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The Fast Company Impact Council is a private membership community of influential leaders, experts, executives, and entrepreneurs who share their insights with our audience. Members pay annual membership dues for access to peer learning and thought leadership opportunities, events and more. The future of AI is bright, yet its continuous evolution and an uncertain regulatory environment cloud its reality for many businesses. The new year has begun with a more relaxed stance on AI policy and DeepSeeks supposed better mousetrap, giving CIOs pause on which direction to go. But these are good reminders for organizations to proactively establish best practices for AI implementation. Doing so will ensure future compliance while effectively leveraging AI for transformation and growth. How regulation may impact innovation In the U.S., California legislation has taken a first stab at defining guardrails domestically, following the footsteps of the EU Artificial Intelligence Act. However, these early regulation attempts will take time to enact and be vetted for successes, failures, and inevitable adjustments. We expect AI to be governed differently according to three broad categories: AI creators: OpenAI and hyperscalers creating AI models from scratch. These entities face unique regulatory challenges related to responsible and demonstrable data sourcing. AI adapters: Fine-tuners of the creators models that embed them along with retrieval-augmented generation and similar technologies, adapting them for specific business application development. Enterprises must ensure they are sourcing models that can be attested to on intellectual property (IP) infringement or have some sort of protection against IP infringement. AI consumers: Most businesses taking advantage of the adapters AI applications in their day-to-day operations. These organizations must ensure their data sets are cleansed and compliant with regulations. The perils of AI missteps Weve seen a lot of enthusiasm from CIOs trying hundreds of different uses for generative AI. But two years in, theyre still largely lost on how to scale and monetize it. Enterprises need to get back to the basics, treating AI initiatives like traditional IT application development rather than uncharted territory. Narrowing the scope to two or three projects can make innovation more manageable and bring demonstrable value. That means asking fundamental questions like: How do we vet the use case? How will we validate it? How will we support it once its built? Start with a clear understanding of the business problem, create requirements, and ensure the solution will generate a measurable advantage, such as increased productivity or cost savings. Its also crucial to follow a structured development lifecycle that includes cost controls, security measures, and governance. Build from where you already have good utilization metrics. For example, you might run a call center and know that your customer service representatives handle 10 calls per hour. If you deploy a tool that allows them to handle 15 calls, thats easily measurable. The key is to find opportunities within your organization that you can optimize and deliver a smarter process. 6 practical steps to AI There are several ways in which your organization can establish a solid AI standard: First look inward: Focus on internal applications to optimize workflows, automate processes, and reduce risks. They are easier to identifyand safer because you’re not exposing yourself to external vulnerabilities. Starting here also allows organizations the grace to learn and adapt before expanding to applications that impact more stakeholders, including customers. You can scale outward once you understand a solutions full implications like security concerns, legal ramifications of how the models were trained, how theyre really licensed, and your operating costs. Ensure data integrity and compliance: Data integrity and compliance are critical for all three AI use case categories. For creators, ensuring responsible sourcing of data is essential. Adapters need to cleanse and comply with data sets, while consumers must vet software-as-a-service providers and confirm proper data management. Follow the lead: Learning from state-level regulations, such as Californias, can offer insights about future federal frameworks. Businesses should learn from how others adapt accordingly. Adopt ethical AI: Implementing responsible practices is imperative to navigate the regulatory landscape. Business leaders and technologists should prioritize transparency, data privacy, compliance, and continuous learning in their AI programs, along with flexibility to adapt to new or changing regulations and technologies. Surround yourself with knowledgeable teams: Leaders should surround themselves with knowledgeable teams to navigate AIs complexities and understand their businesss true needs. AI projects success rely on a cooperative effort from cross-functional teams, including business functional areas addressing specific challenges, development, data science, IT, and FinOps. Establishing an AI center of excellence unites them. Avoid past mistakes: The current rush to adopt AI mirrors past technology adoption cycles, such as the rush to adopt cloud services without proper planning. Avoid being swayed by the allure of new technology without assessing its implications. Instead, methodically approach AI as you would any other enterprise tool. Our industry is at a leaping point from abstraction and conceptual thinking to tangible AI implementation. The goal is to find the real value in the challenges it can solve for your business. For AI to generate new revenue streams and streamlined operations, prioritize practical solutions over grand innovations. Focus first on the unsexy work that frees your employees from the mundane tasks that no one loves. Moving beyond merely trying AI to doing AI requires starting with sound processes and practical applications that not only will insulate your organization from future uncertainties, but drive it forward. Returning to IT fundamentals is the key to making AI a reality. Juan Orlandini is CTO, North America of Insight Enterprises.
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E-Commerce
Social Security has been considered among the most efficient, cleanest government programs in the country. For instance, a study by the Inspector General of the Social Security Administration, published in July, found that from 2015-2022, the government had made $25 billion in Social Security overpaymentstypically payments that went out after someone had already died. But a good chunk of those payments was recouped, so the total amount lost to improper Social Security payments over those eight years was around $2 billion a year, a minuscule sum relative to the Social Security Administration (SSA)s budget (which is now well over a trillion dollars). Yet, to hear Elon Musk and President Trump tell it, Social Security may well be the site of what Musk called the biggest fraud in history. While Musks so-called Department of Government Efficiency (DOGE) was rummaging around in the SSAs various databases, it found one in which more than 19 million people who are 100 years old or older had no official death date recorded. In other words, as far as the database was concerned, they were still alive. Musk posted a table of the living centenarians, broken up by age, and then suggested that these people might be getting Social Security payments, joking, Maybe Twilight is real, and there are a lot of vampires collecting Social Security. Over the next couple of days, the Trump administration amplified this idea. First, press secretary Karoline Leavitt said during a TV appearance that Musk and DOGE suspect there are tens of millions of deceased people who are receiving fraudulent Social Security payments. Then, last night, Trump himself said that we have millions and millions of people over 100 years old in Social Security, and that if we took them all off the payment rolls, all of a sudden we have a very powerful Social Security. If these claims were true, they would, of course, be an absolutely staggering revelation. And fixing them would, as Trump suggested, put Social Securitys finances back on a healthy trajectory. However, the claims are false and are, in fact, absurd. Social Security is not sending out checks to tens of millions, or hundreds of thousands, of dead people, and there was never any reason to suspect that this was the case. What happened here was pretty simple: Elon Musk didnt understand what the table he was looking at represented, and apparently, rather than ask someone who might know (or even just google the subject), he leapt to the conclusion that he and DOGE might have uncovered the biggest fraud in history. Ghosts in the machine What Musk was looking at was data from whats called the Numident database, or “Numerical Identification System,” which is a database of every Social Security number issued. And its true: There are millions of people in that database who are dead, and not receiving fraudulent checks, but for whom the SSA has no official death date. In most cases, thats because these people died before the SSA had systematized the collection of death dates (which is trickier than you might think since death certificates are recorded on the state level, not the federal). In other cases, its because the death date was entered in the payment-records database (which is separate), but not in Numident. The important point, though, is that the Numident database is not the database of people who are getting Social Security checksas Musk and now Trump erroneously seem to think. Thats a separate database, and all those millions and millions of people over 100 years old that Trump referred to are not on the active database of people receiving Social Security checks. We know this because we can check the correct database of how many people ages 99 and older received their regular Social Security checks in December (the last month for which data is available): 89,106 people ages 99+ collected Social Security benefits. Thats a long way from tens of millions, and its also fewer than the estimated number of centenarians in the U.S. In other words, there is no evidence of fraud at all. Even beyond the question of the very elderly, theres no reason to think Social Security fraud is a meaningful problem. Some 51.8 million people over the age of 61 collected retired-worker Social Security benefits (what we think of as traditional Social Security) in Decemberout of a population of well over 60 million people ages 62 and older. If the SSA were paying loads of dead people, the number of old people collecting benefits would not be smaller than the number of old people overall. One other part of the story thats worth noting: The issue of having a database with all these dead people without recorded official death dates is one that the SSA has, obviously, been aware of for a long time. In 2023, in fact, the Inspector General did a report on the subject, recommending that the SSA take steps to fix it as much as possible. The challenge is that would take millions of dollars and lots of work hours to track down death dates from all over the country, most of them from between 50 and 80 years ago. The question is whether it’s worth doing, given that the actual costs of not having the death-date info are trivial, since these people are not getting checks. The point, in any case: This is not a new issue that DOGE has uncovered, but one thats been discussed for many years. More important, the way Musks misunderstanding of a table of numbers quickly turned into the president of the United States making baseless insinuations of fraud about the Social Security Administration is no way to run a government, or any kind of business, for that matter. But now having that erroneous information and baseless claims of fraud out there, courtesy of the misinformed Musk and Trump, can understandably erode peoples confidence in this reliable and most valuable federal program, creating a climate of unnecessary, and inadvertent, anxiety and distrust. Musk and DOGE have been given a tremendous amount of power in this administration. They need to use that power responsibly.
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E-Commerce
The Fast Company Impact Council is a private membership community of influential leaders, experts, executives, and entrepreneurs who share their insights with our audience. Members pay annual membership dues for access to peer learning and thought leadership opportunities, events and more. Healthcare navigation was supposed to be the ultimate guidea GPS for the healthcare maze. Instead its more like an old paper map with half the roads missing. What was meant to simplify care has become just another layer of complexity, dressed up as concierge support but too often steering people based on cost, not quality.At a time when AI, telehealth, and integrated care models are merging and transforming how people experience healthcare, navigation as we know it is becoming obsolete. The riseand fallof Navigation 1.0 Navigation started with a clear, patient-first mission: Remove barriers to care. The concept was pioneered by doctors to improve cancer outcomes for underserved patients who struggled with delays in diagnosis and treatment. The original vision was simple: Get people to high-quality care, faster.But then the industry lost the plot.As digital health took off, the market became flooded with big vision front doorsslick apps that promised convenience but ultimately led nowhere. These platforms were entryways without hallwaysflashy introductions to healthcare that failed to connect people to integrated clinical expertise or personalized support. Instead of providing a pathway to better health, they left people stranded.Then insurers stepped in.They highjacked navigation, repackaging it as a cost-control tool rather than an independent, patient-first service. The incentivesand the experiencewere not the same. Instead of guiding people to the best possible care, insurer-led navigation steered them toward lower-cost providers with little regard for quality or fit. What should have been an unbiased clinical advocate became just another mechanism for network steering.The result? People arent just underserved; some are actively led away from quality care, facing more barriers, more frustration, and worse outcomes. Navigation was supposed to help people get to and through the system, but instead, it became a roadblock.Lets not forget, weve seen this before. Like with the first wave of telehealth, Navigation 1.0 has become an add-on to a fragmented system rather than a real solution. And just like Telehealth 1.0, too many navigation services are now commodity, or more specifically low-value check-the-box offeringsutilization management in disguise. What comes next: A more integrated, person-centric model If Navigation 1.0 is dying, what replaces it?A smarter, all-in-one healthcare modelone that doesnt just point people in a direction, but actually gets them the right care at the right timeproactively, ongoing, and when called upon. Navigation was always meant to simplify healthcare, but that only happens when clinical expertise, advocacy, and technology work together and are deeply integrated to eliminate friction, improve access, and drive better health outcomes. Heres what that looks like: Advocacy, not just guidance People dont need another appthey need someone in their corner. True advocacy means:Fighting billing errors and helping people understand and resolve insurance denials.Connecting people with high-quality doctors, not just network-preferred ones. Helping people navigate treatment decisions and medication costs. Its not about pointing people in the right direction; its about walking beside them. AI + EQ: Smarter, more empathetic care AI assistants and guides are hot topics, but technology alone isnt enough. What people want is AI + EQ = the efficiency of AI-driven experiences combined with real human expertise and empathy.In healthcare, AI should either free up humans to focus on tasks that only humans can accomplish, or provide guidance to humans to help them perform uniquely human roles more effectively. If a system isnt human-centered, its just another version of the problem. At this point in the game, integration cant be vision Navigation without deep clinical expertise, system-wide connectivity, and personalized visibility into an individuals benefits, history, and preferences is about as useful as a tour guide whos never been to the cityhelpful on the surface, but not when you drill down for trusted, known, and proven guidance.For navigation to be effective, it must provide direct access to clinical expertise as part of an integrated teamnot just for finding a doctor. It should go even further to holistically support people across mental and physical health, administrative, financial, and social needs. It must include addressing the unexpected too, such as medication support, in-home care, and a broad range of social determinants of health issues. Smarter, cost-conscious care (not just the latest trend) The GLP-1 drug boom (Ozempic, Wegovy) is a case study in why smarter healthcare decision making matters.These drugs are breakthrough treatments for diabetes and weight lossbut theyre also so expensive that if prescribed indiscriminately, they could bankrupt the system and individuals too.Thats why Navigation 2.0 must be evidence-based, guiding people toward treatments that work, are clinically appropriate, and are informed by a persons benefits and based on what a person can afford short term and ongoing.Better healthcare isnt just about access; its about making smart, data-driven decisions with and for people. The future: Personalized all-in-one healthcare Navigation 1.0 was about helping people wayfind. The next era is about creating a fully connected, advocacy-driven experience that actually improves health, lowers costs, and removes complexity.At Included Health, we call this personalized, all-in-one healthcare. Its not just a replacement for navigationits a new category altogether, one that finally delivers on the original promise of making healthcare simpler, better, and more human.Healthcare navigation, as it exists today, is dying. RIP.Owen Tripp is cofounder and CEO of Included Health.
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