Let’s look at the major benefits:

• Virtually Unlimited Energy (and Lower Costs) – One of the strongest arguments comes from energy economics. As Amir Husain writes in “Why Your Next Cloud Server Will Orbit Earth,” space-based data centers could leverage continuous solar power, free from night cycles or atmospheric loss. Similarly, NVIDIA-backed startup projections suggest up to 10x lower energy costs due to constant solar exposure and elimination of terrestrial constraints.
• Natural Cooling and Environmental Benefits – Cooling is one of the largest costs for terrestrial data centers. In space, excess heat can be radiated directly into the vacuum, avoiding water usage and reducing environmental strain. This fact also sidesteps growing community resistance to large, power-hungry data centers on Earth.
• No Land Constraints or Zoning Issues – Orbital infrastructure eliminates real estate constraints entirely. Data centers could scale far beyond what’s feasible on Earth without competing for land or facing regulatory friction.
• A New “Edge” for Space-Based Workloads – Experts at Enterprise Strategy Group note that space data centers could act as edge compute platforms for satellites, processing data in orbit instead of sending everything back to Earth. That would make space data centers particularly valuable for earth observation, defense systems, and autonomous space operations. In these use cases, space compute actually reduces latency (more on that below).

Now for the major drawbacks:

• Massive Cost and Operational Complexity – Launch costs remain a fundamental barrier. Even with falling costs from reusable rockets (hello, SpaceX), deploying and maintaining hardware in orbit is still orders of magnitude more expensive than terrestrial infrastructure. As telecom analyst Armand Musey has noted, the economics are still “hard to model” due to unknown technical variables.
• Maintenance Is a Nightmare – Unlike Earth-based data centers, you can’t just swap a failed server. Hardware must survive radiation, micrometeoroids, and extreme temperature swings. Repairs would require robotic servicing or additional launches. That’s one reason skeptics argue the concept may be impractical in the near term.
• Latency: The Core Challenge – Here’s the biggest issue for Earth-based applications. Signals must travel from Earth to orbit and back. That distance introduces delay, because geostationary orbit can produce hundreds of milliseconds of latency. Even mid-orbit systems may see ~50–150 ms latency. Compared to fiber, where latency is extremely low, this is a major disadvantage for real-time AI inference, financial trading, and interactive applications, for example.

The big question: how can the latency problem be addressed?

This is where the topic gets interesting, and more nuanced:

• Move to Low Earth Orbit (LEO) – Instead of placing data centers far away (GEO), using LEO (~500–600 km) reduces latency dramatically: only ~2–4 ms one-way propagation delay. That begins to approach terrestrial network performance for certain use cases.
• Optical (Laser) Inter-Satellite Networks – Modern designs rely on laser-based communication links between satellites, allowing data to move through space without touching the ground. In some scenarios, this could even outperform fiber, due to straighter paths, fewer routing hops, and ultra-high bandwidth (multi-terabit potential),
• Hybrid Space–Earth Architectures – The most realistic near-term model is hybrid infrastructure, meaning latency-sensitive workloads stay on Earth, while batch AI training or space-native workloads run in orbit. That is, terrestrial networks still provide ultra-low latency where needed, while space handles energy-intensive compute.
• Edge Computing in Space – For satellite-heavy industries, space data centers actually reduce latency by processing data locally before transmission. Which paints an interesting picture: latency is a problem for Earth users, but a solution for space systems.

The bottom line.

Space-based data centers are not a replacement for terrestrial infrastructure – at least not yet. But they are a compelling extension of it. The best fit for the near term would be space-native workloads, AI training, and energy-intensive compute. The biggest hurdle is latency for Earth-based applications. The key use case now would seem to be LEO constellations + laser networking + hybrid architectures.

The most important insight is this: space data centers are not about better compute. Rather, they’re about cheaper energy. That’s certainly the position of SpaceX’s Elon Musk, who said on a recent podcast: “In 36 months, the cheapest place to put AI will be space.” He maintains that the real bottleneck in AI isn’t chips – it’s energy.

On the same podcast, Musk put it this way: “The availability of energy is the issue. If you look at electrical output, it’s more or less flat [outside of China, he noted]… Where are you going to get your electricity? Especially as you scale….how are you going to turn the chips on?” He went on: “It’s harder to scale on the ground than it is to scale in space. You’re also going to get about five times the effectiveness of solar panels in space versus the ground, and you don’t need batteries.”

So, in closing, think about this: For the past two years, the AI conversation has centered on models, chips, and algorithms. The industry has debated transformer architectures, GPU shortages, and the competitive positioning of hyperscalers. But, beneath all of that is a more fundamental constraint – one that may ultimately define the winners and losers of the AI era – and that constraint is, quite simply, energy. As AI systems scale, the industry is colliding with a simple physical reality: compute requires power. And not just incremental power, but exponential increases in energy demand driven by training ever-larger models and running inference at global scale.

This shift reframes how we should think about AI infrastructure. The limiting factor is no longer just access to advanced semiconductors. It’s access to cheap, abundant, and reliable electricity. And, if the AI era is ultimately constrained by power, not chips, then orbit may become less of a novelty and more of a necessity.

Timmaron Group would welcome a discussion if you are, or expect to be, involved in the space data center industry. We have experience working with Starlink, a business unit of SpaceX, as a solution architect leading up to the launch of its first constellation of satellites in 2019. Reach out to us at hi@timmarongroup.com.

The post The Case for, and Against, Data Centers in Space — And the Critical Question: Can Latency Be Solved? appeared first on Timmaron Group.

Cargill is, of course, the largest U.S.-based privately held company on the world. We’re lucky to have them here in our state! Our CEO, Barb Stinnett, led an engagement some years ago with Cargill that was focused on improvement to its global supply chain.

Barb hopes to see you at the conference! If you aren’t yet registered, jump right over to the sign-up page here. Attendance is expected to reach capacity. If you run into a wait-list situation, just let us know at hi@timmarongroup.com. We can help because we know a guy! See you there!

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Artificial intelligence has actually been present in healthcare for a long time – more than a decade. But the past few years have marked a turning point. Advances in machine learning, computer vision, and generative AI, combined with enormous healthcare datasets, are enabling AI systems to move from experimental pilots into real clinical workflows. Today, AI is helping physicians detect disease earlier, automate routine administrative work, accelerate drug discovery, and improve patient outcomes. Here are ten concrete ways AI is already making an impact across healthcare and medtech.

Detecting Cancer Earlier Through Medical Imaging
AI systems trained on large imaging datasets are helping radiologists detect cancers earlier and more accurately. Algorithms can analyze mammograms, CT scans, and MRIs to identify subtle patterns that might otherwise be overlooked. In breast cancer screening trials in Europe and the United States, AI tools have shown the ability to increase cancer detection rates while reducing the workload of radiologists. Rather than replacing clinicians, these systems act as a powerful “second reader.”

Turning Routine Scans Into Screening Tools
One of the most promising uses of AI is extracting additional insights from scans that were originally taken for another purpose. For example, AI models can analyze standard CT scans and identify early signs of osteoporosis, cardiovascular disease, or lung cancer, even when those conditions were not the reason the scan was ordered. This approach allows health systems to turn existing imaging data into a broader diagnostic tool.

AI-Powered Diagnostic Devices
AI is increasingly being embedded directly into medical devices. New AI-enabled stethoscopes can analyze heart sounds and ECG signals in seconds, helping physicians detect conditions such as heart failure or atrial fibrillation during routine exams. Similar approaches are being applied to ultrasound systems, endoscopes, and wearable diagnostic sensors. By augmenting traditional devices with machine learning, clinicians gain deeper insights at the point of care.

AI Assistance in Surgery
In the operating room, AI is beginning to assist surgeons with real-time guidance. For example, new imaging platforms use machine learning to analyze tissue images during cancer surgery, helping surgeons determine whether any cancerous tissue remains after tumor removal. This can reduce the need for repeat procedures and improve patient outcomes.

Clinical Decision Support
Healthcare generates enormous amounts of data, from patient histories and imaging results to lab tests and genomic data. AI systems can synthesize these complex datasets and provide clinicians with decision-support recommendations. In some cases, AI tools help physicians identify high-risk patients earlier or recommend evidence-based treatment plans aligned with clinical guidelines.

Automating Administrative Work
Administrative tasks consume a surprising amount of physicians’ time. AI is increasingly being used to automate documentation, insurance coding, and clinical note generation. Generative AI tools can summarize patient visits, draft clinical notes, and populate electronic health records. Reducing this administrative burden helps doctors focus more time on patient care, reducing the impact of what has been called “physician burnout.”

AI-Driven Drug Discovery
Drug discovery has historically been slow and expensive, often taking more than a decade to bring a new therapy to market. AI models can analyze massive datasets of molecular structures, biological pathways, and clinical trial results to identify promising drug candidates more quickly. Several pharmaceutical companies are already using AI to accelerate early-stage discovery and optimize drug design.

Predicting Patient Deterioration
Hospitals are increasingly deploying AI tools that continuously monitor patient data, such as vital signs, lab results, and electronic health records, to detect early signs of clinical deterioration. These predictive systems can alert care teams when patients are at risk of sepsis, cardiac arrest, or respiratory failure, enabling earlier intervention.

Expanding Access Through Virtual Care
AI-powered triage tools and virtual health assistants are helping healthcare systems expand access to care. Digital platforms can guide patients through symptom assessments, recommend appropriate care pathways, and connect patients with clinicians when necessary. In some cases, these systems operate 24/7, helping health systems manage patient demand more efficiently.

Personalized Medicine
Perhaps the most transformative long-term application of AI is personalized medicine. Machine learning models can analyze genomic data, lifestyle factors, and clinical history to predict how individual patients will respond to specific treatments. This approach could enable more tailored therapies and improved outcomes across many diseases.

The Future of AI in Healthcare
Despite the excitement surrounding AI, healthcare remains a cautious industry. Regulatory approval, clinical validation, and physician trust are essential before new technologies can be widely adopted. However, momentum is clearly building. Thousands of AI-enabled medical devices have already received regulatory clearance, and health systems are increasingly integrating AI tools into everyday workflows. Rather than replacing clinicians, the most successful applications of AI are proving to be those that augment human expertise, helping physicians make better decisions, faster. As data availability grows and AI models continue to improve, the next decade is likely to see even deeper integration of artificial intelligence across the healthcare ecosystem.

Timmaron Group maintains a strong presence in Minnesota, which is a widely recognized center for healthcare and medtech innovation. One of our clients over the years has been the Mayo Clinic, which has become a major player in AI for healthcare. Begun in 2022, its “Mayo Platform_Accelerate” program has quietly built one of the most credible pipelines in healthcare AI. Over just four years, it has graduated roughly 100 startups across a dozen cohorts, giving them access to one of the largest clinical data sets in the world. Real-world validation is what makes the difference for these startups. They get access to millions of Mayo patient records, direct collaboration with clinicians, and the ability to generate clinical-grade evidence.

To further discuss AI in healthcare, and the challenges your organization may be facing, please schedule a call with the CEO of Timmaron Group, Barbara Stinnett, by emailing us at hi@timmarongroup.com.

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This is a summary of the event.

The Hg Silicon Valley Leadership Summit 2026 – Major Themes and Takeaways:

1. Agentic AI is the non‑negotiable in 2026 – Leaders at the summit agreed that AI has moved from “co‑pilots” to autonomous agentic systems. Models can now handle multi‑hour tasks and are expected to manage multi‑day projects by year‑end. Frontier engineers no longer “write code with AI” — they orchestrate fleets of agents working on parallel features and problems, while humans define problems, review outputs, and guide strategy. This shift means traditional linear productivity metrics break down – one engineer with eight agents isn’t eight times more productive but operates in a wholly new paradigm. Pearl of wisdom: organizations must restructure workflows around agents, revisit assumptions every six to twelve months, and plan in weeks or months rather than annual cycles.
2. Get agent‑ready: anticipate the next bottlenecks – While AI accelerates tasks, it shifts bottlenecks downstream. In software development, code generation is no longer scarce – human comprehension and review are. This creates “comprehension debt,” where teams ship code they don’t fully understand, heightening fragility and security risks. A similar pattern appears in go‑to‑market functions: AI can generate unlimited content, but success hinges on strategy and execution quality. To cope, companies are forming go‑to‑market engineering teams that run rapid experiments, build personalized outreach, and generate quarterly business review decks in minutes. Pearl of wisdom: treat AI as a catalyst for process redesign; map workflows end‑to‑end, identify where acceleration creates pressure, and fix broken processes before layering AI.
3. Talent and culture matter more than ever – Contrary to fears that AI makes people less important, the event discussion argued it amplifies talent: AI raises the productivity “floor” but raises the “ceiling” far more. The best performers become vastly more effective, widening the gap with average staff. Leaders must therefore invest heavily in high performers and be honest that some people may not adapt. Cultural friction often comes from middle management worried about losing influence. Leaders need to model experimentation, admit what they don’t know, and create psychological safety for failure. Pearl of wisdom: the key to transformation lies in cultivating a learning‑oriented culture – leaders should go first, encourage curiosity, and be prepared for differential adaptation.
4. Innovation needs a new operating model – Traditional product development – long roadmaps and careful scoping – was designed for scarce coding capacity. With AI agents able to prototype features in hours, planning must invert: rapid prototyping → customer feedback → iteration. Teams should be small, autonomous, close to customers, and empowered to experiment. Building more features is easy; the challenge is ruthless prioritization around high‑value use cases and killing under‑performing initiatives quickly. Pearl of wisdom: treat innovation as a continuous experiment; prioritize outcomes over outputs, and streamline structures to enable fast pivots.
5. The SaaS expansion: from workflow to work – AI lowers barriers to building custom software, letting small enterprises create tailored CRMs or HR systems in weeks. This challenges incumbents whose moats relied on complexity and integration. Yet incumbents retain distribution and domain knowledge; those that evolve can pivot from selling tools to selling outcomes delivered by AI agents. The discussion identified two strategic paths: become a platform, with API‑first architecture that lets customers and agents build on top, or deliver complete work (outcomes) priced on value. Rich contextual data is essential, as agents need more information than traditional systems of record provide. Pearl of wisdom: decide whether to be a platform or an outcome‑provider – standing still invites disruption.
6. A new leadership imperative – Technology and tools are ready, but successful AI transformation hinges on leadership. Transformations require unambiguous commitment from the top and willingness to question every process and incentive. Leaders must personally use AI tools and avoid delegating understanding to specialists. Support, training, and patience are vital, but accountability is necessary; some staff may not make the transition. Pearl of wisdom: lead by example – senior executives should rebuild their own skills, embrace uncertainty, and set clear expectations that transformation is non‑optional.

Conclusion: The momentum continues – The summit stressed that the AI transformation is already underway. Organizations that restructure their operating models, invest in talent, and move decisively will gain compounding advantages. Those merely experimenting or layering AI onto existing processes will fall further behind. Final pearl of wisdom: the window for decisive action is open but not indefinite – leaders must choose which side of this widening divide they will stand on.

To discuss any of the above topics, or to get further perspective on AI transformation, schedule a call with the CEO of Timmaron Group, Barb Stinnett, by emailing us at hi@timmarongroup.com.

The post Navigating the AI Transformation: A Leadership Discussion appeared first on Timmaron Group.

How Big Is It?
Analysts project the broader space economy could reach $1 trillion by 2030, driven by several structural shifts. Space is increasingly viewed as the “invisible nervous system” underpinning global finance, agriculture, telecommunications, and defense. Starship’s high reusability is expected to radically lower launch costs, though full validation depends on the success of its V3 architecture tests in 2026. Meanwhile, orbital data centers are emerging as a new frontier, especially following SpaceX’s acquisition of xAI, which signals a push to integrate satellite networks with artificial intelligence capabilities. Private space stations from firms like Axiom Space and Blue Origin are also expected to replace the aging International Space Station.

IPO Fever
Speculation around a SpaceX IPO has intensified, with reports suggesting a potential June 2026 listing at a valuation between $1 trillion and $1.5 trillion. Major banks are rumored to be preparing underwriting roles. Strategically, the IPO could reposition SpaceX as the central company within Musk’s portfolio, potentially surpassing Tesla in prominence. There has also been discussion of priority access for Tesla shareholders.

SpaceX’s valuation has decoupled from traditional aerospace peers and is increasingly compared to mega-cap technology firms like NVIDIA or Amazon. As of early 2026, private valuations between $1.25 trillion and $1.4 trillion far exceed the combined market caps of leading U.S. defense contractors. The distinction lies in revenue multiples: legacy defense firms trade at modest sales multiples due to reliance on government budgets, while SpaceX commands software-like valuations (50–60x sales), largely due to recurring Starlink revenue.

The Growth Engine
Starlink is the engine behind this growth. Having surpassed 10 million subscribers in early 2026, the service has been doubling annually. Projections suggest 18–20 million subscribers in 2026 and up to 60 million by 2030, potentially generating $40–$100 billion in annual revenue. By late 2026, Starlink could account for 70–80% of total company revenue, transforming SpaceX from a launch provider into a global telecommunications utility. The rollout of Direct-to-Cell satellite-to-phone services, including partnerships such as T-Mobile, represents a significant upside opportunity. (Full disclosure: Timmaron Group has experience working with Starlink.)

Risks to Consider
However, analysts caution that several high-velocity risks could delay the IPO or derail valuation assumptions. Technically, Starship must achieve full reusability, because any major failure could stall satellite deployment. Orbital debris and “Kessler Syndrome” risks could invite regulatory intervention. Geopolitically, countries are pursuing sovereign alternatives to Starlink, and there is the issue of Musk’s key-person risk. Antitrust scrutiny is also growing due to SpaceX’s control over both launch services and satellite broadband infrastructure.

Financially, a $1.5 trillion debut would leave little margin for error, say analysts. Pressure on Starlink’s average revenue per user, especially in emerging markets, could trigger post-IPO volatility. Additionally, the capital intensity of Mars ambitions and ongoing Starship development may test public market tolerance for prolonged cash burn.

In sum, SpaceX stands at the center of a trillion-dollar transformation in global infrastructure – but execution risk remains as significant as the opportunity.

For more on the space industry, see this recent WSJ article: The Space Economy Is Mission-Critical. Here’s What Leaders Need to Know.

To discuss any of the above topics, or to get further perspective of the future of the space industry, schedule a call with the CEO of Timmaron Group, Barb Stinnett, by emailing us at hi@timmarongroup.com.

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Just what exactly happened this first week of February to cause the latest shock to the industry? A single move by an AI company juggernaut. As Fast Company put it, one Anthropic update wiped billions off software stocks. It begins: “Investors fear Claude Cowork’s new agentic features threaten entire categories of SaaS tools.” The article continues:

“Tech workers have been worried for years about the AI tidal wave coming for their jobs, but their bosses are starting to worry now, too.

“Stocks plunged this week as fears escalated that AI advancements will take a bite out of business for many software, data, and professional services companies. The market losses are tied to updates to Anthropic’s AI-powered workplace productivity suite, Claude Cowork, which threatens to replace some software tools ubiquitous in the professional world.”

But despite many in the tech world piling on to the prevailing opinion that enterprise software is dead, there are a few voices speaking out with a viewpoint that all is not lost for the software industry. One example is cited in the closing paragraph of the above Fast Company article:

“Not everyone deeply invested in AI agrees. Nvidia CEO Jensen Huang swatted away worries that AI would eat the traditional software industry after the stock bloodbath that began on Tuesday. ‘There’s this notion that the tool in the software industry is in decline, and will be replaced by AI,’ Huang said, emphasizing that relying on existing software tools makes more sense than reinventing the wheel. ‘It is the most illogical thing in the world, and time will prove itself.’ ”

Another skeptic is Steven Sinofsky, a former Microsoft exec who’s a board partner at Andreessen Horowitz (a16z). Here’s a (long) opinion piece he published this week: Death of Software. Nah. Additionally, an article in The Economist argued a bit against the prevailing dire view: Why software stocks are getting pummelled. Are investors overestimating the risk from AI? (May require a free subscription.)

And the Fox Business article cited above ends with this note: “Despite the stock market turbulence this week, the Dow Jones managed to cross the historic 50,000 level, underscoring the continued exuberance surrounding the AI race.”

For a fascinating, deep-dive analysis of the situation, Ignite Insights published this (very long) post on its Substack just days ago: The Great SaaS Unbundling. Where Software Value Actually Goes When Agents Arrive. (May require subscription.)

So, experts and pundits are weighing in on both sides. But, of course, the story is still being written. What’s your take? Timmaron Group’s Founder and CEO Barb Stinnett has a long history in software, cloud, and AI. To chat with her, schedule a call by contacting hi@timmarongroup.com.

The post A Fascinating Saga Continues: AI Disrupting Enterprise Software appeared first on Timmaron Group.

Recently, the Minnesota Department of Health announced that the state would receive approximately $193 million through the federal Rural Health Transformation Program. It had requested about $200 million for the year, making the final award effectively a full funding success. The program is administered by the Centers for Medicare & Medicaid Services and is part of a broader, multi-year federal effort to strengthen rural healthcare systems nationwide.

The funding is intended to stabilize and modernize rural care delivery. Planned investments include 1) expanding telehealth and mobile services, 2) strengthening the rural healthcare workforce, 3) improving management of chronic conditions such as diabetes and heart disease, and 4) upgrading technology and data infrastructure. For rural hospitals and clinics under strain, the funds offer rare flexibility to invest in longer-term solutions rather than short-term fixes.

At the same time, local reporting by outlets such as the StarTribune and others has noted an important caveat: this funding does not fully offset other looming financial challenges. Minnesota, like many states, faces projected reductions in federal Medicaid funding tied to recent federal budget changes. These changes include work requirements, eligibility verification tightening, and cost-sharing rules, which are intended to encourage employment, improve program integrity, and control costs. Those reductions are expected to affect provider revenue and increase uncompensated care, particularly in rural areas where Medicaid plays an outsized role.

Our perspective is that this $193 billion rural health award for Minnesota creates both opportunity and urgency. State agencies and healthcare organizations will need to deploy funds quickly and demonstrate measurable impact to remain competitive for future years of the program. Success will depend on execution—how effectively Minnesota converts this one-year funding win into sustainable improvements in access, workforce stability, and care delivery.

In short, this recent funding represents a strong vote of confidence in Minnesota’s rural health strategy—but not a permanent solution. The real test will be whether these investments can deliver durable results in a rapidly evolving healthcare and policy environment.

Timmaron Group stands ready to help you understand the current reality in rural healthcare, based on our long experience around this issue. We have worked with county health departments all around the State of Minnesota. How may we help you get the money where it’s needed? How can this funding benefit your community? Book a call with CEO Barb Stinnett by contacting us at hi@timmarongroup.com.

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As of this writing, the CES show has wrapped up. The JPM event is in full swing, and Barb just attended a private event on the “top 20 investments of JPM” on the second evening of the conference. More on her insights on what’s called “JPM Week” below. But first…

What were the takeaways from CES? It was no secret that AI was the main focus, again. This year, however, “the emphasis was on AI driving the mechanics of products,” Barb said. The obvious, well-hyped theme was robotics, but Barb noted other AI products impressed her, including earbuds that provide virtually instant translation in multiple languages. No human involvement necessary!

CES panels that Barb attended included one on technology in human communication — meaning how people relate to one another. That not only includes language translation, but also things like dictation for physicians, which can now go right into a patient’s electronic health record. The benefit here is that it can lead to time savings that will actually reduce billing rates for doctors. And that can ultimately mean this AI technology will reduce spending on Medicaid and Medicare! Yes, Barb noted that CES provided insights in applied AI for both the consumer technology and healthcare industries.

Insights from the JPM Conference. Two big things coming out this week in San Francisco, according to Barb Stinnett, are determining “the right AI for healthcare,” and — as you have heard here before — the replacement of enterprise software with AI, which is a major trend now underway. Barb also noted that healthcare technology companies are looking at how the major EHR systems, Epic and Oracle-Cerner, work, and how AI can replace or enhance them — especially with the use of voice. Vendors are looking at the entire workflow of healthcare and how it can be improved, by roles — starting with the admin check-in and the nurse’s welcome, and especially looking at saving doctors’ time, where applicable.

One big number Barb noted: JPM estimates that $500 billion will be invested as a result of this year’s conference! That would certainly seem to be a record. (If interested, you can see the entire agenda for this year’s JPM Healthcare Conference here.)

Applying AI to healthcare is central on everyone’s mind attending JPM Week this year. Another focus is how to bring the patient to own their own health — in a 50-50 collaborative partnership with the provider. This is the first step, said Barb, in changing how the industry can start a process to determine, “What is insurance?” Not just as it affects the doctor — who of course can be sued — but changing to a model of patient acceptance of their own health. The big thing here, said Barb, is that “redefining insurance could actually be done without government!”

Timmaron Group has much experience and unmatched connections in the industry, and we stand ready to help you navigate the changing landscape and achieve your goals. To learn more, just reach out today: hi@timmarongroup.com.

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Note that we will be closed beginning December 22, returning January 5.

As you’re developing plans for 2026, be sure to reach out if we may help you with a rural or community healthcare initiative… or perhaps help you supercharge your business development efforts… or provide an AI Assessment for your leadership team. Just say hi@timmarongroup.com… and Happy New Year to you and yours!

The post Wishing a Happy 2026 to All Timmaron Clients and Friends! appeared first on Timmaron Group.

Not surprisingly, the subject of AI was a central topic as well. “What it was yesterday is not what it is today, nor what it will be tomorrow,” said Barb, “and the human element will remain critical.” Other subjects addressed during the session included 1) DEI: “it’s gone, we lived through it, we learned from it, and need to embed it into what we do every day”; 2) remote work, which the JPM exec noted is definitely a changing focus at her company – “and we’re learning”; and, finally, 3) digital currency was called out as something we need to learn and live with.

“It was a very emotional and worthwhile event,” said Barb, and she thought the talk by JPM’s Dorothee Blessing “was fantastic!” In addition to Best Buy’s very talented CEO being in attendance (and also speaking), there were three or four other Fortune 500 CEOs in the audience, said Barb, who serves on several boards.

Women Corporate Directors (WCD) is the world’s largest membership organization and community of women corporate board directors. It is a trusted community of directors serving on more than 8,500 public and private boards around the world. For more about the Minnesota Chapter of Women Corporate Directors, see their LinkedIn page here.

And do feel free to reach out to Barb for more about the event, by emailing hi@timmarongroup.com.

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