New location brings an amusement park-inspired approach to fitness, transforming the traditional gym into eight distinct worlds built around strength, Pilates, recovery, mindfulness, entertainment and more

KILDEER, Ill., Oct. 15, 2026 /PRNewswire/ -- Amped Fitness®, the rapidly expanding fitness brand redefining what a gym can feel like, today announced plans to bring Amped Universe to Kildeer, Illinois, at 20771 N. Rand Road.

The new location will introduce the Chicago-area community to Amped Fitness's most ambitious fitness concept yet: a gym designed less like one continuous room and more like a collection of destinations waiting to be discovered.

Inspired in part by the experience design of an amusement park, Amped Universe gives different parts of the fitness experience their own distinct identities, environments and sense of arrival.

Instead of walking past rows of equipment on a traditional gym floor, members move through portals into eight immersive worlds. One moment they can be training inside an ancient Roman colosseum; the next, crossing beneath the Brooklyn Bridge, taking Reformer Pilates in Bali, recovering inside a wellness escape or leaving Earth entirely for a meditation experience on the moon.

The concept takes what makes great destinations memorable — anticipation, discovery, storytelling and a sense that something different is waiting around the next corner — and applies it to fitness.

"We started asking ourselves why gyms couldn't create the same feeling of discovery that the world's best entertainment and hospitality experiences create," said Travis LaBazzo, Founder and CEO of Amped Fitness. "An amusement park isn't memorable because every attraction looks the same. Every destination has a personality and gives you a reason to explore what's next. We took that philosophy and built it around fitness."

EIGHT WORLDS. ONE FITNESS UNIVERSE.

At the center of the Kildeer experience will be eight signature Amped Fitness destinations:

Amped Arena transports members into an ancient Roman colosseum, surrounding strength training with monumental architecture and larger-than-life Greek and Roman gods in an environment built around power.

The Babe Cave® transports members to the Brooklyn Bridge and into Amped Fitness's signature women-only training destination, created around confidence, strength and community.

Aura Pilates transports members to Bali, pairing professionally instructed Reformer Pilates with a serene, resort-inspired environment designed around control, balance and intentional movement.

South Peach transports members to South Beach, Miami, surrounding glute and lower-body training with the color, energy and playfulness of Miami.

Lunar Escape transports members from Earth to the moon, creating a celestial environment for meditation, stretching, breathwork and mindfulness.

Azura Cove becomes a restorative escape built around recovery and wellness experiences.

Sweat Cinema transforms cardio into entertainment, allowing members to run, ride and climb inside a theater-style environment surrounded by larger-than-life content.

Studio Suites provide private, flexible spaces for individual workouts, stretching, virtual training, meetings, content creation and more.

Together, the destinations are designed to make each visit feel different depending on what a member wants from that day — power, confidence, control, playfulness, mindfulness, recovery, entertainment or focus.

THE WORKOUT IS THE ATTRACTION

While Amped Universe borrows principles from amusement parks and destination entertainment, the experience remains grounded in serious fitness.

The environments are built around the workout rather than around it.

Members will have access to Amped Fitness's expansive strength and cardio offering, specialized plate-loaded and selectorized equipment, Reformer Pilates, recovery, dedicated training environments and a wide range of fitness experiences within a single club.

"The point isn't to put a theme around a gym," LaBazzo said. "The point is to use design to change how people feel about going to the gym in the first place. We want you to walk in and wonder where you want to go today."

That philosophy represents a broader shift for Amped Fitness from simply designing gyms to designing experiences.

Traditional health clubs have largely been organized around equipment categories. Amped Universe instead organizes much of the experience around destinations and emotions, giving different types of training their own environment and identity.

The result is what Amped Fitness believes can become a new category of experiential fitness: part serious training facility, part boutique fitness collection, part wellness destination and part immersive entertainment experience.

PREMIUM FITNESS, DEMOCRATIZED

Despite its experience-driven design, Amped Fitness's strategy remains centered on accessibility.

The company believes consumers should not have to maintain separate memberships for a traditional gym, Reformer Pilates studio, recovery concept and specialty fitness experiences.

At Amped Fitness, those experiences come together under one roof.

"Our mission is to democratize experiential fitness," LaBazzo said. "Experiences this thoughtfully designed have traditionally been attached to premium price points. We believe incredible environments, incredible equipment, Pilates and recovery should be accessible to far more people."

The Kildeer club will be located at:

20771 N. Rand Road

Kildeer, IL 60047

Additional details surrounding the club's opening, memberships and presale will be announced soon.

"When those doors open, we don't want people to feel like they're entering another gym," LaBazzo said. "We want them to feel like they're entering somewhere they haven't been before."

About Amped Fitness

Amped Fitness® is a rapidly expanding Experience-Design fitness brand built around immersive training destinations, premium amenities and community-focused experiences. With signature destinations including Amped Arena, The Babe Cave®, Aura Pilates, South Peach, Lunar Escape, Azura Cove, Sweat Cinema and Studio Suites, the brand is transporting members and redefining what a gym should feel like. For more information, visit www.ampedfitness.com.

Media Contact:

Katie Snyder

Senior Account Manager

Pitch Public Relations

[email protected]

480-286-2411

SOURCE Amped Fitness

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Metropolitan Park Supports Variety Boys & Girls Club of Queens' New Jackson Heights Facility

QUEENS, N.Y., Oct. 15, 2026 /PRNewswire/ -- Metropolitan Park, the $8.1 billion sports and entertainment district led by Hard Rock International and Steve Cohen, today announced a $10 million investment in the new Jackson Heights community center of the Variety Boys & Girls Club of Queens. The investment is part of Metropolitan Park's more than $1 billion in community benefits and served as the seed funding for the community center's $33.8 million in total public-private funding.

In addition to Metropolitan Park's investment, Governor Kathy Hochul committed $10 million in State funding, $9.3 million was secured by Council Member Shekar Krishnan and the New York City Council, $2 million from U.S. Senators Chuck Schumer and Kirsten Gillibrand, $1 million by Congress Member Grace Meng, $1 million in New York State funding secured by Assembly Member Jessica González-Rojas, $250,000 by Assembly Member Steven Raga, and $250,000 by Assembly Member Catalina Cruz.

Metropolitan Park leaders released the following statements:

"On behalf of Hard Rock International, the Seminole Tribe of Florida and Jim Allen, we are proud to make this $10 million investment and to stand alongside our elected officials, community leaders, and the Variety Boys & Girls Club to help turn a long-held community vision into reality," said Sean Caffery, Hard Rock International. "This investment can make a difference and transform the lives of New York residents and their families for generations."

"Metropolitan Park is about creating opportunities for Queens, and that commitment doesn't stop at the boundaries of our site," said Michael Sullivan, Metropolitan Park. "Jackson Heights, Corona and East Elmhurst are some of the most vibrant communities in New York City and we heard loud and clear from our neighbors that they need a new community center to meet the area's growing needs."

About Metropolitan Park

Metropolitan Park, a proposed $8.1 billion private investment and partnership between Steve Cohen and Hard Rock International, will transform 50 acres of Citi Field parking lots into a year-round sports and entertainment destination. The project will feature a Hard Rock Hotel with a casino, sportsbook, restaurants, bars, meeting and event space and a state-of-the art live music venue as well as a Taste of Queens Food Hall. Metropolitan Park will create 25 acres of public green space, generate 23,000 union jobs, and deliver over $1 billion in community benefits including new infrastructure improvements and a completely revamped, ADA-accessible 7-line train station.

About Hard Rock®

Hard Rock International (HRI) is one of the most globally recognized companies with venues in nearly 80 countries spanning more than 300 venues including owned/licensed or managed Hotels, Casinos, Rock Shops®, Live Performance Locations and Cafes. Beginning with an Eric Clapton guitar, Hard Rock owns the world's largest and most valuable collection of authentic music memorabilia with more than 89,000 pieces displayed at locations around the globe. The Unity™ by Hard Rock global loyalty program rewards members for doing the things they love across participating properties. In addition, Hard Rock Digital spotlights the sports betting and iGaming experience with products remixed in the spirit of Hard Rock for players worldwide.

HRI has received numerous industry, destination and workplace awards across the travel, hospitality, gaming, entertainment and food & beverage sectors. HRI currently holds investment grades from primary investment-grade rating agencies: S&P Global Ratings (BBB), Moody's Investors Service (Baa2), and Fitch Ratings (BBB). For more information on Hard Rock International, visit www.hardrock.com or shop.hardrock.com.

SOURCE Hard Rock International

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Illinois Bus Workers Prepare to Fight for First Contract

BELLWOOD, Ill., Oct. 15, 2026 /PRNewswire/ -- Nearly 300 Illinois bus workers at Sunrise Transportation, now a subsidiary of Student Transportation of America, have voted overwhelmingly to join Teamsters Local 777. These workers include school bus drivers and monitors who organized to secure fair pay, improved working conditions, and better benefits.

"Every day, these workers are entrusted with safely transporting students to and from school," said Jim Glimco, President of Local 777. "The work they do is essential to families and communities across Illinois, and they deserve wages, benefits, and workplace protections that reflect the value of that responsibility. We're proud to welcome them to the Teamsters."

Local 777 will now work with the newly organized group to negotiate a first collective bargaining agreement.

"We're excited for the opportunities that becoming Teamsters will create for all of us," said Al Mann, a bus worker at Sunrise and a new member of Local 777. "We stood together because we want a voice on the job and a stronger future for ourselves and our families. By joining the Teamsters, we now have the support and resources to fight for higher wages, improved health care and vacation benefits, safer working conditions, and more."

Founded in 1937, Teamsters Local 777 represents workers in a wide variety of industries throughout the Chicago area. For more information, go to teamsters777.org.

Contact:

Lena Melentijevic, (347) 208-2279

[email protected]

SOURCE Teamsters Local 777

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If your production servers run Enterprise Linux and your DGX Spark runs the DGX OS it shipped with, a prototype has to change operating systems on its way to production. Rocky Linux from CIQ Pro AI (RLC Pro AI) runs on both.

Your Spark and your production servers, Arm or x86, run the same RLC Pro AI, and production is a license change from Developer to Enterprise.

CIQ builds, validates, and signs the CIQ Linux Kernel (CLK) 6.18 and the NVIDIA driver. Both ship in RLC Pro AI's repositories, so you install them as one product instead of assembling them yourself.

The next question is how RLC Pro AI does on the Spark itself. CIQ ran Spark Arena's published recipe for three Qwen3.5-0.8B tests on a DGX Spark running RLC Pro AI 9.8. Here are the results and the recipe behind them, so you can rerun every test on your own.

See what CIQ's runs measured

CIQ ran each test 6 to 12 times on a DGX Spark set up with the steps in this post. The table puts the range of those runs beside the best result another DGX Spark owner has published for the same test, as of October 1, 2026:

Against those published results, CIQ's runs would place first on two of the three tests and second on the third. Each test has five published results from other DGX Spark owners.

Gather what you need

The setup takes a stock RLC Pro AI 9.8 install to a host that runs sparkrun, the harness Spark Arena uses. Each step ends with a check that must pass before you move to the next one. You need:

- An RLC Pro AI 9.8 host with an NVIDIA GPU, such as the DGX Spark CIQ used, and a free RLC Pro AI Developer license.

- A Depot user and token. Log in to the CIQ Portal to get your token, as described in the Depot documentation.

- Sudo access on the host, and time for one reboot.

0. Check your starting point

uname -r

nvidia-smi --query-gpu=name,driver_version --format=csv,noheader

Expect CLK 6.18.43-2.1.el9_clk as the default boot entry and the pinned 580 driver.

Start from a Server, Server with GUI, or Workstation install. A Minimal Install doesn't work as a starting point. The package group that carries CLK and the driver belongs only to those three environments, so a minimal host has neither, and nothing below applies.

1. Enable the RLC AI repositories

sudo depot login -u -t

sudo depot enable --yes rlc-ai-9

This enables clk-6.18-el9, rlc-9-ai, rlc-9-core, and rlc-9-supplemental. Step 2's CLK module build comes from clk-6.18-el9.

--yes runs the post-enable script, which disables the stock rocky*.repo files in favor of the rlc-ai-9 copies. The RLC Pro AI repository guide covers what each repository carries.

2. Switch the NVIDIA driver to 595

sudo dnf install --allowerasing --disableplugin=protected-kmods \

nvidia-dc-driver-latest-cuda kmod-nvidia-dc-open-latest

sudo systemctl reboot

The swap installs 8 packages at 595.71.05 and removes the 10 stock *580* packages. The removed packages include nvidia-dc-fabric-manager580 and nvidia-dc-imex580, which have no replacement in the -latest set.

Use nvidia-dc-driver-latest-cuda, not plain nvidia-dc-driver-latest. The plain package is the full display driver: it resolves to 40 packages and 167 MB and pulls in Xorg and Mesa.

Keep --disableplugin=protected-kmods on hosts that have dnf-plugin-protected-kmods installed. CIQ needed it there to complete this swap. On a host without that plugin, the flag does nothing.

Check after the reboot:

$ nvidia-smi --query-gpu=driver_version,persistence_mode --format=csv,noheader

595.71.05, Enabled

3. Install Docker Engine

These are Docker's published RHEL steps:

sudo dnf config-manager --add-repo https://download.docker.com/linux/rhel/docker-ce.repo

sudo dnf install docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin

sudo systemctl enable --now docker

sudo usermod -aG docker $USER

Log out and back in. docker ps must then work without sudo.

4. Install the NVIDIA container toolkit

Install it from the RLC repositories:

sudo dnf install nvidia-container-toolkit

sudo nvidia-ctk runtime configure --runtime=docker

sudo systemctl restart docker

This check, run as your normal user, exercises the driver, the runtime, and the toolkit together:

$ docker run --rm --gpus all rockylinux:9 nvidia-smi --query-gpu=driver_version --format=csv,noheader

595.71.05

5. Install uv and sparkrun

curl -LsSf https://astral.sh/uv/install.sh | sh

uv tool install sparkrun

This installs sparkrun and uvx into ~/.local/bin, which is on the default login PATH. You need uvx because sparkrun runs its benchmark client, llama-benchy, through it.

For repeatable numbers, let sparkrun drop the page cache before each launch. This command writes a sudoers rule limited to /proc/sys/vm/drop_caches, and it prompts for your sudo password:

sparkrun setup clear-cache --hosts localhost --save-sudo

Check what you installed

These are the versions installed on the DGX Spark; the benchmark numbers came from (NVIDIA GB10, aarch64):

Docker moves independently of RLC Pro AI. At the time of writing, an x86_64 host installing Docker gets docker-ce-29.8.2. The container toolkit is 1.19.1 on x86_64 and 1.19.0 on aarch64.

CIQ signs the driver, the kmod, and the container toolkit with its key, e8258450c7780912. You don't need the EPEL or CRB repositories.

See how CIQ verified these steps

CIQ ran steps 0 through 5 under KVM with Secure Boot enforcing, from both published starting points: the RLC Pro AI 9.8 qcow2 image and a Server install from the 9.8 DVD, each with a GPU passed through. Both produced identical results. The page-cache rule in step 5 is the exception: it prompts for a sudo password and there wasn't one on either test account. The example output in this post comes from the DGX Spark.

The aarch64 image carries the same kernel and driver packages. The Spark's installed set matches what these steps produce, plus kmod-nvidia-dc-open-peermem-latest-clk6.18-base, which these steps don't install.

One difference between the starting points: the qcow2 image arrives with /etc/yum.repos.d/depot-rlc-pro-9.repo and a registered rlc-pro-9 Depot product already enabled, before you run depot login. A DVD install doesn't.

Run the Spark Arena tests

Spark Arena publishes the recipe behind each entry: the serve command, defaults, environment, mods, and container. CIQ ran the published Qwen3.5-0.8B recipe with two changes:

curl -fsS -o qwen3.5-0.8b.yaml https://spark-arena.com/api/recipes/bf736380-8483-49ce-929d-96d817a48543/raw

sed -i 's/qwen3_coder \\$/qwen3_coder/' qwen3.5-0.8b.yaml

sparkrun benchmark qwen3.5-0.8b.yaml --hosts localhost \

--image ghcr.io/spark-arena/dgx-vllm-eugr-nightly:2026091001 \

--profile @official/spark-arena-v2 --output results.yaml

- The sed line removes a stray \ at the end of the recipe's serve command. sparkrun 0.3.10 can't handle it. The fix is merged but not yet released, so drop this line once a release includes it.

- --image pins the container CIQ ran, in place of the recipe's moving :latest tag, so your run uses the same build.

The @official/spark-arena-v2 profile applies Spark Arena's run rules, so each result cell lines up with a leaderboard column. The sparkrun benchmark reference lists the other options, including --fresh to discard state from an earlier run of the same recipe.

The results file reports every cell in the profile. These are the cells behind the three tests:

tg128 is 128 tokens of generation, d16384 is a 16K-token context, and c1 or c2 is the number of concurrent requests. You should run the benchmark more than once before you compare your results with the table.

Get RLC Pro AI for your Spark

Register for a free RLC Pro AI Developer license through the CIQ Portal, follow the steps above, and compare your numbers with CIQ's. When the work is ready for production, see what RLC Pro AI includes.

NVIDIA, DGX, and DGX Spark are trademarks of NVIDIA Corporation. All other trademarks are the property of their respective owners.

Stablecoins, tokenized deposits, CBDCs and the changing shape of money

The future of money isn’t only about making balances programmable. It’s about understanding what those balances represent, who stands behind them, how they can be redeemed, and how they behave under stress.

Stablecoins have demonstrated demand for always-on digital value transfer. Tokenized deposits show that banks can respond without giving up the deposit model. Central bank digital currencies (CBDCs) and tokenized reserves may provide another critical component—trusted settlement infrastructure.

But, although these different forms of money may seem similar to the end user, they’re not the same.

Money used to be easier to explain: Understanding digital money

For a long time, the banking industry could explain money through a relatively familiar hierarchy. Central bank money sat at the foundation. Commercial bank deposits were the money most households and corporates used. E-money and stored-value balances sat within the payments layer. The roles weren’t always simple, but they were at least familiar.

That clarity is now being challenged. A corporate treasurer, a product lead, or a payments architect may now see balances labeled as stablecoins, tokenized deposits, CBDCs, tokenized reserves, e-money balances, or tokenized money market funds. All may look cash-like in a user interface, but they’re not the same thing.

The practical question isn’t simply: Is this money? The better questions are: who issued it, what legal claim does the holder have, how is it backed, how is it redeemed, where does it settle, what happens in stress, and how does it appear in the bank’s books and controls?

Comparing digital money: Issuers, claims, and implications for banks

As new forms of digital money emerge, familiar distinctions between deposits, payment instruments, stablecoins, and central bank money are becoming harder to navigate. A useful starting point is to look beyond the technology and focus on the underlying economic and legal characteristics: who issues the money, what claim the holder has, how it is backed and redeemed, and what role it plays in settlement. The table below provides a simple taxonomy to help distinguish the major forms of money and highlight why each matters to banks.

This table compares major forms of money by issuer, holder claim, and their implications for banks.

The distinction matters because digital money isn’t simply a technology choice. Each form creates different implications for funding, liquidity, legal claims, controls, settlement, and the customer experience.

What’s driving adoption of stablecoins, CBDCs and tokenized money?

Digital money adoption isn’t accelerating because of a single technology or a single regulation. It’s the result of several pressures arriving at the same time:

- Cross-border payments remain too slow, expensive, and fragmented for many corporate and retail use cases.

- Stablecoins have demonstrated that market participants value 24/7 digital value transfer.

- Tokenized securities, funds, and real-world assets need a tokenized cash leg if settlement is to become genuinely atomic.

- Central banks are exploring wholesale CBDC and tokenized reserves as the settlement layer for a tokenized financial system.

- Banks are responding to the threat of deposit displacement and the opportunity to modernize commercial bank money.

- Corporate clients increasingly expect real-time liquidity visibility and more programmable treasury services.

This isn’t a crypto sideshow. It’s a payments, liquidity, settlement, data, and operating-model issue for banks.

Comparing the strengths and weaknesses of new forms of digital currency

Stablecoins, tokenized deposits, and CBDCs are often discussed as part of the same digital money landscape, but they serve different purposes and carry different implications for banks. The key distinctions lie in who issues them, what backs them, how settlement occurs, and where trust ultimately resides. For banks, understanding those differences is essential to evaluating the opportunities, risks, and strategic roles each model may play in the future of payments and tokenized finance.

This table compares the key strengths, weaknesses, and strategic implications of stablecoins, tokenized deposits, and CBDCs for banks.

Stablecoins: Useful today, risky in stress

Stablecoins shouldn’t be dismissed. They address real market needs, including 24/7 value transfer, cross-border reach, open-network access, digital asset settlement, platform payouts, and programmable payment workflows. Just as importantly, they have pushed the industry to ask why traditional forms of money can’t move with the same speed and flexibility.

At the same time, stablecoins introduce important systemic considerations. Under normal conditions, a fiat-referenced stablecoin may function much like money. Under stress, however, its underlying structure becomes more visible, and it’s ultimately a claim that depends on the issuer, the quality and liquidity of the reserves, the custodian, and the network through which it operates.

- Depeg risk: The token may no longer reliably maintain its intended value relative to the underlying fiat currency.

- Redemption and run risk: Holders may rush to convert stablecoins into bank money during periods of stress.

- Reserve liquidation risk: Issuers may need to sell reserve assets quickly to meet redemption demands.

- Deposit displacement risk: Funds may shift from commercial bank deposits into stablecoin structures.

- Singleness of money risk: Different tokens may carry different levels of trust and trade at different values, weakening the principle that equivalent forms of money should exchange at par.

The policy issue: Preserving the singleness of money

The singleness of money is the expectation that one pound, euro, or dollar should remain one pound, euro, or dollar, regardless of the form in which it’s held. A bank deposit and cash aren’t the same legal claim, but the financial system is designed so they exchange at par in normal conditions.

Digital money complicates that expectation. A pound held in a bank account, a pound-denominated stablecoin, a tokenized deposit, and a tokenized money market fund may appear “cash-like” to the user, but they may not be equally trusted, redeemable, or final.

The implication for banks is that, if money fragments into multiple private tokens with different issuers, reserves, and redemption rights, the system risks moving from a unified monetary architecture to a marketplace of money-like claims.

Tokenized deposits offer a bank-native response

Tokenized deposits provide a different model. They are commercial bank money represented on programmable infrastructure and remain liabilities of the issuing bank. That is what makes them attractive to banks: they can introduce programmability and richer transaction capabilities without moving client money outside the banking model. Within a single institution, tokenized deposits can support treasury movements, approved institutional transfers, internal liquidity management, and controlled settlement.

For customers, this can create a faster and more flexible service. For banks, it can help preserve the deposit relationship. However, there’s an important limitation. Bank A’s tokenized deposit isn’t automatically Bank B’s money. Once a transaction needs to be made between institutions, tokenizing the deposit is only part of the answer.

The next question is settlement.

In my next blog on digital money, I'll look at why tokenized deposits alone don’t solve the interbank problem and why the future of digital money will depend on settlement, interoperability, and trusted infrastructure.

I'll also be exploring these issues further in an upcoming webinar, “What profitable roles can banks play in the digital currency race?” I'll join a panel of experts to discuss how banks can operationalize digital money, navigate settlement and liquidity challenges, and identify opportunities as the market evolves. Register for the webinar to join the conversation.

If you'd like to discuss the ideas in this blog or learn more about CGI's approach to helping banks navigate the evolving digital money landscape, please reach out.

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Collections organizations can no longer afford to rely on traditional methods and legacy applications alone. As portfolios grow more complex, delinquencies rise, and customer preferences evolve, real-time collections can be a game changer.

Without timely updates, collections teams risk wasted outreach, inconsistent treatment, and unnecessary compliance exposure. A real-time approach helps avoid these risks by updating account and contact data as soon as it changes, so decisioning and treatment paths can adjust immediately rather than waiting for the next batch cycle.

Many of the approaches described here are informed by our experience supporting banking clients through CGI Credit Studio for collections. The challenges and considerations, however, are common across industries.

Move beyond batch-based dependencies in your collections system

Many collections operations continue to rely on batch-based data updates because that is all their collections system can support. Updates typically post to accounts and contacts overnight and may require a period of system downtime while the batch is processed.

The result is data that is often refreshed only once per day. If something changes immediately after processing, account or contact information can remain stale for nearly 24 hours.

Today's collections environment requires up-to-date information. With a real-time collections platform, teams can react as soon as something changes, including:

- Payment made

- Risk score changes

- Contact's phone number or updated consent

Paired with guided workflows and real-time re-decisioning, current data gives teams the information they need to tailor each outreach attempt to the latest account and customer circumstances.

Adjust treatment minute by minute to modernize collections

In legacy environments, contact treatments are typically set once per day and then executed with limited ability to adapt. In many cases, a batch-based dialer kill file—often refreshed every 15 minutes—is the primary mechanism for changing the contact strategy for an account.

A modern collections environment works differently. The platform can receive new account information throughout the working day and respond as events occur. If a payment arrives at midday, for example, the platform can recognize it immediately and update the next action.

In an event-driven model, that response could include sending a real-time dialer kill notification, changing the contact strategy based on updated days past due, or making an account inactive once it is cured. Payments are one example; however, the same principle can apply to many other account attributes. Real-time capabilities eliminate the need to wait for the next batch cycle before acting.

Respond faster to time-sensitive collections compliance events

Certain regulations require immediate action when a triggering event occurs. Bankruptcy is a clear example, and the same real-time approach can support other regulated processes, such as repossession.

When a real-time collections platform is integrated with the appropriate sources, important regulatory events can reach the collections organization immediately. For example, if a customer files for bankruptcy and the platform is connected to a bankruptcy monitoring solution, the update can trigger the appropriate bankruptcy workflow as soon as it is received. That immediate response can remove the account from treatment and reduce the risk of a compliance violation. The same principle applies to real-time notifications from repossession vendors when a vehicle has been repossessed.

Near real-time evidence of adherence can also help demonstrate to regulators that the organization is taking timely, deliberate steps to comply with applicable requirements.

Modernize your collections operations

Collections operations are becoming less dependent on batch-based processes, but the transition to real-time capabilities requires coordination across systems, data sources, and workflows. The benefits, including fresher information, more responsive treatment, and stronger support for time-sensitive compliance, make it worth starting that transition now.

Discover how Credit Studio for collections and its real-time capabilities can modernize your collections operations—and contact me to discuss how your organization can benefit from real-time collections data.

How AI helps change how leaders sense, understand, and adapt to change in real time

Every transformation is designed to create value. Yet many organizations face challenges in realizing the full return on their investment and may encounter some speed bumps along the way. Technology isn’t necessarily the issue. Rather, organizations may lack timely insight into how people are experiencing change.

The information to support these insights exists, but much of it goes unseen or unused. Instead, many organizations continue to rely on periodic surveys, workshops, and retrospective reporting to understand adoption of transformation programs, technologies, processes, and so on. By the time resistance or confusion crops up during a change transformation, time and momentum may have been lost.

This is where change management is evolving. AI is making it possible to move beyond periodic assessments toward what I call continuous organizational sensing—the ability to continuously sense, interpret, and adapt to how people are experiencing change. Rather than relying on snapshots in time, leaders can develop an ongoing understanding of organizational dynamics and make better informed decisions throughout a transformation.

The challenge with change management isn't a lack of data

Most organizations aren’t short of information. They’re short on timely, actionable insights. Change management is one area where this is especially true.

Every transformation generates data, yet much of it remains hidden in plain sight. Collaboration platforms, help desk tickets, training feedback, meeting transcripts, and open-text comments all provide valuable signals about how change is unfolding. Too often, however, these sources are overlooked, analyzed only after the fact, or left untapped because of legitimate privacy and sensitivity considerations.

Traditional change management approaches create blind spots because information is often:

- Delayed

- Fragmented across systems

- Subject to individual interpretation

- Manually analyzed after the fact

Consider a bank rolling out new digital capabilities across its branch network. Traditional measures such as training completion and communications engagement may suggest the transformation is progressing well. However, those measures don't necessarily show whether bankers are using the tools effectively in customer conversations.

By identifying adoption patterns early on, frontline feedback and business outcomes can reveal where employees are struggling with fragmented access, competing priorities, or a lack of reinforcement. This gives change leaders the insight to intervene before those barriers become embedded in day-to-day behavior.

Reasonably, many change leaders are still building confidence in how to access, interpret, and use growing volumes of information responsibly. As AI becomes more integrated into transformation, data literacy is becoming a core capability for change professionals. Understanding what data sources are available (even if fragmented), what data is allowed to be used, and what the data is or isn’t telling us, are essential to making informed decisions.

AI impacts how we understand change—not why we lead it

AI doesn’t replace change management. It strengthens it. By analyzing patterns across large volumes of structured and unstructured information, AI enables continuous organizational sensing. Instead of asking change leaders to manually review thousands of comments, conversations, and support requests, AI helps surface meaningful insights that would otherwise remain hidden.

This is more than another technology capability. It represents a shift in how organizations understand change. Rather than relying primarily on retrospective reporting, change leaders can continuously sense what is happening across the organization, interpret emerging patterns, and adapt their actions before challenges become barriers to adoption.

Instead of asking, "How did the project go?" organizations can begin asking, "What is happening today?"

This shift enables change leaders to identify:

- Emerging resistance before it slows adoption

- Changes in stakeholder sentiment

- Opportunities to improve communication clarity

- Adoption barriers across different groups

- Hidden champions who can help accelerate change

When these insights are available in real time, organizations can intervene earlier and engage more effectively. This improves the likelihood of achieving their transformation objectives, while keeping costs low and productivity stable.

There are tools to support this. Take CGI’s dedicated change management assessments, for example, which can bring together data outputs into a single view, including fragmented change data, to show readiness, adoption, and risks.

The real opportunity is better decision-making

Technology alone isn’t the competitive advantage. The competitive advantage comes from combining AI-generated insights with experienced leadership.

Organizations that embrace continuous organizational sensing can make decisions with greater confidence because they have a clearer understanding of how change is unfolding across the business. Greater transparency also helps build trust by making it easier to explain why decisions are being made and where additional support is needed. This transparency provides leaders with more confidence when they need to act and sometimes make uncomfortable decisions.

As organizations strengthen training in both data literacy and change literacy, they create an environment where insight becomes part of everyday decision-making rather than an activity reserved for project milestones.

Continuous organizational sensing isn’t about replacing intuition. It’s about complementing experience with evidence, enabling leaders to make better decisions at the moments that matter most.

Where human judgment matters most

AI can recognize patterns at remarkable speed and scale, but insight alone doesn't lead people through change. Human judgement is essential to understanding what those patterns mean and determining how to respond.

Building trust, creating psychological safety, demonstrating empathy, and providing meaning during uncertainty are uniquely human aspects of change leadership.

From my experience, one principle continues to stand out: successful transformation isn’t about implementing change for its own sake. It’s about helping people navigate change in ways that are meaningful, practical, and sustainable.

This is why I see AI and human judgement as complementary. AI can help us see where attention may be needed. People bring the context, experience, and empathy to decide what action is needed.

As continuous organizational sensing evolves, human oversight and leadership are how data will be used responsibly and transparently.

The future of change management

The future of change management isn’t AI-driven. It’s evidence-driven.

Organizations that succeed won’t necessarily be those with the most advanced technology. They’ll be those that develop the capability for continuous organizational sensing and use AI to better understand how people are experiencing change while relying on leaders to provide judgment, empathy, and direction.

Technology can reveal patterns. People give those patterns meaning. That combination enables organizations to respond earlier, adapt faster, and realize greater value from their transformation investments.

In the end, the competitive advantage isn’t AI itself. It’s the ability to continuously sense, interpret, and adapt human behavior throughout a transformation.

If you're exploring how AI can strengthen change management in your organization, I'd welcome the opportunity for a conversation. I also encourage you to learn more about CGI's work in the areas of change management and artificial intelligence.

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Why bank-grade settlement is the missing layer in tokenized finance

In the first part of this series, Digital money: Not all tokens are created equal, I wrote about why stablecoins, tokenized deposits and central bank digital currencies (CBDCs) shouldn’t be treated as interchangeable forms of digital money.

Stablecoins have demonstrated demand for always-on value transfer, while tokenized deposits give banks a way to make commercial bank money programmable without giving up the deposit model. CBDCs, meanwhile, could provide a digital form of central bank money for settlement.

But making money programmable within one institution is only part of the challenge. The harder question is what happens when that money needs to move between institutions and how the transaction is ultimately settled.

This is where tokenization meets settlement.

Tokenized deposits: Useful, but limited

Tokenized deposits allow banks to introduce programmability while retaining the underlying deposit relationship. But their limitation becomes clear when money needs to move beyond the issuing bank. Bank A's tokenized deposit isn’t automatically Bank B's money.

Across banks, the receiving institution requires settlement before it can safely credit its customer. This is why tokenized deposits without tokenized settlement are useful but incomplete. They can digitize and program a bank’s own deposit ledger. However, they don’t, alone, solve the interbank settlement problem.

The missing layer: Tokenized settlement

Across banks, the neutral settlement asset is central bank money. In a tokenized financial system, that may mean tokenized reserves or wholesale CBDCs. The customer-money leg may be a tokenized deposit; the settlement leg should be central bank money.

Common transaction legs and their purpose

This table describes different banking transaction legs.

This layered model matters because the financial system doesn’t simply need tokens that can move. It needs debit, settlement and credit to complete together.

Initiatives such as Project Agorá from the Bank for International Settlements Innovation Hub are important because they explore this combination of tokenized commercial bank deposits with tokenized wholesale central bank money on shared programmable infrastructure.

The significance isn’t simply tokenization. It’s the coordination between commercial bank money and settlement money.

The token mint/burn problem is really a synchronization problem

Tokenized finance discussions often focus on mechanisms such as token minting and burning. The expensive and risky part, however, isn’t the computational act itself. The harder problem is synchronization. The system must ensure that debit + settlement + credit complete together.

Those movements must also reconcile with bank books, pass through the required controls and achieve legal finality. If one leg completes while another fails, the bank can be left with an operational, liquidity or legal problem.

Tokenization, therefore, doesn’t remove the need for trusted infrastructure. It changes where trust sits.

- Banks control bank money.

- Central banks control settlement money.

- A governed platform coordinates the transaction.

What this means for banks: Assessing the impact of digital currency

Digital money touches the entire banking model, not just payments. Banks need to understand how each form of digital money affects funding, liquidity, settlement, risk, controls and the customer experience.

The enterprise-wide impact of digital money

This table highlights how digital money could impact different areas of the bank.

This breadth is important. A digital-money initiative may begin as an innovation or payments program, but production deployment quickly becomes an enterprise banking issue.

Making digital money work: What to look for in a partner

A partner with both payments and technology expertise can help banks maximize the value of digital money in terms of usability, compliance, integration, resilience, auditability and business-need alignment. Look for these attributes in a partner:

- Strategy and readiness: Expertise in use case selection, regulatory impact, business case development and road mapping.

- Target operating model: Strong capabilities in the areas of onboarding, wallet controls, redemption, exception management and crisis playbooks.

- Architecture and integration: Solutions in core banking, payments, custody, token ledgers, APIs, ERP/TMS and data platforms.

- Risk and controls: Deep experience with anti-money laundering, sanctions, depeg monitoring, reconciliation, cyber resilience and legal finality controls.

- Data and reporting: Support for single liquidity views, legal classification, valuation, audit trail and regulatory reporting.

- Managed services: End-to-end monitoring, reconciliation support, incident response and operational resilience.

The market doesn’t need more proof that tokens can move. It needs proof that tokenized money can be governed, reconciled, audited, secured and integrated into banking infrastructure.

A pragmatic digital money roadmap for bank clients

Banks don’t need to solve the entire future-of-money architecture at once. However, they do need a clear view of where they stand and which capabilities should be built now.

Building a future-of-money architecture

This table lays out steps to take in building a future-of-money environment.

This gives banks a practical way to make progress without trying to solve the entire future of monetary architecture in a single program.

From tokenization to trusted settlement

Stablecoins have shown the demand for always-on programmable money. Tokenized deposits show that banks can respond without giving up the deposit model. CBDCs and tokenized reserves suggest a settlement anchor that may be needed for digital money to scale safely across institutions. The future of money isn’t just tokenized. It must be settled, governed and trusted.

If you'd like to discuss how your organization can prepare for the next phase of digital money—from tokenized settlement and interoperability to the operational capabilities required to support it—reach out to me for a conversation.

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As frontier AI models such as Anthropic's Claude Mythos and OpenAI's GPT-5.6 Sol continue to advance, much of the discussion has focused on cybersecurity operations: How quickly can vulnerabilities be discovered? How rapidly can exploits be developed? How should security teams adapt? These questions matter, but they’re only part of the leadership challenge.

In his blog, Defending at the speed of frontier AI: Lessons from the Fable shutdown and what to do in the next 60 days, Pierre Le Calvez outlines practical actions organizations can take now to strengthen preparedness and resilience. The next challenge is broader.

What happens when cyber risk becomes less about technology alone and more about an organization's ability to make decisions, coordinate action, and adapt at speed? Increasingly, that is the challenge frontier AI presents.

Pressure is expanding beyond the cybersecurity team

For years, cybersecurity leaders have focused on improving visibility through monitoring, threat intelligence, vulnerability scanning, and detection. Those investments remain essential, but frontier AI is changing where the pressure sits inside organizations.

As AI-assisted tools become better at discovering vulnerabilities, identifying weaknesses, and generating findings, the constraint increasingly shifts from discovery to decision velocity. Security teams may know about a vulnerability within minutes and understand its potential impact within hours. The harder question is whether the organization can decide, coordinate remediation, and implement mitigations quickly enough to keep pace.

In many organizations, the biggest delays no longer sit inside the security operations center. They sit in approval processes, competing business priorities, resource constraints, and fragmented ownership.

The bottleneck is increasingly organizational.

Why resilience matters more than ever

That is why resilience is becoming such an important differentiator. Organizations often think of resilience as recovery after a cyber incident, but it starts much earlier.

Resilience is the ability to absorb pressure, adapt to changing conditions, and continue operating effectively when the unexpected happens. In a frontier AI environment, resilient organizations tend to share several characteristics:

- Clear visibility into critical systems and dependencies

- Clear ownership and knowledge of key decision-makers

- Effective coordination across technology, security, operations, and business teams

- Regular testing of response processes

- A clear understanding of operational bottlenecks

Most importantly, resilient organizations can act quickly without sacrificing governance. That balance becomes more important as threat timelines continue to compress.

Governance is becoming a competitive advantage

Frontier AI is also introducing new governance questions. Organizations are increasingly dependent on external software providers, cloud platforms, open-source components, and AI services, while also introducing AI-enabled capabilities into business processes and operations. Together, these shifts create new forms of operational dependency and risk.

Boards and executive teams are increasingly asking questions such as:

- How do we govern AI adoption responsibly?

- How do we maintain appropriate human oversight?

- How do we manage AI-related third-party risk?

- How do we balance innovation and pace with resilience?

- How do we make decisions quickly while maintaining accountability?

These aren’t purely technology questions. They’re leadership questions.

The organizations adapting most effectively aren’t necessarily creating entirely new governance structures. Instead, they’re evolving existing risk, security, and resilience frameworks to reflect the realities of an AI-enabled operating environment.

Defending at the speed of AI

The phrase "defend at the speed of AI" is becoming increasingly common across the cybersecurity industry. In practice, it means recognizing that human-scale processes alone may no longer be sufficient for machine-scale challenges.

Defensive applications of AI are already helping organizations improve threat detection, vulnerability analysis, code review, incident investigation, and remediation prioritization. Used appropriately, AI can help security teams process information faster, reduce manual effort, and accelerate response.

The goal, however, isn’t to remove people from the process. Human judgment, accountability, and governance remain essential. The most effective organizations combine AI-assisted speed with disciplined oversight and operational control.

The objective isn’t full autonomy. It’s faster, better-informed decision-making.

The path forward with frontier AI

Much of the attention surrounding frontier AI has focused on the capabilities of the models themselves. The more important question may be how organizations choose to respond.

The fundamentals of cybersecurity remain remarkably consistent:

- Reduce unnecessary exposure

- Strengthen identity controls

- Improve visibility

- Accelerate remediation

- Test response plans

- Understand dependencies

- Modernize legacy environments where the risk justifies investment

What changes is the speed and discipline with which these activities must now operate. Organizations that strengthen resilience, improve governance maturity, and increase operational readiness will be better positioned, regardless of which AI model or technology emerges next.

The future of cybersecurity won’t be defined solely by better tools. It’ll be defined by better leadership, stronger operational discipline, and the ability to adapt confidently as technology continues to evolve.

In the age of frontier AI, resilience is no longer only a cybersecurity objective. It’s becoming a core business capability.

Building resilience for the frontier AI era

Frontier AI is compressing the time organizations have to understand risk, make decisions, and act. Explore CGI's frontier AI capabilities to see how we can help strengthen governance, resilience, and operational readiness so your organization can move at AI speed with confidence and control.

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For health and life sciences organizations, the AI conversation is changing.

The question is no longer simply where AI can be applied. Leaders now need to determine which investments can deliver measurable value at enterprise scale while managing cost, risk and accountability.

This creates an important executive challenge: How do you scale AI without allowing its economics to scale unchecked?

Part of the answer lies in understanding and governing AI consumption.

AI introduces a different cost equation

Health and life sciences organizations face rising patient demand, talent scarcity, tighter budgets, pressure to speed up research and development (R&D) and changing regulatory requirements. At the same time, many are moving AI from pilots into production. Our 2026 Voice of Our Clients research shows that leaders are prioritizing high-value AI use cases and strengthening governance as they pursue measurable business outcomes.

While traditional technology investments can often be planned around relatively familiar cost structures, Generative AI (GenAI) changes the equation. Consumption varies based on how models are used, which models are selected and how agents and applications are designed.

Tokens, the units processed by many GenAI models, are one component of that consumption. Individually, their cost may appear small. At enterprise scale, however, leaders need to know where consumption occurs and whether it creates sufficient value.

This is particularly relevant in health and life sciences, where AI applications can span clinical and administrative workflows, claims, R&D and other information-intensive processes.

Without effective governance, organizations can struggle to understand AI spending, allocate costs to the appropriate business solutions and products and establish accountability. Costs can also escape scrutiny during development and testing, making overall AI spending harder to forecast.

AI cost management therefore cannot begin after deployment. Organizations need to build it into their operating model before starting pilots.

Moving from cost visibility to accountability

Effective AI cost governance connects technical consumption to business ownership.

Leaders need to understand which AI resources teams consume, who consumes them, which use cases they support and what outcomes those use cases are expected to achieve.

A practical governance model establishes clear ownership, cost allocation principles and accountability for AI resources. Metrics and reporting can then help leaders monitor consumption and make informed decisions.

Similar challenges around cost governance emerged during the early adoption of cloud services. As organizations scaled their cloud environments, many saw costs grow rapidly without the governance and visibility needed to manage them effectively. Cloud FinOps emerged in response, bringing financial accountability, governance and cost optimization into cloud operating models.

As enterprise AI approaches a similar inflection point, the lesson from cloud is clear: organizations should establish the financial and governance disciplines for AI early, before adoption outpaces their ability to understand, control, and optimize its costs.

For generative AI, token consumption can be part of that picture. Tracking tokens at the appropriate level can help teams identify inefficient usage and assess design choices. Leaders can then evaluate that consumption against the business metrics that determine whether a use case warrants further investment.

The objective is not to minimize token use. It is to optimize AI consumption relative to the value it creates.

Connecting governance with architecture

Cost governance is not solely a finance responsibility. Architecture decisions also influence how efficiently organizations consume AI resources.

Cloud architectures that do not support effective cost management, limited use of capabilities such as autoscaling and automatic shutdown and the application of on-premises practices to cloud environments can weaken cost control. As organizations scale AI, technical, financial and business decisions need to support the same objectives.

Data governance is equally important. According to our 2026 VOC research, 54% of the health and life sciences business and IT leaders interviewed report having a holistic data strategy across their internal enterprise, while only 17% report a centralized approach to internal data governance. Across external ecosystems of partners and suppliers, those figures fall to 32% and 14%, respectively.

This gap matters because enterprise AI depends on trusted data and governance that extends beyond individual experiments.

Making economics part of enterprise AI governance

For health and life sciences executives, AI governance needs to address more than security, responsibility and compliance. It also needs to determine whether AI is economically sustainable.

Three actions can help:

- Establish ownership before scaling: Define who owns each AI resource and use case, who is accountable for consumption and how costs will be allocated.

- Measure consumption alongside outcomes: Monitor relevant cost and usage metrics, including token consumption where appropriate, and connect them to business measures. A lower AI bill is not necessarily a better result if it reduces the value of the application.

- Build continuous optimization into governance: AI services, pricing and purchasing options continue to change. Establish an ongoing process for reviewing costs, architecture choices and consumption patterns rather than relying on a one-time business case.

For more guidance on this topic, I invite you to read recent blogs from our global AI team: The CFO's AI token economy playbook: Why agentic cost management is becoming a boardroom issue and The AI token economy: Managing the hidden costs of agentic AI.

From AI experimentation to sustainable value

Health and life sciences organizations are moving from AI experimentation toward operational execution. Enterprise-ready AI will require modern foundations, meaningful metrics and human-led governance embedded in individual use cases.

Economic governance needs to be part of that approach. For years, organizations have relied on established governance structures, such as project management offices (PMOs) to oversee programs and projects, and enterprise architecture boards to guide technology and architecture decisions. Rather than creating entirely new governance layers for AI, organizations can build on these proven structures, embedding relevant AI considerations and decision rights into existing processes. This approach can strengthen AI oversight while minimizing unnecessary complexity and duplication.

As AI adoption expands, leaders need to understand both what their organizations consume and what they gain in return. Token consumption provides one useful measure, but the broader objective is to make informed investment decisions that connect AI costs with measurable outcomes.

For executives, the question is no longer simply, “How much AI are we using?” A more useful question is: “Are we governing AI consumption in a way that allows us to scale its value?”

If your organization is considering how to build economic governance into its AI strategy, I welcome the opportunity to compare perspectives. Reach out to me to discuss how health and life sciences leaders can plan for AI costs, accountability and value as they move from experimentation to enterprise scale.

Read Part 1 of this series, Digital reengineering in health & life sciences: From transformation ambition to measurable results, to explore why scaling AI with discipline is essential to turning transformation ambition into measurable business value.

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