1. A Simple Step with Direct Benefits

The value of a security.txt file is immediate and tangible. It creates a single, accessible point of contact for security researchers who may discover vulnerabilities and need to report them quickly. In a world where threats evolve constantly, the last thing you want is for helpful researchers to face hurdles in reaching your security team. This is a low-cost, high-impact way to enhance your responsiveness and streamline incident reporting.

Even if your company doesn’t have a formal bug bounty program, a security.txt file enables you to welcome and act on external security disclosures. It’s about setting the right tone and showing that your organization values security contributions from outside its walls.

2. Balancing Disclosure Rewards: When and How to Communicate

For companies that do offer rewards for disclosures, a security.txt file can serve as a transparent way to communicate program details—or signal openness to the possibility of a reward. If your bug bounty program is public, include it here to give researchers immediate clarity on how they might be compensated. But if your approach is more flexible, consider a simple statement like, “Contact for information on disclosure rewards,” which signals a willingness to discuss terms without committing to a rigid structure.

This approach lets you communicate interest without limiting options, allowing researchers to understand that their contributions are appreciated, even if a structured reward isn’t defined.

3. The Absence of Security.txt: A Missed Opportunity for Community Trust

Not having a security.txt file is more than a technical omission—it may signal a reluctance to engage with the security community. By skipping this simple step, companies can unintentionally communicate that they don’t value the efforts of ethical hackers, researchers, and white hats who could help secure their systems. In a world where collaboration is key to a resilient security posture, that’s a costly message to send.

This is especially true as your organization matures. For companies with well-developed security postures (a cumulative score of 2.0 or above on frameworks like NIST or MITRE), the lack of a security.txt file becomes harder to justify. As your security capabilities grow, consider how this minor addition can enhance your reputation and reflect a commitment to open, constructive partnerships with the community.

Conclusion: Strengthening Security Through Openness and Trust

Adopting a security.txt file isn’t just about creating a contact point; it’s a visible demonstration of your organization’s attitude toward collaborative security. When you create a clear, open channel for vulnerability reporting, you’re reinforcing a message that ethical researchers are welcome and valued. It’s an inexpensive way to foster trust, boost transparency, and align with best practices in security governance.

If your organization hasn’t yet implemented a security.txt file, consider the message this might be sending. In a time where trust is paramount, a small step like this can have outsized impact. Don’t let an oversight be mistaken for indifference—take the opportunity to signal your commitment to security and community.

Considering adding a security.txt file or want to explore more ways to strengthen your security program? Reach out—we’re here to help make security best practices accessible and actionable for your organization.

Figure 1. The Twitter Post that Inspired this Blog

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1. Start Small, But Think Big

Revolutions don’t always start with fireworks. They start with steady wins that build momentum. In AI, begin with small, budgetable projects—ones that can scale over time. These are about creating quick, valuable wins that prove AI’s worth to the business. But as you do this, keep the bigger picture in mind. These small steps should ladder up to a vision that’s much larger. With each project, you’re setting the stage for bigger transformations, paving the way for AI to eventually touch every corner of the organization.

2. Make Trust the Core Metric

In today’s IT landscape, trust is everything. The greatest silent threat to modern enterprises isn’t a technical vulnerability but shadow IT—the projects people start outside of sanctioned channels because they don’t trust IT to deliver. And with shadow IT comes unmanaged risk, scattered governance, and countless security gaps. To counteract this, focus on trust as your ultimate KPI. Trust isn’t measured by words; it’s seen in the number of projects in your backlog and the speed at which they’re delivered. If your backlog is robust and your delivery is steady, trust is growing. This isn’t just an IT metric; it’s a company-wide indicator of how aligned and connected your teams are. Bubble these metrics up, celebrate them, and make sure the whole organization knows that trust in IT is climbing.

3. Champions: The Lifeblood of Transformational Success

In AI and beyond, champions are everything. Champions don’t just amplify your work—they are the lifeblood of a culture of change. Think of them as the ultimate multiplier, bringing new projects to you and generating excitement for what’s possible. They’re the ones telling the story of AI’s value to their peers and advocating for your team’s contributions. The presence of champions signals that you’re creating a sustainable, scalable transformation that resonates at every level.

But here’s the kicker: champions don’t come from rigid structures or executive edicts. They’re grown organically, at the peer level, where their influence is strongest. Don’t force it or set arbitrary criteria; let champions emerge naturally, based on their enthusiasm and impact. Executive leaders can define what a champion looks like and provide air cover when needed, but let the team breathe life into it. Trust me, if you create an environment where people feel valued and rewarded for driving change, champions will come out in force.

And if you’re doing it right, champions will bring champions. With each new advocate in your ranks, you’re not only building momentum—you’re creating an unstoppable movement. A movement where your backlog is filled not by top-down initiatives but by genuine, grassroots demand for AI to make work better, faster, and more exciting.

4. Embrace Failure as a Learning Engine

The path to AI-driven success isn’t linear. It’s a loop of small experiments, constant adjustments, and, yes, failures. Each failure is just as valuable as a win; it’s a guidepost pointing out what doesn’t work so you can zero in on what does. If a project falters, don’t overanalyze. Just pick another approach, adjust, and try again. Like any scientist, identify your variables, change one at a time, and see what sticks. Failure, in this context, isn’t the enemy—it’s a tool for refinement, a path to the ideal solution.

5. Build a Culture of Feedback and Recognition

For this revolution to succeed, feedback must flow freely. You want all ideas—not just the “good” ones. Keep feedback channels open and easy, and make sure people know they’re being heard. Even when a suggestion doesn’t pan out, employees should feel valued in the process. Celebrate wins loudly and visibly. Acknowledge everyone who contributes to a successful project, regardless of their role. Set up a dashboard to track accepted ideas and feature requests, and make it public. Broadcast the wins far and wide—in newsletters, on office screens, in board reports. Recognition shouldn’t be just an afterthought; it should be a cornerstone of the culture you’re building.

Rewarding each accepted idea, even in small ways like a coffee gift card, creates a culture where people feel inspired to bring their best ideas forward. It’s not about setting up hoops to jump through; it’s about creating a space where people are excited to contribute.

6. Lead the Charge, Don’t Micromanage the Details

Your role as a leader isn’t in the trenches; it’s in the vision. Enable your team to succeed by setting the direction, then letting them own the journey. Guide, support, and celebrate their wins, but resist the urge to do the work for them. Give them the autonomy to test, iterate, and implement. This approach builds both capability and confidence, giving your team the space to become their own champions for change.

7. When You’ve Built Enough Champions, Scale Up

When the number of champions in your organization reaches a critical mass, you’ll have the trust and support to move from smaller projects to transformative ones. By then, your backlog will be brimming with projects that have organic buy-in, and your team will be experienced enough to handle larger, more complex initiatives. This is where the revolution goes full-scale. And remember: the more you focus on trust, champion growth, and continuous feedback, the easier it will be to sustain this momentum.

Call to Action: Seize the Revolution

The era of incrementalism is over. This is your chance to redefine what it means to be a transformational leader. Trust, champions, and a culture of continuous learning aren’t just buzzwords—they’re the foundation of an AI-driven revolution that you, as a tech leader, are uniquely positioned to lead. Don’t just let AI happen to your organization; use it to drive unparalleled value and unleash your team’s true potential.

And if you’re ready to go deeper, to push harder, and to make this transformation a reality, let’s talk. Reach out to me and my team to explore how we can support you on this journey. Together, we’ll make sure your organization doesn’t just adopt AI but thrives because of it.

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1. Define Roles: Professional Developers vs. Nonprofessional Developers

With AI’s rise, coding is becoming accessible to individuals beyond traditional tech roles. For nonprofessional developers—employees who incorporate coding into their roles but aren’t exclusively focused on development—the focus should be on fostering curiosity and exploration.

Professional developers take on an expanded role. Their responsibilities extend beyond governance and scalability; they must become mentors who support nonprofessional developers by understanding business needs and guiding them to achieve goals effectively and securely.

Takeaway: Structure your team with clear roles. Nonprofessional developers focus on experimentation, while professional developers emphasize governance, scalability, and mentoring.

2. Let AI Handle Repetitive Tasks—But Maintain Human Oversight

AI should take on the repetitive, time-consuming tasks developers tend to avoid—documentation, vulnerability checks, and testing. But don’t expect AI to fully understand your business context, risks, or compliance standards. This knowledge comes from human oversight, not from AI.

While AI’s ability to find and fix issues is faster than human work, blind trust isn’t an option. Start today with a scientific approach: run A/B tests on AI-driven and manually executed work, comparing results across experience levels. Use this data to build confidence and assess AI’s strengths and limitations objectively.

Takeaway: Leverage AI for efficiency, but measure its impact. Use A/B testing to build trust while maintaining human review until confidence in AI is earned.

“CTOs: AI isn’t just another tool—it’s a revolution. To lead in a billion-developer world, you need to redefine your operating model and make AI a core part of your strategy.”

3. Measure AI’s ROI Through Time Savings and Operational Change

When evaluating AI’s impact, time saved is the most telling metric. How much time did your team spend on testing, documentation, or process improvements before AI? Tracking these metrics before and after AI adoption reveals a clear ROI and shows how well AI is transforming your operating model.

Takeaway: Track time savings as a core metric of AI’s value. View AI’s impact as a shift in your operating model, not a one-off improvement.

4. Build Trust in AI: Experiment, Test, and Upskill

Integrating AI effectively means upskilling your team based on their strengths. Those with strong interpersonal skills should focus on prompt engineering, as they’re likely to find it intuitive. For others, prioritize technical AI skills, enhancing interpersonal abilities only where it will deliver a direct return.

This approach allows leaders to upskill team members to provide the most immediate value while fostering a culture of adaptability to AI. Everyone becomes proficient in prompt engineering, but team members also develop in areas where they can make the biggest impact.

Takeaway: Tailor AI training to each team member’s strengths, focusing on prompt engineering and enhancing soft skills where they’ll have the most impact.

5. Treat AI as a Product—You Are the Product Manager

With AI evolving rapidly, CTOs need to act as product managers for AI within their organization. Don’t let AI decisions become “settled”; instead, reassess them regularly. In a world where sticking with a single model for six months can leave you behind, constant agility is essential.

For highly regulated industries, this means creating a repeatable review and approval process for AI models that balances innovation with compliance. Agile model evaluation is part of the CTO’s role, as is building a culture of continuous AI integration.

Takeaway: Constantly reassess AI decisions. Build a model evaluation process that enables agility without sacrificing compliance, especially in regulated industries.

6. Set the Foundation with an AI Design Guide Focused on Data Quality

AI lives and dies by data quality. CTOs should start by defining an AI design guide for the organization, ensuring that every AI project is backed by high-quality data. This guide should set standards for aligning AI with organizational goals, including levels of human oversight based on data sensitivity. Projects with low data risk may require minimal oversight, while sensitive applications need more rigorous review.

It’s also important to set clear expectations around AI adoption and support. CTOs should actively endorse AI, provide time for training, and communicate that this investment will pay off long-term.

Takeaway: Begin with an AI design guide that defines data standards and oversight levels. Show commitment to AI and foster a culture of learning and growth.

Conclusion

As AI reshapes development, CTOs must adapt to a world where coding and innovation extend beyond the technical experts. This evolution requires strategic oversight, flexibility, and an unwavering focus on data quality. The competitive edge lies in building an organization where AI is integrated thoughtfully, securely, and with full executive alignment.

If you’re ready to make AI a product your organization can trust, my team and I are here to help. Let’s build an AI-ready operating model together—one grounded in agility, data quality, and strategic oversight.

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Here are the key actions CIOs should take to ensure they’re not just managing IT, but actively enabling their organizations to grow, innovate, and transform in 2025 and beyond.

1. Build AI Literacy and Trust Within IT First

The first step in leading AI transformation is starting within your own organization. CIOs should focus on building AI literacy programs within their IT teams, ensuring they have a solid understanding of what AI can do and how it applies to their work. This is where quick wins come into play—focus on immediate pain points within IT, such as improving operational efficiency or automating repetitive tasks, to deliver fast results. These early wins will create internal champions who can advocate for AI, helping spread the message across the organization.

Ask Yourself: Am I starting with quick, high-impact AI initiatives within my own team that can demonstrate real value? Have I identified the internal champions who will sell the success of these initiatives to their peers?

“AI isn’t just a tool—it’s your path to transformation. If you’re still managing technology, you’re missing the point.”

2. Win Hearts and Minds by Making AI Personal and Measurable

To ensure sustained AI adoption across the business, CIOs must focus on making the workday easier for employees. Every AI initiative should have two clear outcomes: personal impact on employees and quantifiable data for leadership. By showing how AI simplifies tasks or enhances productivity for individuals, while simultaneously delivering metrics that prove its impact, CIOs can win over both employees and leadership. This balance avoids the risk of AI feeling like “big brother” and ensures that AI is seen as a value-add, not a threat.

Ask Yourself: Are my AI projects producing measurable business value while also making a positive difference in employees’ daily work? Am I balancing these two outcomes to ensure broad adoption and trust?

3. Start with Existing Problems to Drive Dynamic Capacity

When it comes to AI-enabled dynamic capacity, the key is to start where the company’s current bottlenecks are. Whether it’s production outpacing logistics, supply chain inefficiencies, or gaps in customer service, target the areas where problems already exist. By using AI and automation to solve these issues, CIOs can deliver immediate value that resonates across the business. Once that first problem area is resolved, the ripple effects will spread, allowing you to expand AI adoption gradually, eventually moving the entire operational chain from data-informed to data-driven decision-making.

Ask Yourself: Am I focusing AI efforts on the biggest pain points in the business today? Have I built a feedback loop to expand AI and automation from these problem areas out to the rest of the organization?

4. Keep Human Oversight Until Trust is Earned

The shift from data-informed to fully data-driven decision-making doesn’t happen overnight. It requires building trust in the data. Until teams trust the data enough to follow its guidance without hesitation, human oversight is essential. Once you reach the point where the organization consistently relies on data and follows AI’s lead without doubts or complaints, you can start to introduce more prescriptive AI models. This gradual shift ensures the transition is smooth and minimizes resistance.

Ask Yourself: Is my team ready to trust AI and data-driven decisions, or do we need more time with human oversight to build confidence? How can I help foster that trust through smaller wins?

5. Collaborate with HR to Lead the Human-Machine Workforce

Integrating AI into the workforce is a delicate balance, and collaborating with HR is critical to success. CIOs must build a strong relationship with HR leaders, focusing on creating AI literacy programs for the broader organization and preparing for a human-machine workforce integration. By aligning early with HR, CIOs can co-lead this transition, ensuring it’s done thoughtfully and with employee trust at its core. The focus here should be on building trust first, so that when it’s time for transformation, both sides are ready to lead together.

Ask Yourself: Have I built a strong relationship with HR to co-lead AI-driven workforce changes? Am I preparing the organization for this integration before it becomes an imperative?

6. Lay the Foundation with Accurate, Trustworthy Data

For AI and dynamic capacity to succeed, data is king. Moving from a fixed to dynamic capacity model requires accurate, timely, and trustworthy data. One of the first steps in this process is establishing a standardized lexicon of business terms and data definitions across the company. There should be one definition of a sale, a customer, or an employee. With a unified understanding of these core metrics, the organization can then scale AI and automation initiatives with confidence.

Ask Yourself: Is my organization’s data consistent and trustworthy? Have we established a common language across the business to ensure that AI initiatives are built on a strong foundation?

7. Balance Innovation with Security from the Start

Security should never be an afterthought. In the rush to innovate and adopt AI, security must be considered before defining or quantifying the value of any project. This means working closely with the CISO from the beginning to ensure security is a core component of every AI and automation effort. By reducing friction between IT and cybersecurity teams and presenting a unified front, CIOs can streamline innovation while ensuring the organization remains protected.

Ask Yourself: Is security baked into my AI and data initiatives from the outset? Am I working closely with the CISO to reduce friction and create a seamless, secure environment for innovation?

8. Scale AI Adoption by Creating an Executive Steering Committee

Once you’ve gained momentum from smaller wins, it’s time to scale. As leaders see the success of early AI initiatives, they’ll naturally be more willing to commit to larger projects. At this point, CIOs should create an executive steering committee, comprised of key decision-makers from across the organization. This committee will help prioritize AI initiatives based on cost/benefit analysis and will ensure that future projects have executive buy-in from the start. Keep the group small, focusing on CIO peers and those who can actively contribute.

Ask Yourself: Do I have the right executive steering committee in place to help scale AI initiatives? Am I leveraging the early success of AI projects to build further momentum across the leadership team?

Conclusion

The role of the CIO is evolving, and CEOs are looking for leaders who can drive AI transformation, build dynamic capacity, and manage the shift toward a human-machine workforce. By focusing on small, personal wins, building trust in data, and collaborating closely with HR and cybersecurity, CIOs can lead their organizations through these complex transformations with confidence.

If you’re unsure how to take these steps or need guidance on how to align your AI initiatives with CEO priorities, my team and I are here to help. We have the experience to guide you through the process, ensuring your organization is set up for success and that you’re positioned as a trusted advisor at the executive table.

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Gartner touched on some of these points, but I believe they didn’t go far enough in addressing the critical questions CIOs should be asking themselves. The modern CIO is no longer just a technology steward—they are central to driving business strategy, enabling digital transformation, and embedding technology across the enterprise in meaningful ways.

Below is my actionable guide for CIOs—a blueprint for becoming the force for innovation your organization needs. If you’re ready to make bold moves, these are the steps you need to take.

1. Forge Strong, Tailored Relationships with Each CxO

Instead of approaching each CxO with the standard “tech equals efficiency” pitch, CIOs should actively engage with them to uncover deeper business drivers.

• CFO: Go beyond cost management. Understand the financial risks the company faces, such as cash flow volatility or margin pressures, and find ways technology can mitigate these risks.
• COO: Focus not just on operational efficiency but on process innovation—how can technology fundamentally change how work gets done, not just make it faster?
• CMO: Delve into the customer journey and experience. Understand how technology can be a key differentiator in enhancing customer intimacy or scaling personalization efforts.
• CHRO: Understand their challenges in talent acquisition and employee engagement. How can technology make the workplace more attractive, productive, and aligned with HR strategies to develop talent?
• Product/BU Leaders: Work closely to drive product innovation, not just from a technical perspective but to discover how technology can create competitive advantages or new revenue streams.

Ask Yourself: Do I truly understand what drives each of my CxOs at a strategic level, or am I stuck thinking in tech terms? If I don’t have the insight I need, what steps can I take to get there—and am I leveraging external expertise where needed to fill the gaps?

2. Prioritize Based on Shared Commitment and Strategic Value

Not all CxOs will be equally engaged or ready to partner closely with the CIO, but this should influence prioritization. CIOs should assess:

1. CxO Commitment: Is the CxO fully bought into digital transformation and willing to invest time and resources? If they aren’t, start with those who are.
2. Technology Team Enthusiasm: Does the ask from the CxO spark excitement within the technology team? If the IT team can see the challenge as an inspiring and innovative project, prioritize it.
3. Potential for Broader Impact: Will this initiative create a success story that can inspire other parts of the business? Choose projects that not only solve immediate problems but also demonstrate value to other BUs.
4. Business Impact: Does this move the needle enough? Focus on projects that are impactful enough to gain visibility and drive momentum across the organization.

Ask Yourself: Am I working with the most committed and strategic partners, or am I spreading myself thin trying to please everyone? How can I ensure my efforts focus on high-impact initiatives that inspire others? If I’m not sure which projects have this potential, who can I turn to for a fresh perspective?

3. Develop a Communication Strategy to Be the Executive Team’s Trusted Advisor

The CIO needs to craft a communication strategy to regularly update the C-suite on what’s happening in technology, why it matters, and—most importantly—how it applies to their specific business challenges. This is not about sending generic updates or forwarding research articles.

•  Provide insights on emerging trends like AI, automation, or cybersecurity, and explain how they can solve real problems or create real opportunities for their business.
• Create a visionary narrative that places your company at the forefront of industry evolution, emphasizing how specific technologies will help each CxO achieve their goals.

Ask Yourself: Do I have a proactive communication strategy that positions me as the go-to advisor for technology insights within the C-suite? Am I demonstrating how technology directly impacts their business outcomes? If I’m struggling to create this narrative, who can help me fine-tune it?

4. Champion Digital Experience (DX) and Build KPIs Around Adoption and Value

While the CIO doesn’t need to own the day-to-day design conversations, they must champion the importance of digital experience (DX) and ensure that it’s a KPI across the company. Build a culture where every digital initiative is measured not just by completion, but by how well it’s adopted and how it sustains value over time.

• Ensure KPIs include sustained usage, not just launch metrics.
• Build Management by Objectives (MBOs) that tie DX and adoption rates into performance metrics for teams using the tools, ensuring continuous focus on the user experience.

Ask Yourself: Am I setting the right metrics to measure the long-term success of digital initiatives, or am I just tracking short-term implementation? How can I establish sustained adoption as a core business KPI? And if I don’t have a strong framework in place, who can help me build it?

5. Cultivate Multidisciplinary Fusion Teams with Curious, Collaborative Members

Create multidisciplinary fusion teams where business and IT collaborate on solving real business problems. Initially, look for those who are naturally curious and collaborative—people who are eager to break down silos and innovate. As you scale, formalize selection processes but ensure that it doesn’t become a bureaucratic process. Encourage progress-driven contributions, where results are measured and where teams feel empowered to iterate, rather than meet to discuss roadblocks endlessly.

Ask Yourself: Am I identifying the right people to drive multidisciplinary collaboration, or am I waiting for teams to form on their own? Are my teams making progress, or are they stuck in meetings that don’t lead to results? Who can I consult to get these teams moving in the right direction?

6. Be the Early Advocate for Emerging Technologies

Emerging technologies like AI, automation, and low-code/no-code platforms are already enterprise-ready but often fail due to a lack of understanding of how to drive real business value. CIOs must be early advocates for these technologies, preparing the organization to adopt them when they’re at the right point on the maturity curve. This prevents shadow IT from adopting technologies outside the CIO’s purview and ensures that IT is seen as an enabler, not an obstacle.

Ask Yourself: Am I advocating for emerging tech early enough, or am I waiting too long to act? How can I ensure the organization is ready when the technology hits the right maturity curve? If I’m unsure where to start, who can help me assess our readiness?

7. Foster a Culture of Cross-Functional Digital Leadership

Create an organic ecosystem where IT leaders move into business roles and business leaders spend time in IT. This exchange creates a more integrated understanding of how technology drives value across the business. Work with HR to launch a pilot exchange program with a willing BU, and ensure that this doesn’t become another bureaucratic initiative. Instead, keep it agile, fast, and focused on creating leaders who are equally strong in tech and business.

Ask Yourself: Am I fostering an agile and collaborative environment where digital leadership can flourish across functions? Or are we too siloed in our thinking? If I need guidance on how to get this started, who should I bring in to help make it happen?

8. Align Technology Outcomes with Clear Business Goals

Every tech project must have clear business goals and measurable metrics that matter to the business. Don’t aim for perfection—aim for progress. Track and report metrics regularly to keep the project’s business value visible to stakeholders.

Ask Yourself: Are all my technology projects aligned with clear business goals, and do I have the right metrics in place to measure their impact? If I don’t have a process for this, what support do I need to create one that works?

9. Track Adoption and Engagement Metrics Beyond the Initial Rollout

Adoption isn’t just about getting users on board for launch—it’s about measuring ongoing engagement. CIOs should track:

• Satisfaction rates: How do users feel about the tool or platform over time?
• Improvement metrics: Are there measurable improvements in efficiency, productivity, or revenue tied to the tech?
• Feature requests: How often do users ask for new features or enhancements?
• Number of users/BU’s using the platform: Track growth or stagnation in usage across teams.
• New projects spawned from existing tech: What new initiatives are being created because of successful platform use?

Ask Yourself: Am I tracking the right metrics to measure long-term success and adoption, or am I too focused on the initial rollout? If I’m unsure of how to keep engagement high, who can I turn to for expert advice on optimizing these KPIs?

Transformation doesn’t happen by chance, and it won’t happen if CIOs stay in the background, waiting for others to drive change. It requires intentional, strategic action, a commitment to aligning technology with business outcomes, and a willingness to ask the tough questions. The steps I’ve outlined are designed to challenge your thinking, help you prioritize where to focus your efforts, and ensure you’re seen as a leader, not just a technologist.

If you’re unsure how to move forward or need guidance in turning these insights into action, remember that you don’t have to go it alone. My team and I have worked with CIOs across industries to turn complex challenges into strategic advantages, and we’re here to help. Becoming an agent of transformation starts with taking that first step—and we’re ready to walk with you through the journey.

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