value from such an important and prevalent organizational resource. Future-ready companies naturally engage heavily in data monetization to achieve their customer experience and efficiency outcomes. But it’s never too soon for any company, regardless of where they fall on the digital spectrum, to begin learning. Our research found that top-performing companies of all kinds attribute 10 percentage points more to data monetization than bottom-performers.

Smarter organizations appreciate that it takes investment and time to establish and activate a data monetization strategy. For instance, Anthem (now Elevance Health) spent a year laying foundations to monetize its patient health data. It had to deidentify, cleanse, secure, and establish permissioning for millions of patient records to enable researchers, start-ups, and other partners to collaborate on artificial intelligence (AI)-based solutions for disease prevention. The resulting innovation platform led to new capabilities and solutions that the company could use to achieve better patient health outcomes. Data monetization efforts like this one can help companies develop more efficient work, craft better customer (patient) experiences, and even deliver new solutions (via new business models) that can be sold to others in the industry.

Wong: While several C-suites highlight the future potential value of digital transformation at their firms, actually monetizing the assets is often a different story. What are your top three practical recommendations for the monetization of assets which readers can begin deploying during this second half of 2023?

 I’ll give you one practical recommendation: start now. But I will give you three practical ways to make that happen. Here is a simple but effective framework that has resonated with my students and data board. You can monetize data in three ways: improving, wrapping, and selling [as I note in my book, “Data is Everybody’s Business”].

First, improving leverages data to create efficiencies in work from better, cheaper, or faster operations. For your sector, consider how forward-thinking organizations are using data to improve daily operations such as by reducing staffing needs (nurse response optimization), lowering the cost of medical product expenses (spend analytics), and improving hospital recovering times (infection rate reduction). Realizing value from such efforts requires removing or redirecting the slack created by the efficiencies so that they flow to the organization’s bottom line.

Second, wrapping draws on data to enhance an existing offering such that clients buy more, stay longer, or are willing to pay a premium. With the continued changes in the bio-pharma industry such as the recent momentum to broaden access of certain medicines via over the counter versus the historical model of needing a physician’s prescription, how can data and insights motivate target audiences to make that change? Moreover, how can a company leverage data insights to provide new experiences? Think informative patient portals and smart-hospital room interactions.

Third, selling is the exchange of an information solution for some form of money. By doing so, realizing value is straightforward and appears in the form of new financial inflows. Back in the day, selling meant selling data sets. Today, companies prefer to keep and protect their data and instead sell information solutions (patient health monitoring) based on their data assets.

How can you get started learning? Pick a data monetization approach that fits best with your company’s financial needs. Need to lower costs? Consider improving. Need to improve customer satisfaction scores? Consider wrapping. Need to grow? Maybe selling can help. Then, as you develop, execute, and monitor data monetization activities, you will build capability muscle that will serve you well in a “future-ready” world. My hope is that “Data is Everybody’s Business” will be a helpful tool for leaders as they develop managerial acumen in converting their loads of data into fruitful data assets.

Barbara Wixom, PhD leads the MIT CISR Data Research Advisory Board, comprised of data and analytics executives from MIT CISR organizations. The board prioritizes, informs, and participates in data research activities in ways that influence findings and insights at MIT CISR and in the broader global academic community. Dr. Wixom received her PhD from the University of Georgia.

Michael Wong is an emeritus board member of the Harvard Business School Healthcare Alumni Association.

MIT’s Barbara Wixom, PhD, outlines the importance of data-powered research in producing financial success. 

Why Data Should Be a Top Priority

With at least 13 different companies announcing staff reductions resulting in hundreds of layoffs during Q1-2023,1 many biopharma C-suites have doubled down on evolving technologies like generative artificial intelligence (AI) as panaceas to turnaround their fortunes. I sat down with Ayelet Israeli, PhD, a Marvin Bower associate professor of business administration at the Harvard Business School Unit, to discuss the capability for businesses to successfully utilize these technologies.

Michael Wong: Big data, machine learning, next best action, next best experience, and generative AI; if we only had a dollar for every time that one of these terms was referenced in a biopharma’s annual report. Technology buzz words have been around for decades, so, given your research findings over the years, what is the realistic potential for companies to leverage and scale these evolving areas and deliver quantifiable returns?

 At the heart of these evolving technologies is the simple principle of leveraging data for informed decision making; be it from pilot to scaled production. For example, I wrote a case study on how GAP’s CEO considered how they might leverage big data, versus creative directors, to predict what consumers want to wear next.2 And what was interesting to me was to think about which tasks are big data and AI are suited to do, and which tasks are less appropriate for data and AI. As we think about store and supply chain operations, integration between a seamless online and offline shopping experience, restocking sizes across stores, all of those can be better executed with algorithms. However, predicting trends and the success of new products is an extremely challenging task, especially in fashion, and we cannot expect that simply using historical data will help us predict future preferences and trends.

To your point, tech buzz words have been around for a while, but what is exciting to me is that generative AI could be something as big as the first Industrial Revolution. This assertion is based upon my own research and other field studies where large language models (LLMs) have successfully created value for everyday tasks like improving coding, enhancing professional writing (emails), and driving greater end-user self-sufficiency via call centers’ sophisticated chatbots. These examples are no longer just pilots but deployed in some Fortune 500s like Microsoft and Google where they have integrated LLM into office products and Internet Search.

While it is great to hear of the potential, what are your top three recommendations to help biopharma C-suites pragmatically deploy evolving solutions like LLMs and other technologies?

While different industries within the Fortune 500 will have various strategies, I would recommend a three-step playbook for your biopharma colleagues given the safety aspects of this vertical.

First, as many of these firms have thousands of employees spread around the globe, their C-suites need to provide simple but clear directives around what their teams can and cannot do around generative AI. Frankly, given the risks of well-intended but dangerous employee actions (for example, around data security and privacy and protection of proprietary data), I would recommend that most employees are banned from using generative AI on their company-provided computers. While there might be some test areas that are closely supervised, start small during this learning process. Assign specific teams that will evaluate different use cases across the company and see where these new tools can be deployed responsibly.

Second, provide continuous training and explorations around these new technologies for your employees. While it is great that many people have enthusiasm around the potential to use LLMs and reduce the time to bring life-saving drugs to patients, it is crucial to consider that many of the generative AI tools that we are seeing these days (such as ChatGPT) were not meant to reveal the truth or to display correct knowledge. Rather, LLMs were built to generate content like text that displays the words that are most likely to come next. In turn, one cannot expect the outcome to necessarily be true statements. Managers and employees need to understand the risks that LLMs are known to make things up or hallucinate and therefore cannot be used without audits for correctness.

Finally, invest in change management and communications during this journey. For the former, recognize potential biases if pilots are being trained on data with underlying biases contained in them, and figure out how to circumvent these issues. For the latter, continuous C-suite communications of expectations of behaviors with ideally the CEO’s and the CIO’s or CDO’s signatures on the email should be the norm. This simple approach will help set the guard rails for the entire organization to carefully crawl, walk, and eventually run with these powerful new tools to drive profitable growth.

Layoffs Continue to Batter Biotech, with Big Pharmas Piling on the Pain,

https://www.youtube.com/watch?v=zsh7opE4QZE

Ayelet Israeli is the Marvin Bower associate professor of business administration at the Harvard Business School Marketing Unit. She is the co-founder of the Customer Intelligence Lab at the D^3 institute at Harvard Business School. She received her PhD from Northwestern University and her MS, MBA, and BS from The Hebrew University of Jerusalem.

In this latest Harvard Business School Healthcare Alumni Association Q&A, Ayelet Israeli, PhD, explains the potential—as well as limitations—of these offerings and pragmatic guardrails for deployment in organizations. 

Leveraging Data for Informed Decision Making

Michael Wong: As your book mentions, the 60-plus economy is vibrant and, in the US, it presents an $8.6 trillion opportunity.

Still, why has this new concept been so challenging for companies to understand and engage with, and pivot away from society’s obsession with younger population segments?

 Overtaking the baby boomers in 2015, the younger “18-to-34 years” age segment has been long considered the key target segment for advertisers. Still, with 10,000 people turning 65 each day in America, by 2050, the +65 cohort will surpass the under 18 cohort. It is not surprising, as the average life expectancy has increased by 30 years over the past 100. For those reaching 65 years old today, approximately 50% will reach their 90s. And for those babies born today, over 50% of them can expect to live into their 100s! Those over 50 contribute to earnings and spending to the longevity economy valued at over $22 trillion worldwide and presents an outstanding opportunity for smarter firms, which can reframe their mindset about historically prized demographic segments.

When you consider how biopharma firms share common missions of saving and improving lives, their C-suites should especially lean into serving this growing population segment. These executives should consider other companies which have already deployed strategies to uncover new clients such as Warby Parker with their creation of a whole product line focused on progressive lenses. Or consider Bank of America’s Merrill Lynch Wealth Management group which hired the industry’s first financial gerontologist in 2014 to help the more than 14,000 financial advisors better understand the needs of their clients and implement a framework that assumes them to be living to one hundred.

Besides reimagining their external business development opportunities, what longevity strategies should be deployed for their internal workforces as people are going to have 60-year career spans?

First, the historical “learn/earn/retire” mantra no longer applies. With a potential 100-year life span, continuous upskilling coupled with career breaks will become a new norm. In March 2022, LinkedIn shared a new feature that enables people to easily insert career breaks into their profiles. Surveying nearly 23,000 workers and 7,000 hiring managers, LinkedIn discovered that nearly two-thirds (62%) of employees have taken a break at some point in their professional career.

 So, employers need to change their mindsets around career breaks and the historical assumptions, often negative, around them.

Second, create programs, like returnships, that provide near-term solutions to your firm’s talent pipeline needs as well as build long-term employer of choice recognition. As Goldman Sachs has proudly communicated, “Pioneering the first program of its kind in the wake of 2008, Goldman Sachs has sought to listen and continuously innovate to increase the number of opportunities in the return to workspace and recruit the best talent regardless of resume gaps.”

 I’ve read that Goldman Sachs has even trademarked the term, “returnships” and I do believe this programming has secured positive results for the firm.

Finally, C-suites should consider reimagined terminology when it comes to considering new ways of engaging with their workforce. As I have shared with my students at Stanford as well as corporate clients, it is stage, not age.

 Specifically, it is the stage of life we are in that is most important. A 50-year-old and a 75-year-old could be in the same stage of life, the "reinvention stage", given a 100-year life span. And when these two people reinvent themselves via upskilling their capabilities, they can potentially create new value at firms which recognize the benefits that they might bring to the table.

Stage (Not Age): How to Understand and Serve People Over 60--the Fastest Growing, Most Dynamic Market in the World, 

Harvard Business School Press, June 14, 2022

2. Golden, Susan Wilner; Carstensen, Laura, 

How Merrill Lynch Is Planning for Its Customers to Live to 100

LinkedIn Members Can Now Spotlight Career Breaks on Their Profiles

https://www.goldmansachs.com/careers/professionals/returnship/

5. Susan Wilner Golden | Stage (Not Age) | Talks at Google – YouTube: Susan Wilner Golden explains the major demographic shift currently taking place in the workforce; YouTube: Reskilling America’s aging workforce to meet the demands of the future (Full Stream 4/25)

As the founder and director of the dciX initiatives at the Stanford Distinguished Careers Institute, Golden engages experienced professionals who are focusing the next chapter of their lives on building innovations for both social and economic impact. Golden is the author of the book “STAGE (Not Age),” published by Harvard Business Review Press in June 2022. Golden received a Doctorate of Science from the Harvard School of Public Health.

In this latest Harvard Business School Healthcare Alumni Association Q&A, Susan Wilner Golden, DSc, lecturer at the Stanford Graduate School of Business, reveals an untapped $22 trillion global opportunity for all companies and others supporting healthy aging and longer life spans.
 
