The race to capture AI’s value is on – and it’s no longer confined to early adopters. Across industries, companies are using AI to cut costs, speed up decisions, and open new revenue streams. But for mid-market businesses, the real question is where to place their bets: aim for small, fast wins that show ROI in months, or commit to larger-scale initiatives that could transform the company over years?

That’s where the dilemma begins. In a market that’s moving fast and full of uncertainty, both feel urgent. The World Economic Forum puts it simply: short-term gains keep the business moving, while long-term transformation builds resilience and keeps companies competitive for years to come.

The hard part is striking the right balance. In this post, we’ll unpack the trade-offs between tactical AI wins and broader strategic bets. You’ll see how companies in non-tech sectors are approaching the challenge, the ROI they’re getting, the risks they face, and a practical, phased plan for blending speed with scale.

Why does this matter now? Because almost every company is putting money into AI, but very few are getting the full payoff. The gap between AI ambition and AI impact is still wide. According to new research from McKinsey, nearly every company has invested in AI in some form, from pilot projects to full-scale deployments. Yet only about 1 percent of organizations describe themselves as “AI mature,” meaning they have AI fully integrated into their operations and generating substantial, measurable results.

The potential payoff is enormous, but short-term returns can be murky. Leaders are under pressure to deliver quick wins that justify the spend while also steering their companies toward the deeper, long-term changes that AI makes possible.

In the sections ahead, I’ll unpack how to strike that balance with clear, practical, and data-backed insights to help you decide where AI fits into your strategy and how to turn investments into measurable outcomes.

The Tension Between Quick Wins and Long-Term AI Strategy

The Case for Quick Wins

Every executive appreciates a “quick win.” In AI, that means small, targeted projects that can be launched in weeks or months to solve a specific problem and deliver measurable results right away. According to the Distribution Strategy Group, examples include automating repetitive data-entry work like order entries, adding an AI assistant such as a custom GPT to speed up customer service responses, or using predictive analytics to fine‑tune a single production line.

As Medium notes, these efforts typically involve lower‑complexity AI, fit neatly into existing workflows, and produce a clear return on investment, which makes them easier for stakeholders to understand and support Medium. Quick wins provide tangible proof that AI can work in your organization, helping to overcome skepticism and build momentum.

Industry research shows that focusing on small, high‑impact AI applications that “prove worth quickly” often plays a crucial role in securing buy‑in for broader adoption. Many companies report that early projects with fast payback not only create enthusiasm but also unlock funding for more ambitious, enterprise‑wide AI initiatives.

The Case for Long-Term Initiatives

On the other end of the spectrum are enterprise‑wide AI initiatives – the “moonshots” of digital transformation. These are large-scale, strategic programs designed to weave AI into the core of how a business operates. They might involve reengineering critical workflows with AI, launching entirely new AI‑driven products or services, or building a centralized AI platform and data infrastructure to serve the entire organization.

Analysts at Launch Consulting Group, a technology and business transformation firm, note that these programs carry high potential rewards. Done right, they can enable breakthroughs such as fully autonomous supply chains or highly personalized customer experiences at scale. Their balanced perspective on quick wins vs. moonshot investments illustrates how bold, industry‑defining projects can reshape competitive positioning.

Insights from a PwC AI Business Predictions report highlight that a comprehensive AI portfolio should include a mix of “roofshots” (attainable yet significant efforts) and “moonshots” (high‑reward, challenging ventures such as entirely new AI‑driven business models).

Bain & Company reinforces this view, arguing that executives who boldly invest in generative AI moonshots can position their companies for long-term growth and leadership, provided they align the strategy with the right operating model to support such leaps.

That said, the trade-off is clear: these programs are high-risk. They demand significant investment, multi-year timelines, and often require a deep shift in company culture and operations. When successful, they can redefine an industry position—but when they falter, they can stall for years or consume resources without delivering early, visible results.

The Case for Both

It’s easy to see why there’s tension between the two approaches. Lean too heavily on quick wins, and you risk missing the bigger prize. Medium points out that short-term projects often “eliminate weaknesses or inefficiencies but do not create sustainable competitive advantage.” The result can be a patchwork of small, siloed solutions that make incremental improvements but never add up to a lasting edge.

The reverse is just as risky. Advisors at Launch Consulting Group warn that companies betting everything on long-term “moonshot” projects face lengthy timelines, uncertain returns, and the danger of burning through resources before showing measurable results. In some cases, the scope and complexity of these efforts can lead to outright failure.

The message from experienced leaders is that this isn’t an either–or decision. According to Manish Goyal, IBM’s global AI leader, companies need to find the right balance by using short-term AI wins to build momentum while making the longer-term investments that can scale across the business and unlock AI’s full potential.

Finding the right mix of short-term wins and long-term bets can determine whether a company simply keeps pace or pulls ahead of the competition. Quick wins deliver immediate operational gains and measurable ROI, while strategic initiatives lay the groundwork for future growth and potential industry disruption. Launch Consulting Group notes that the most effective AI strategies combine both.

An IBM survey of 2,400 IT decision-makers highlights how divided priorities can be. Twenty-eight percent of respondents said they focus mainly on quick ROI, 31 percent put innovation first, and 41 percent aim for an even balance. Nearly half reported that their AI projects have already achieved a positive ROI. Among those who haven’t, most don’t expect returns within the next year.

The data suggests a split in how companies approach AI: some concentrate on near-term results by deploying off-the-shelf tools, while others commit to large-scale innovations knowing that financial returns may take years. The most successful organizations are finding ways to do both by delivering quick wins that build confidence and momentum, while steadily investing in AI capabilities that can drive far greater value over time.

ROI and Risks: Short-Term Gains vs. Strategic Bets

To understand how to approach AI investment, it helps to look at what the research says about the return on investment and the risks of quick wins compared with longer-term initiatives.

Quick Wins – ROI and Benefits

Quick-win AI projects are designed to deliver a fast, visible payoff. They usually target a narrow problem where efficiency can be gained or costs reduced, and they often succeed. Research cited by CIO notes that certain AI applications can produce rapid returns, measured through outcomes like improved customer satisfaction or higher conversion rates. Because these projects are smaller in scope and require minimal disruption, even modest gains can generate strong ROI. For instance, automating document processing or data entry can free up many hours of manual work almost immediately. Findings from Launch Consulting Group and Medium indicate that value from these efforts can often be measured within months rather than years. Deloitte’s research also supports this, showing that focusing on a few high-impact use cases can accelerate returns, particularly when they are built on proven areas of benefit.

Quick wins also carry relatively low execution risk. As Medium points out, they rarely require a complete overhaul of core systems or processes, which lowers the chance of failure or runaway costs. This makes them an attractive starting point for organizations beginning their AI journey.

Beyond the financial metrics, there is also a psychological return. Distribution Strategy Group highlights that early successes are crucial in the first stages of AI adoption because they serve as proof that AI can deliver real results. Seeing a sales team gain more leads through AI-enhanced data filtering, or a production line avoid downtime thanks to predictive maintenance alerts, creates enthusiasm and momentum. Medium adds that these wins strengthen the position of internal AI advocates. As one technology product officer told CIO, quick wins build trust and pave the way for more innovation in the medium to long term. In many cases, the savings or gains from early projects are reinvested into larger AI initiatives, while the organizational confidence they create makes it easier to take on more ambitious goals.

Quick Wins – Risks and Limitations

Quick wins can be powerful, but they can also create a false sense of progress if they are not connected to a larger strategy. Medium warns of the “hygiene factor” trap: many quick wins focus on fixing inefficiencies such as reducing costs or removing repetitive tasks. While valuable, these changes may not improve competitive advantage. Saving a small percentage in operating costs with AI is helpful, but competitors can replicate it. No company wins lasting market share simply by automating invoice processing first.

Fragmentation is another risk. Medium notes that if departments launch quick fixes on their own, organizations can end up with a patchwork of AI tools that do not integrate. Different teams may choose different vendors or platforms, causing data silos and future compatibility problems. A CIO article confirms this is common, with many businesses operating reactively rather than following a coordinated AI plan. Quick wins that are operationally focused and isolated can lack strategic direction.

There is also the problem of momentum stalling once the obvious opportunities are gone. Without a roadmap, companies can hit an “innovation backlog,” as Medium describes it, where they are unsure of the next move and lack the infrastructure for larger-scale AI. CIO warns that focusing only on immediate ROI risks neglecting the deeper challenges that long-term innovation can address.

Finally, rushing for a quick payoff can backfire. Deploying an AI solution before it is accurate or ready may lead to quality issues, loss of trust, and customer dissatisfaction. A chatbot that frustrates users is one example. Even small projects require proper due diligence and change management to ensure they are both reliable and capable of supporting future AI growth.

Long-Term AI Initiatives – ROI and Benefits

Large-scale AI programs, whether it is an AI-powered supply chain overhaul, an enterprise-wide data platform feeding machine learning models to every department, or a bold new AI-driven service offering, are built for transformative returns. Instead of incremental improvements, the goal is step-change value: higher growth, entirely new revenue streams, and competitive advantages that are difficult to replicate.

These “moonshot” projects often enable things that simply were not possible before. A new business model, such as usage-based insurance priced by AI analysis of driver behavior, or a fully automated “lights-out” factory run by AI-controlled robotics, are examples. The ROI can be extraordinary. UPS’s decade-long investment of $250 million in its AI-powered routing system, ORION, now saves over $300 million annually in fuel and operational costs by eliminating 100 million unnecessary miles each year. That efficiency is not only a return on investment but also a competitive moat.

AI at scale can also unlock substantial revenue gains. McKinsey estimates that advanced AI, including generative AI, could add trillions of dollars in value across industries through productivity gains and faster innovation. Many retailers have seen revenue increase by 10 to 15 percent from AI-driven personalization, along with higher customer loyalty. The real power lies in the compounding value once AI is woven into everyday operations. Because it is analyzing data continuously, supporting decisions, and autonomously executing tasks, it delivers levels of efficiency and agility that manual or disconnected approaches cannot match.

There is also a defensive benefit. Long-term AI investments future-proof the organization. As one executive told CIO, the real advantage of AI is in driving sustained innovation and efficiency improvements over time, not just immediate returns. These programs push companies to build essential capabilities such as robust data infrastructure, AI talent pipelines, and governance frameworks, which position them to adapt quickly to new technologies, market shifts, or competitive threats.

Deloitte’s 2024 survey of more than 2,700 executives found that nearly 74 percent of organizations saw their advanced AI initiatives meet or exceed ROI expectations, even if returns took longer to arrive. Those companies pushed through early challenges, built strong foundations over about a year, and then unlocked significant gains in both efficiency and cost savings. As McKinsey warns, the bigger risk for leaders is not aiming too high, but thinking too small. Without strategic AI bets today, tomorrow’s market leaders may be decided without you.

So quick wins deliver fast ROI with relatively low risk but have limited reach. And large AI initiatives can transform a business and create long-term advantages, yet they require more time, money, and carry greater risk. Each approach has strengths and weaknesses, which is why balance matters. As CIO notes, the smartest AI strategies resemble an investment portfolio, where smaller projects generate quick returns that help fund the larger bets requiring years to mature.

Next, we will look at examples of how different enterprises have put this balance into action.

Real-World Examples: AI in Action Beyond Tech Companies

To bring this discussion into the real world, here are a few short case studies from manufacturing, retail, and logistics. Each shows how companies have achieved quick AI wins while also building toward larger, organization-wide impact.

Ford’s Dual Approach to AI in Manufacturing: 

Ford shows how a manufacturer can get quick AI wins while still keeping an eye on the long game. On the quick-win side, it rolled out two AI-powered quality checks, AiTriz and MAIVS, on its assembly lines. These systems spot tiny misalignments and confirm that parts are installed correctly, all in real time. The idea is to catch problems before cars leave the factory and avoid expensive recalls. AiTriz is already running in 35 stations, and MAIVS is in nearly 700 across North America, delivering clear gains in accuracy and efficiency.

At the same time, Ford is thinking bigger. While it has not shared a full “moonshot” AI plan, rolling out these tools in stages looks like part of a broader strategy. Start with targeted improvements that work right away, build confidence and skills, then expand into a more connected, AI-driven production system.

This mix of short-term results and long-term planning reduces risk now while setting Ford up for larger-scale innovation later. In effect, it is treating AI like a portfolio, where early wins help fund and pave the way for the bigger bets.

Old Navy’s Phased AI Rollout

Old Navy is a good example of a company getting fast results from AI while setting up for bigger things down the road. For quick wins, it’s rolling out RADAR, a system that combines RFID, AI, and computer vision, in 1,200 U.S. stores. With RADAR, store teams can find products in seconds, restock shelves faster, and fulfill online orders more accurately. The payoff is fewer out-of-stock items and a smoother shopping experience.

But that’s just the starting point. This rollout is also building the backbone for a much more connected retail operation. With real-time inventory data feeding into AI analytics, Old Navy can move toward things like dynamic inventory allocation, personalized recommendations, and truly seamless shopping across stores and online.

It’s the same idea as a balanced investment portfolio. The quick operational gains from RADAR create momentum and confidence, while the long-term plan keeps Old Navy moving toward bigger, transformational AI goals.

DHL’s Two-Speed AI Strategy

DHL is showing how a logistics giant can score quick AI wins while building toward a much bigger transformation. On the fast-results side, it’s using Stretch robots from Boston Dynamics to unload trailers at almost twice the speed of human workers. Right now, there are seven of these robots in action, but DHL plans to grow that to 1,000. The result is less manual strain for employees and a big boost in throughput.

At the same time, DHL is putting serious effort into long-term AI infrastructure. Its forecasting platform, now running in more than 220 countries, predicts delivery times with 95 percent accuracy and has cut delivery times by a quarter. The company is also rolling out “smart trucks” that use machine learning to reroute on the fly, saving around 10 million delivery miles each year.

By combining fast payoffs from robotics with deeper AI capabilities like dynamic routing and inventory orchestration, DHL is improving operations today while setting itself up for major breakthroughs tomorrow. Quick wins keep the momentum going, and the foundational work ensures those gains scale across the whole network.

A Playbook for Balancing AI Wins and Scale

So how do you actually pull off the balance between quick, tactical wins and big, strategic plays? The framework below pulls together what’s worked for other companies, based on industry research and real-world examples. It’s not a rigid checklist, but it gives you a logical way to get value fast while keeping an eye on the bigger picture.

1. Start with a vision that thinks big but stays grounded

Begin by deciding what “AI success” means for your business over the long term. This should be about business impact, not just a list of cool technologies. Pin down a few strategic goals where AI could really make a difference, whether that’s improving customer experience, streamlining operations, or opening up entirely new revenue streams.

For example, if your big-picture goal is an AI-enabled supply chain, your vision might include predictive analytics for planning, automation in warehouses, and smarter routing for deliveries. Knowing that vision helps you choose quick wins that actually build toward it, instead of random side projects.

It’s also critical to get executive sponsorship right away. McKinsey’s research shows leadership and governance are among the top enablers of AI at scale. In other words, the C-suite needs to believe in the vision and be willing to invest over time.

Think big, but roll it out in manageable steps.

2. Go after the low-hanging fruit first

Once you’ve set the big-picture vision, zero in on one to three high-impact, low-complexity projects you can knock out quickly. The perfect quick win is something you can deliver in a few months, uses data you already have, and solves a pain point everyone understands.

Think small projects with big visibility like automating a repetitive process to save hours of manual work, using predictive analytics to fix a recurring operational issue, or adding an AI feature to an existing product or service. Whatever you pick, define clear success metrics up front, like cutting processing time in half or increasing sales conversions by five percent.

Keep the scope tight. It’s far better to over-deliver on a small project than over-promise on a big one. Put together a cross-functional team that pairs technical know-how with business expertise so you get the best of both worlds.

And remember, this stage isn’t just about the result—it’s about learning how AI works in your organization. Pay attention to data challenges, user adoption, and skill gaps. According to Distribution Strategy Group, well-executed quick wins deliver measurable improvements and become internal proof points for what’s possible. Celebrate them, share the results widely, and start building a culture that’s excited about AI.

3. Build the foundations for scaling up

Those first quick wins will almost always shine a light on gaps in your data, your tech, and your people. This is the moment to start fixing them so AI can grow beyond pilot projects.

If your early projects hit roadblocks because of messy or inaccessible data, put effort into better data governance and integration. That could mean creating a central data hub, cleaning up existing datasets, or making it easier for teams to share information. If your success depended on a handful of superstar data scientists working off the cuff, think about building a formal AI Center of Excellence or training more people with AI skills.

On the tech side, develop an enterprise AI architecture. Choose a cloud platform, set up reliable pipelines for moving data and deploying models (MLOps), and put in place solid security and privacy standards. This is the “plumbing and guardrails” that will make your future AI projects faster to launch and easier to manage.

Governance matters too. Decide how projects will be prioritized, how you’ll address ethical risks, and how you’ll measure AI’s performance and ROI over time. Many companies create an AI steering committee at this point to pull siloed efforts into one coordinated strategy.

It’s not the most glamorous work, but Deloitte’s AI leadership points out that infrastructure and cultural readiness are what make AI value last. And you don’t have to stop progress while doing it—this can run alongside other phases. The key is that once you have a couple of wins under your belt, you start shoring up the base so the next wave of AI can hit with more impact and less friction.

4. Scale up what works

Once you’ve proven an AI project delivers value, it’s time to roll it out more broadly. Look at your successful pilots and ask where else they can make an impact. If a predictive maintenance trial worked on one production line, consider expanding it to every factory or across your entire fleet of critical equipment. At this stage, you’re productizing the solution by making it more reliable, integrating it into existing systems, and training teams so they can use it effectively.

Scaling is not just about turning on the same tool in more places. You need a strong feedback loop to track ROI and performance in different contexts, then make adjustments as needed. Integration is key. The AI solution should fit smoothly into people’s workflows, not disrupt them. This often means updating standard operating procedures, re-skilling employees, or tweaking incentives so adoption is encouraged.

By scaling proven solutions, you’re turning small experiments into enterprise-wide capabilities. This prevents “islands of experimentation” and unlocks the full value of your early wins. Keep tracking enterprise-level KPIs like total cost savings, revenue growth, or improved customer satisfaction. The bigger and more visible the results, the easier it is to secure ongoing support from leadership and the board for future AI investments.

5. Innovate and transform

Once you’ve got a portfolio of AI projects delivering value and a team that’s comfortable working with the technology, you can start aiming for the bigger bets. Pick one or two “moonshots” that tie directly back to your long-term vision, like a retailer building a personalization engine for every customer touchpoint, or a manufacturer investing in a fully automated, AI-driven plant.

These projects should have clear strategic benefits, but expect them to take time before showing ROI. Break them into milestones or smaller pilots so you can track progress along the way. Keep the playbook from earlier phases in mind: strong executive sponsorship, cross-functional teams, and a focus on business outcomes.

It’s smart to keep a few quick wins going in parallel so you’re delivering short-term results while the bigger initiatives mature. Over time, those moonshots can produce game-changing outcomes and even spark the next round of quick wins.

Bringing It All Together

Quick wins and long-term initiatives are not competing priorities—they work best together. Quick wins prove that AI can deliver real value in your business, creating the confidence and financial returns to power larger investments. Enterprise-wide initiatives, driven by a clear strategic vision, make sure those smaller successes add up to something bigger: a lasting competitive advantage and a future-ready organization. As one CIO advisor put it, think of AI like an investment portfolio, with some projects delivering faster ROI and others aimed squarely at long-term growth.

With AI advancing at a rapid pace, this balanced approach is also the safest path forward. It avoids getting stuck waiting for the perfect plan and prevents the chaos of random, uncoordinated experiments. You get the momentum of quick wins that keep the business moving, while the long-term initiatives position you for sustained growth. Follow this playbook, and AI stops being a buzzword and instead becomes a core strength that drives ROI today and builds your competitive edge for tomorrow.