Strategyminds UG

Inspire a New Vision: Private equity leaders in Latin America are on the cusp of a transformation. Traditionally, post-acquisition playbooks have leaned heavily on cost reduction, operational streamlining, or expanding into new markets to drive value. Those levers remain important – but artificial intelligence (AI) is now emerging as a far more powerful lever of value creation, one that promises exponential performance gains. Forward-thinking Executives see that AI can boost productivity and optimize processes at unprecedented scales while uncovering deep customer insights that fuel growth. In fact, many top executives agree that adopting AI is no longer optional: seven in ten CEOs worldwide say their companies must embrace AI or risk falling behind . For PE firms, this means reimagining the playbook – moving AI to the center of their value creation strategy – to unlock outsized returns and build future-ready portfolio champions.

AI as an Exponential Value Driver in Portfolio Companies

AI’s impact goes beyond incremental improvements; it offers step-change gains that amplify traditional value levers. While a typical efficiency program might trim costs by a few percentage points, AI-driven automation can slash turnaround times and error rates by orders of magnitude, fundamentally changing cost structures. On the growth side, AI opens new frontiers: advanced analytics and machine learning can parse vast datasets to reveal customer behaviors and untapped market niches that humans would miss. By leveraging AI, companies can personalize product offerings at scale, predict demand with uncanny accuracy, and even create new data-driven revenue streams. Generative AI and advanced algorithms are accelerating everything from product development to marketing – boosting both top-line and bottom-line performance simultaneously.

For example, using proprietary data alongside AI has been shown to drive anywhere from a 10% to 45% increase in sales for consumer goods companies . This kind of uplift – double-digit revenue growth from data that was always there, now harnessed by AI – illustrates the untapped potential in many businesses. Similarly, AI-powered process optimization can dramatically improve productivity: consider manufacturing plants using AI for predictive maintenance to eliminate downtime, or service companies deploying AI chatbots that handle thousands of customer queries instantly, freeing staff to focus on high-value tasks. These are exponential gains – not just doing the same work a bit cheaper, but doing things fundamentally faster, smarter, and at greater scale than before.

Crucially, AI doesn’t replace the need for sound business strategy; it augments it. The best PE firms are already blending AI into their investment thesis and operations. In 2024, private equity is rapidly moving beyond pilot projects in back-office automation towards enterprise-wide AI deployments – using AI for everything from smarter deal sourcing and due diligence to real-time performance monitoring in portfolio companies . And within portfolio businesses, generative AI is becoming a direct value driver, turbocharging classic initiatives like cost takeout, pricing optimization, and customer experience enhancements . The message is clear: AI is a force-multiplier for the traditional playbook. It allows a PE-backed company not only to trim costs more efficiently than competitors, but also to innovate and capture market share faster, creating a gap that purely traditional approaches simply can’t bridge.

Local Constraints vs. Global Tools: Surmounting the Barriers

Of course, Latin America’s business environment presents unique challenges that any AI strategy must navigate. Fragmented infrastructure, inconsistent data quality, and talent shortages are real concerns in Colombia and across the region. However – with the right strategy – these constraints are surmountable. In fact, global tools and best practices can help leapfrog many local barriers, turning challenges into opportunities for competitive advantage.

• Infrastructure Gaps: Latin America has made huge strides in connectivity, but gaps remain. As of 2022 only about 67% of households in the region had internet access (versus 91% in OECD countries) . High-speed broadband and reliable cloud services are still uneven, with robust hubs in some countries (like Brazil) and weaker networks elsewhere . This fragmented digital infrastructure could slow AI adoption – but here’s where global cloud and technology providers become game-changers. Today, a company in Medellín or Bogotá can tap into the same AI cloud platforms used in Silicon Valley, without needing local data centers. Major cloud providers (AWS, Microsoft, Google) have been expanding in LatAm, and even where on-premises infrastructure is lacking, firms can leverage cloud-based AI tools delivered over the internet. Yes, governments and telcos must continue improving broadband reach (and the trend is positive every year), but PE leaders can plan AI rollouts assuming cloud access as a given, even building hybrid solutions to cope with spotty connectivity. The key is strategic investment: upgrading connectivity for your facilities, using edge computing for critical real-time operations, and partnering with telecom providers if needed to ensure your portfolio companies have the bandwidth to run AI solutions. By piggybacking on global infrastructure, even mid-sized companies in Colombia can harness world-class AI computing power – essentially overcoming the local hardware gap with global cloud tools.
• Data Quantity and Quality: Data is the fuel of AI, yet many Latin American companies have historically struggled with data management. Challenges range from a lack of quality data (or data not digitized at all) to inaccessible, siloed databases and low interoperability between systems . Furthermore, a cultural reluctance to share data across departments or partners can stifle AI projects that need diverse data inputs . However, these issues can be resolved with a deliberate strategy in the first phase of any AI initiative. The 100-day plan should include a data audit and acceleration of data governance efforts: identify what data exists, clean and consolidate it, and establish pipelines to start capturing new data consistently. Many Latin American firms are sitting on “dark data” – information collected but not used – which can often be unlocked quickly. Where data is missing, consider quick wins like digitizing key processes (for example, moving paper-based operations to mobile apps or IoT sensors to generate real-time data). Also, leverage global tools for data handling: modern ETL (extract, transform, load) platforms, data lakes, and even open data sets that can enrich your models. The lack of historical data is often cited as a barrier, but organizations can start small with AI on whatever data is available and iterate – machine learning models can be retrained as data quality improves. What’s critical is to instill a data-driven culture: encourage every decision to be backed by evidence and analysis. With proper data governance and the adoption of cloud data warehouses, even companies that were “data-poor” can rapidly become data-rich, feeding the AI engine with high-octane fuel. It’s a classic case of getting the house in order– once data begins to flow and improve, the AI algorithms can deliver dramatically better insights and predictions.
• Talent Shortages: Perhaps the most cited constraint is the lack of specialized AI talent in the region. Latin America simply has fewer seasoned data scientists and AI engineers than, say, the U.S. or Europe, and competition for these experts is fierce. In a recent survey, 79% of IT companies in LatAm anticipated difficulty filling AI-related roles by 2024 , and in markets like Mexico over two-thirds of employers report hard-to-fill tech vacancies . The talent gap is real – but a savvy strategy can mitigate it. First, consider a “build, partner, or buy” approach: not every portfolio company needs an army of PhDs in-house. PE firms can partner with AI vendors or specialized startups to quickly bring in expertise for specific projects (many startups in the region and globally offer AI-as-a-service for things like customer analytics or supply chain optimization). Additionally, leverage the global talent pool: remote work and freelance platforms mean a company in Colombia can hire a data scientist based in Buenos Aires or Bangalore just as easily as one in Bogotá. We’re seeing more Latin American firms tap into nearshore talent exchanges and even reverse brain-drain by luring back local experts who trained abroad. Second, and most importantly, invest in upskilling your existing workforce. Train your analysts, engineers, and domain experts in the basics of data science and AI tools – enough to bridge the gap between business needs and technical implementation. Many cloud AI platforms (from Azure’s AI Studio to Google’s AutoML) are becoming more user-friendly, enabling semi-technical staff to develop AI models. By providing targeted training (perhaps through portfolio-wide programs or partnerships with universities), PE firms can cultivate a pipeline of AI-capable talent internally. It’s also worth nominating “AI ambassadors” or champions within each portfolio company – people who get additional training and then disseminate knowledge to their teams. The bottom line: while LatAm’s AI talent shortage won’t disappear overnight, through creative resourcing and aggressive upskilling, PE firms can ensure each portfolio company has the skills needed to execute AI projects. The hunger to learn is there; in fact, more than half of Latin American employees say they are excited to use AI tools in their jobs – they just need the support and training to do so .
• Other Headwinds (and Opportunities): Regulatory uncertainty around AI and data privacy is another local concern, as laws vary country by country and are still evolving. PE leaders should stay ahead of this by implementing strong AI governance frameworks proactively (ethical guidelines, data compliance checks) – turning responsible AI use into a competitive advantage rather than a risk. Moreover, Latin America’s economic diversity (mix of developed urban markets and underserved rural populations) means AI initiatives must be tailored: solutions for a Bogotá or São Paulo customer base might differ from those for smaller cities or rural areas with lower connectivity. Instead of seeing this as a problem, view it as a space for innovation. For instance, AI-driven mobile apps can bring services to remote regions (in finance or telehealth) in cost-effective ways that traditional models couldn’t. By aligning AI projects with social needs (like financial inclusion or agricultural efficiency), PE firms can unlock new markets while also meeting development gaps. The key is a flexible, context-aware approach – and again, learning from global tools and case studies. Successful AI deployments in Southeast Asia or Africa, for example, might inspire models that work in Latin America’s fragmented landscape.

The common thread in all these constraints is that none are insurmountable. Each challenge – be it infrastructure, data, or talent – has a strategy and a set of tools that can resolve it, often by leveraging innovations and resources that are global in nature. The role of the PE leader is to bring those global best practices into the local context. Indeed, Latin America has an advantage as a “late-mover” in some areas: we can learn from mistakes made elsewhere and adopt cutting-edge solutions directly (for example, implementing the latest cloud-native data architecture instead of dealing with legacy on-premise systems). This is the essence of “Local Constraints, Global Tools” – using world-class technology and know-how to overcome local hurdles, thereby propelling portfolio companies to a level of performance that competitors stuck in an old mindset cannot match.

Structuring AI into the 100-Day Plan and Beyond

How can private equity firms put all this into practice? It starts on Day 1 of ownership. The first 100 days of a new acquisition are the critical window to set the tone and lay the foundation for transformative change. By deliberately structuring AI-driven value creation initiatives into the 100-day program, PE leaders ensure that digital transformation doesn’t remain a vague aspiration but becomes an actionable, funded part of the business plan.

Launch with a vision and quick wins: In the initial weeks, the CEO and deal team should articulate a clear vision for AI in the company’s future – essentially, answer how will AI make this business fundamentally better and more valuable? This vision might include becoming a data-driven leader in their sector, dramatically improving customer experience through personalization, or automating a complex workflow to become the low-cost producer. With that vision in mind, conduct an AI readiness assessment. Evaluate the company’s current data assets, IT infrastructure, and analytic capabilities. Identify 3-5 high-impact use cases where AI could either reduce costs, boost revenue, or mitigate a key risk. It’s crucial to prioritize use cases that align with the investment thesis and are feasible with available data. For instance, if the thesis depends on margin expansion, look for AI opportunities in operational efficiency (say, optimizing supply chain logistics or automating credit scoring to reduce defaults). If growth is the play, focus on customer analytics or AI-driven marketing optimization.

From this assessment, pick one or two “quick win” AI projects to initiate within the first 100 days. These should be projects that are narrow enough to implement or pilot rapidly (in a matter of a few months or less) but significant enough to demonstrate value. Examples might be deploying a chatbot on the company’s website to handle customer inquiries (reducing call center volume and improving response times), or using a machine learning model to re-price inventory more dynamically. The goal is to show early results – build momentum and credibility for AI by delivering something tangible. Quick wins not only provide direct benefits but also serve as case studies within the organization to overcome skepticism.

Build the enablers immediately: Alongside quick wins, the 100-day plan should kick off essential enabler initiatives. This includes shoring up data infrastructure – for example, set up a cloud data warehouse or data lake if one doesn’t exist, so that all company data can start to be centralized and structured for analysis. Begin capturing new data if needed (e.g., start logging machine sensor data or customer clickstreams). Also, address any low-hanging fruit in technology upgrades: ensure the company has the necessary software tools and access to cloud AI services. Secure outside expertise early as well. If the company lacks data scientists, consider hiring a consulting firm or an independent expert to jump-start the first projects. Many top PE firms now maintain a bench of functional experts in-house (or on-call consultants) with deep knowledge of areas like advanced analytics and AI . Use them – bringing in an experienced data science lead for a few months can massively accelerate the learning curve for a portfolio company. As McKinsey advises new portfolio CEOs, develop an actionable plan grounded in reality, and don’t hesitate to augment your team’s capabilities with external help in these early stages . The first 100 days are about building the launchpad for AI: the core team, tools, and initial projects that will set the trajectory.

Integrate AI into the value creation roadmap: Beyond the first 100 days, AI initiatives should be woven into the longer-term transformation roadmap (the 1-3 year horizon of the investment). This means setting specific milestones for AI adoption over the next several quarters. For example, by month 6, maybe the goal is to have a pilot of an AI-driven predictive analytics system running in production. By the end of year 1, the company might aim to have at least two core business functions (say, marketing and operations) using AI insights regularly in decision-making. Over 2-3 years, the vision could be to develop proprietary AI capabilities that become a competitive differentiator – such as a unique recommendation algorithm, or an AI-optimized supply chain that gives a cost advantage. Each quarter’s operating review should include progress on digital initiatives as a key metric, right alongside financial KPIs. This keeps focus and accountability on the AI transformation, not letting it drift.

Importantly, the AI roadmap should sync with the overall investment thesis timeline. Private equity holding periods (often around 4-6 years) define a window in which the company’s value must be significantly elevated. AI projects should be planned such that their biggest impact is realized well before exit. If done right, by the time the PE firm is ready to exit (sale or IPO), the company can be presented as a tech-enabled, AI-savvy leader in its industry – which can command a valuation premium. We’ve seen multiples expand for companies perceived as digital pioneers, because buyers value the future earnings potential and resilience that comes with analytics-driven decision making. In effect, embedding AI in the organization’s DNA is itself a value creation event. It’s not just the immediate cost savings or revenue gains from AI projects, but the strategic repositioning of the company as a more innovative, efficient enterprise. Savvy acquirers will pay for that.

Use a structured approach with flexibility: Executing an AI-driven transformation is a complex endeavor, so a structured program management approach helps. Many firms create an “AI task force” or digital transformation office within the portfolio company to coordinate all the moving parts (tech, data, HR, vendor management). This task force, supported by the PE firm’s operating partners, can ensure that initiatives stay on track and that learnings are shared across projects. However, maintain flexibility – the AI field is rapidly evolving. The roadmap should be revisited at least annually to incorporate new technologies or adjust course based on what’s working or not. Perhaps a machine learning initiative yields breakthroughs in one area, suggesting new opportunities; or perhaps a chosen technology doesn’t pan out, meaning an alternate solution is needed. Agile project management (short cycles, regular reassessment) is ideal here. The mindset to instill is one of continuous improvement and iteration, guided by the strategic North Star of the investment thesis.

Finally, incorporate AI considerations into 100-day plans of every new acquisition going forward. Just as any playbook would immediately look for cost synergies or quick commercial wins, it should now also scan for digital/AI upside from day one. Over time, PE firms can refine a repeatable model – an AI playbook template – that can be applied, of course tailored to each company. This might include checklists of common AI use cases by industry, lists of approved technology vendors, and frameworks for evaluating ROI on AI projects. By making AI integration a standard part of post-acquisition planning, firms ensure they capture this lever in each deal. It’s akin to adding a new chapter to the traditional 100-day playbook – one that didn’t exist a decade ago, but is now essential.

The Latin American Advantage: Energy, Openness, and Hunger for Change

It’s easy to focus on the hurdles in Latin America, but equally important is recognizing the region’s unique advantagesin adopting and benefiting from AI. Colombia and its neighbors are not starting from zero in this journey – in many ways, the business culture and macro trends in LatAm provide fertile ground for an AI revolution. Let’s flip the narrative: rather than a lagging region that must catch up, Latin America can be seen as an agile innovator ready to leapfrog in certain areas, powered by an entrepreneurial spirit and a hunger for transformation.

A Surge of Tech Entrepreneurship: Nowhere is Latin America’s energy more evident than in its booming startup ecosystem. In recent years, the region has witnessed an explosion of tech startups, including those focused on AI. For example, the number of AI startups in countries like Brazil, Mexico, Chile, Colombia, and Argentina has skyrocketed between 2018 and 2024 . Colombia, notably, saw a 669% increase in AI startups during this period, reflecting an entrepreneurial surge in advanced tech innovation. Overall, Latin America is now home to an estimated 30 to 40 unicorn startups – more billion-dollar tech companies than Germany or France have . Homegrown successes like fintech giant Nubank (Brazil), on-demand delivery pioneer Rappi (Colombia), e-commerce leader MercadoLibre (Argentina) and many others showcase a generation of founders willing to disrupt traditional industries with technology. This matters for AI adoption in PE portfolio companies: the entrepreneurial mindset is infectious. Employees and managers in the region have seen how digital-native companies can achieve massive scale and value, and they’re eager to replicate that success. In short, entrepreneurial energy is part of Latin America’s DNA – a creative, problem-solving ethos nurtured by strong universities, accelerators, and a necessity to innovate around development challenges . A PE firm can harness this energy by positioning AI initiatives not as top-down mandates, but as exciting innovation opportunities where teams can experiment and think like entrepreneurs within their businesses.

Openness to Tech-Driven Models: Latin American consumers and businesses have shown an impressive openness to adopting new technologies and business models. Consider how rapidly the region embraced smartphones, social media, and fintech apps in the past decade. Internet penetration jumped from 43% to 78% across Latin America in the last ten years (even reaching ~90% in Chile) , bringing tens of millions online and creating a vast digital marketplace. This connectivity means customers are ready for digital solutions – whether it’s e-commerce, digital banking, telemedicine, or AI-powered services. In many cases, Latin America leapfrogged legacy systems; for example, millions went from being unbanked straight to using mobile finance apps, bypassing branch-centric banking. This creates a customer base that is receptive to innovation. A portfolio company rolling out an AI-enhanced product (say, an app with built-in AI recommendations, or a chatbot-based service channel) will often find users quite willing to engage with it. In fact, surveys show Latin Americans are cautiously optimistic about AI – a public poll found populations like Brazil’s have high trust in AI (over 80% expressing trust), which is higher than global averages . People here want better services and they’re not as encumbered by the skepticism that sometimes slows adoption in other markets. Moreover, traditional industries in LatAm are full of inefficiencies and pain points (due to past under-investment or market fragmentation). This means AI-driven business models that solve these pain points can scale quickly. We’ve seen it with ride-sharing, last-mile delivery, and online marketplaces – once a tech solution proves itself, the uptake is massive because it often fills a long-standing gap. For PE firms, this openness is an invitation: implementing AI in portfolio companies can not only improve internal operations but can become a market differentiator that attracts customers away from less tech-savvy competitors. Whether it’s an insurer using AI for instant claims processing or a retailer using AI for personalized offers, tech-forward companies can capture the “early adopter” consumers who are plentiful in Latin America.

Hunger for Digital Transformation: At the macro level, there is strong momentum and political will in many Latin American countries to drive digital transformation. Governments are crafting national AI strategies and investing in innovation hubs. Colombia, for instance, has launched its Center for the Fourth Industrial Revolution in Medellín – a public-private initiative with the World Economic Forum – to serve as an AI innovation hub that will develop strategies and solutions with nationwide and regional impact . This underscores a broader regional commitment to technology as a path to progress. Why does this matter for PE? Because it means you often have allies in the public sector and society for modernization efforts. If a portfolio business wants to implement AI in, say, public services, logistics, or healthcare, there are likely government programs or innovation agencies (like Ruta N in Medellín, etc.) willing to support or at least applaud these moves. Additionally, Latin America’s historically stagnant productivity is in dire need of a boost, and AI is increasingly seen as a solution to that puzzle. Productivity in the region has lagged developed nations for decades, contributing to slower economic growth. There is a hunger to break this cycle – among business leaders, policymakers, and younger professionals who want to see their industries become globally competitive. This creates a favorable environment for change. Unlike in some mature markets where employees might resist new tech due to complacency, in LatAm many workers and managers understand that change is necessary for their industries to thrive. We see this in the workforce surveys: 6 in 10 employees in LatAm say they’re excited to use AI, as noted earlier, and young professionals are flocking to digital skills courses. If you introduce AI projects with the promise of upskilling staff and making the company a leader, you are likely to tap into that enthusiasm and idealism.

To crystallize these advantages: Latin America brings high levels of creativity, adaptability, and drive to the table. In a sense, constraints have bred resilience and ingenuity. A culture of solving problems with limited resources (a common theme in emerging markets) actually pairs well with AI – which often thrives on clever, out-of-the-box solutions more than big budgets. Entrepreneurial teams will find ways to implement AI cheaply using open-source tools; digitally hungry consumers will give AI features a chance; and a new generation of employees will readily learn AI skills if given the opportunity. For a PE leader, the task is to channel this latent momentum – provide the vision, training, and support, and then let the natural dynamism of Latin American teams amplify the impact.

Leading the Charge: Change Management and Portfolio-Wide Enablement

Implementing AI is as much a leadership and change management challenge as it is a technical one. To truly reap AI’s benefits across a portfolio, PE firms must cultivate an AI-ready culture in each company and at the group level. This means guiding people through the change, aligning incentives, and sharing knowledge across the portfolio. Here are some practical strategies to ensure that AI initiatives don’t falter due to organizational resistance or siloed efforts:

• Secure Top-Down and Bottom-Up Buy-In: Successful AI transformation requires conviction from the C-suite and grassroots enthusiasm. From day one, make sure the portfolio company CEO and leadership team are not only on board but are vocal champions of the AI agenda. They should frame AI as critical to the company’s future success (because it is), not just as a pet project from the PE owners. At the same time, involve employees at various levels early. Identify influential frontline staff or middle managers who are tech-savvy or simply enthusiastic about innovation – bring them into pilot projects. These folks can become change agents who advocate for AI among peers. Communicate openly about why the company is investing in AI, how it will help the business grow and also make employees’ jobs more interesting (less drudgery, more creative problem-solving). People often fear AI as a job killer; it’s the leadership’s job to emphasize augmentation over replacement. For example, explain that an AI system might automate data entry, but it will free those employees to focus on client relationships or more complex analysis, making their roles more valuable and secure. Consistent messaging – that our goal is to enhance human work with AI, not cut heads – will ease anxieties and build support. When employees see quick-win projects actually helping (say, an automated report generator saving them two hours a day), they’ll become champions too.
• Invest in Training and Upskilling: As noted, the workforce is eager to acquire digital skills; they just need the opportunity. Provide training programs across the portfolio to develop AI literacy and skills. This can range from basic workshops on data-driven decision making for all managers, to more advanced courses for technical staff on using specific AI tools or programming languages. Leverage online learning platforms (Coursera, Udemy, etc.), possibly negotiating enterprise deals to get your portfolio employees enrolled in relevant courses. Another approach is hosting internal hackathons or innovation days where cross-functional teams try to solve a business problem using data and simple AI techniques – this hands-on experience can demystify AI. Some PE firms set up a Center of Excellence (CoE) for AI at the fund level – a small team of experts that can rotate through portfolio companies to train and support projects. Even without a formal CoE, you can facilitate peer learning: if one portfolio company has successfully implemented an AI solution, arrange a seminar or case study where that team shares their experience with other portfolio companies. People learn best from peers, and this also creates a healthy internal network of practitioners. Remember that statistic: currently only 3 in 10 Latin American employees say their employer is investing in AI training – by bucking this trend and prioritizing upskilling, you not only get more capable teams, but you also boost morale and retention. Employees see that they are growing professionally, which builds loyalty. It’s a win-win: they gain career-enhancing skills, and the company gains productivity.
• Foster a Culture of Innovation and Trust: Introducing AI will inevitably bring experimentation and, yes, some failures. Not every pilot will succeed. That’s why cultivating a culture that encourages innovation and tolerates smart failures is crucial. Employees need to feel safe trying new tools or processes without fear of blame if results aren’t perfect the first time. Latin America’s best workplaces already understand this – in a recent survey, 90% of employees at top-ranked innovative companies said their workplace celebrates people who try new things, even if those efforts fail, compared to just 59% at average companies . Strive to make each portfolio company a place where experimentation is celebrated. One practical idea: implement an “innovation sandbox” – a portion of time or budget allocated for teams to pilot new ideas (like an AI model on a subset of data) with minimal bureaucracy. When someone does deliver a successful AI project, recognize and reward that achievement visibly. Conversely, if an experiment doesn’t work out, treat it as a learning opportunity, not a fiasco. This approach builds trust: trust that management supports innovation, and trust among employees that they can speak up with ideas. Also ensure transparency in AI projects – keep communication open about what is being done, why, and what the results are. People are more likely to trust and engage with AI when they understand it. Consider forming a cross-department “AI Council” in the company, a group that meets to discuss ongoing projects, share feedback, and suggest new areas to apply AI. Inclusion in the process breeds ownership.
• Align Incentives and Objectives: To drive adoption, tie AI initiatives to tangible business goals and personal KPIs. For instance, if the sales department is implementing an AI-driven CRM system, make improved lead conversion rate or customer satisfaction part of the sales managers’ targets for the year – showing that using the AI tool is directly linked to hitting their numbers. For senior management, include a digital transformation metric in their performance evaluation (e.g., percentage of processes automated, or revenue from new digitally-enabled products). Many PE firms could also incorporate value creation from digital initiatives into the earn-out or management equity plan of the portfolio company’s leadership. When people have skin in the game, they focus. That said, be careful to measure outcomes, not just tech adoption for its own sake. The ultimate aim is not to implement AI widgets – it’s to improve the business. So define success metrics for each AI project from the start (cost saved, time reduced, NPS increased, etc.), and monitor them. Celebrate and financially reward teams when milestones are achieved. This keeps AI work disciplined and business-focused, and it signals to the whole organization that AI isn’t a fad; it’s producing real results that everyone can be proud of (and benefit from).
• Portfolio-Wide Synergy: Leverage the fact that as a PE firm, you have multiple companies in your portfolio – don’t let each company reinvent the wheel in isolation. Create forums or platforms for portfolio companies to collaborate on AI. This could be periodic portfolio CTO/COO meet-ups to discuss digital strategies, or a simple shared knowledge repository of case studies, successful vendors, code libraries, etc. If one company negotiates a great deal on an AI software license, extend that pricing to others. In some cases, if the companies are in similar industries (or even different ones), they might pool resources to co-develop a solution that both can use – say, a customer analytics engine that can be customized per business. The PE firm can facilitate these synergies by spotting common needs and connecting the dots. Additionally, consider economies of scale: maybe hire a data science team that serves two or three smaller portfolio companies which individually couldn’t afford full-time experts. The operating partner model many top PE firms use is exactly to provide such shared expertise. By treating AI talent and tools as a shared investment across the portfolio, you maximize the ROI and accelerate learning. It’s one of the advantages PE firms have over standalone companies – use it.
• Change the Narrative to Opportunity: Finally, in all change management aspects, frame AI as an opportunity for everyone in the company. From the factory floor to the back-office analysts, people should hear how AI will make their work more effective and the company more competitive, securing its future. Share success stories frequently – e.g., “Look how our new AI-powered demand forecast reduced stockouts by 30%, we’re serving customers better and sales are up” or “Remember Juan from the finance team? After learning our new AI tool, he automated the invoicing process and now has taken on a new role in data analytics – a great career move.” These narratives reinforce that AI is not a threat, but a path to growth and even personal development. As a PE sponsor, when you visit companies or host town halls, underscore the vision: we want to build the most innovative, agile company in this sector, and we’re investing in our people to get us there together. That rallying call can be very inspiring in Latin America, where employees often resonate with visionary leadership that aims to break norms and achieve international-level success.

Conclusion: Rewriting the Playbook for Exponential Growth

Latin America’s private equity leaders have a remarkable opportunity at hand. By infusing AI into the core of their value creation playbook, they can achieve what previous generations of PE deals could only dream of: portfolio companies that grow faster, operate smarter, and leapfrog competitors on the global stage. Yes, there are local challenges – infrastructure, data, talent – but as we’ve outlined, these can be overcome with savvy use of global tools and a bold, strategic approach. In many ways, the constraints themselves, once addressed, become competitive moats. A firm that figures out how to harness AI despite patchy infrastructure or talent gaps will have built capabilities that others will struggle to replicate.

The mandate for PE professionals is clear. It’s time to expand the traditional playbook. Cost cutting and market expansion will always be part of the equation, but AI offers a third dimension of value creation – one that compounds gains and can even redefine business models. The post-acquisition 100-day plan is no longer just about financial restructuring or quick operational fixes; it’s about laying the digital rails for an AI-powered journey. The months and years that follow should see a steady rollout of AI solutions, each building on the last, transforming portfolio companies into lean, innovative, data-savvy organizations. Those are the companies that will thrive in an increasingly digital economy and fetch premium valuations at exit.

Moreover, by embracing AI, PE firms in Colombia and across LatAm position themselves as pioneers in the region’s next economic chapter. They are not just boosting their own returns, but also contributing to a broader uplift in technological sophistication and productivity in the economies where they operate. This is a narrative that investors, limited partners, and other stakeholders will find compelling. Who better to lead this charge than executives who combine sharp financial acumen with a visionary outlook on technology?

In the end, executing this AI playbook comes down to leadership. It requires CEOs and operating partners who can see the possibilities, rally their organizations, and navigate the technical and human complexities along the way. The case for AI-driven value creation is strong and getting stronger by the day. The tools are ready – from cloud platforms to advanced algorithms – and Latin America’s hunger for innovation is palpable. For the PE professional reading this, the message is one of empowerment: you have the chance to redefine what value creation means in this decade. By weaving AI into your strategy, you won’t just outperform the competition – you’ll future-proof your portfolio and leave a legacy of transformed companies.

It’s time to pick up this AI playbook and make it a core part of how you do business in Colombia and Latin America. The winners of tomorrow are being decided today – those who act boldly, invest in AI wisely, and manage change effectively will create the exponential value that sets them apart. In a region brimming with talent and ideas, guided by resolute leadership and the leverage of global technology, the potential for value creation is boundless. Let’s seize it.