AI implementation
14 min read
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Oct 20, 2025
A strategic guide for C-suite executives on leveraging AI for sustainable business growth, competitive advantage, and market leadership in the next decade.
Imogen Jones
Content Writer
Here’s a prediction worth making: in the next five to seven years, many companies that haven’t built artificial intelligence into the fabric of how they grow will quietly disappear. Not overnight, and not because their products fall short, but because they’ll be moving in slow motion while everyone else accelerates.
A convergence of factors has pushed AI from academic labs to mainstream business in just the last few years. According to PwC, 73% of US companies have adopted AI in some capacity. The impact of effective adoption is becoming clear; according to a recent report, companies with mature, AI-led processes are already achieving 2.5 times higher revenue growth and 2.4 times greater productivity than their peers.
This piece is a guide for leaders who see that shift coming and want to build around it rather than chase it. It looks at where AI is already creating momentum across industries, what an effective adoption roadmap actually looks like in practice, and how to measure its impact once it’s embedded in the business.
More than anything, it’s an argument for urgency. The window for treating AI as an experiment is closing fast.
In this article:
How AI fundamentally changes business performance
Growth applications of AI across key business functions
Industry-specific opportunities and AI strategies for different company sizes
A practical implementation strategy for measurable and sustainable growth
The Growth Equation: How AI Changes Everything
Artificial intelligence is a fundamental growth accelerator that amplifies every aspect of business performance. Understanding this is the first step for any business leader aiming to secure a competitive advantage in the coming decade.
With that being said, it can feel like we're approaching the peak of the hype cycle. It's not that there aren't compelling use cases already in play, or opportunities on the near horizon, but it can be difficult to see the wood for the trees.
Below are three reasons to get genuinely excited.
Traditional technology investments tend to follow a linear path, where each upgrade buys a little more efficiency or scale. AI behaves differently.
Modern AI, especially machine learning and deep learning, discovers its own patterns and “rules” by training on vast amounts of data. This means AI isn’t limited to tasks we know how to explicitly describe. If you feed a machine learning system enough examples (say, of legal contracts or successful sales emails), it can learn to interpret or even generate similar content without a human coding those rules line by line.
Its learning feedback loops create exponential returns. The result is a compounding advantage: better models lead to better insights, which in turn generate better data.
A McKinsey report highlights that organizations are already reporting both cost decreases and revenue jumps in business units that deploy generative AI. The compounding nature of AI means that early adopters are building competitive advantages that become increasingly difficult for others to overcome.
For years, AI was talked about mainly in terms of efficiency, like automating manual tasks, reducing overhead, and cutting costs. That view isn't wrong, but it misses the larger story. The most compelling economic value of AI lies in its ability to generate new forms of revenue by uncovering patterns, preferences, and opportunities that were invisible before.
Take law as an example. According to LexisNexis' The Future of Legal GenAI report, 70% of respondents believe AI will "enable a variety of new value-added work for clients." Nearly half are already exploring new business lines or billable opportunities underpinned by AI.
In real estate, McKinsey reports seeing real estate companies gain over 10% or more in "net operating income through more efficient operating models, stronger customer experience, tenant retention, new revenue streams, and smarter asset selection."
The ability to act and react faster than competitors often determines who wins the market. AI’s primary competitive advantage might be summed up in one word: velocity. It enables rapid decision-making, agile responses to change, and accelerated execution across all business functions.
This extends to operational execution, where AI workflow automation can accelerate everything from product development cycles to customer service resolutions.
Critically, AI speeds up the cycle of innovation itself. An AI can run through thousands of design iterations or A/B tests in the time it once took to do one. This means product development and go-to-market cycles shrink.
Case in point, Moderna used AI and automation to help design and test its mRNA COVID-19 vaccine in just 63 days from genome sequencing to human trials, a process that traditionally takes many months
Industry-Specific Growth Opportunities
You know your business best, and that’s where the real value of AI begins. Much of its impact comes from understanding your own workflows and identifying where intelligent automation can make the biggest difference.
That said, here are just a few examples of how V7 Go customers are already using AI to unlock growth, streamline operations, and create new opportunities across their industries.
AI for Financial Services
The growth opportunities unlocked by AI in finance are remarkably broad, spanning everything from portfolio optimization to fraud detection, risk modeling, and client engagement.
To zoom into just one example, venture capital has always been about spotting trends or parallels between today’s opportunities and yesterday’s breakout successes. The challenge is that these patterns often hide in unstructured data, like pitch decks, market research, founder bios, investor memos, and even social media presence.
Traditionally, analysts could only process a fraction of this information, meaning potentially valuable insights were left unexplored. AI is changing that.
Take the V7 Go Venture Capital Deal Screening Agent as an example. This agent automatically analyzes pitch decks, business plans, and investment materials to pinpoint the most promising opportunities, flagging strong market positioning or emerging trends. It helps investors process far more deals in less time, reducing manual analysis and accelerating deal flow.
To explore further use cases, check out 10 Key Use Cases of Generative AI in Finance.
AI for Real Estate
In real estate, AI is reshaping how firms analyze property data, predict market trends, and accelerate investment decisions.
One example is AI document automation. From lease agreements and title deeds to zoning reports and compliance certificates, these documents are critical, but traditionally slow and labor-intensive to review.
With platforms like V7 Go, this process becomes faster, more accurate, and highly scalable. AI automatically extracts essential details such as lease terms, rent escalation clauses, and ownership structures, while flagging inconsistencies or missing information that could signal risk.
See below through a walk-through example, or check out our case study with relos to learn how they're using the platform to automate property transactions.
Another powerful application lies in AI-powered property valuation and market analysis, a field opening up entirely new opportunities for growth and investment strategy. We explore this in more detail in our blog, AI in Property Appraisal: Use Cases, Risks, and ROI.
Appraisers and investors constantly monitor market trends to determine whether prices are rising, stabilizing, or declining. Traditionally, these judgments rely heavily on expert intuition and lagging indicators such as quarterly reports or sales data. AI changes this equation by introducing real-time, data-driven forecasting into the valuation process.
Instead of waiting for official indices, AI models can analyze thousands of faster-moving signals, from satellite imagery and construction permits to consumer spending data, rental listings, and social sentiment. This shift from reactive analysis to proactive insight transforms property valuation from a backward-looking task into a forward-looking growth engine.
AI for Legal Services
The legal industry is rapidly embracing artificial intelligence as a strategic advantage capable of driving growth. According to Thomson Reuters, 95% of lawyers believe AI will be central to their workflows within the next five years.
Few sectors demand the same level of accuracy, auditability, and ethical responsibility as law. That’s why it's critical to use secure platforms that prioritize traceability and transparency. For example, V7 Go provides AI Citations for every output. These built-in references ensure lawyers can verify exactly where each piece of information comes from, maintaining compliance and client trust.
Legal contact automation is one of the most exciting areas for AI adoption. Manual review of contracts is time-intensive and prone to human oversight, especially when dealing with thousands of agreements across multiple jurisdictions.
V7 Go rewrites this process. Legal AI agents can interpret complex contractual language with precision, flag non-standard clauses, surface inconsistencies, and even compare new agreements against prior versions.
By eliminating repetitive review tasks, firms can handle higher volumes of cases, shorten turnaround times, and refocus talent on higher-value strategic work. In high-volume environments, this can mean hundreds of hours saved, faster deal cycles, and new capacity for growth.
Another high-impact use case is AI eDiscovery. The exponential growth of digital communications has made eDiscovery one of the most data-heavy and time-consuming tasks for modern law firms.
AI excels here. Advanced document intelligence can rapidly cluster related materials, detect privileged content, and summarize key findings. It also reduces review fatigue and lowers the cost of discovery, often one of the largest expenses in litigation.
It’s no surprise that 77% of law firms report already using AI to some extent for document review. You can learn more about this use case in our blog, eDiscovery for Law Firms: A Complete Process Guide.
Small vs. Large Company AI Growth Strategies
The approach to AI-driven growth can differ markedly based on a company’s size and resources. Large enterprises have the advantage of scale, data, and capital, but they also face complexity and inertia. Small and mid-sized businesses may lack big budgets or data troves, but they often can move more quickly and adopt off-the-shelf AI solutions to punch above their weight.
Let’s explore how AI growth strategies differ for big vs. small players, and why the democratization of AI is leveling the playing field in many respects.
Enterprise AI Growth Strategies
For large organizations, AI is a lever to reinforce their market leadership and create competitive moats that are hard to breach. Key considerations and strategies for enterprises include:
Leveraging Scale and Data Advantages: Enterprises sit on massive datasets (customer behavior, transactions, supply chain info, etc.). A strategic large company will invest in turning this raw data into AI models that improve with scale. For instance, an enterprise retailer can train recommendation engines on billions of interactions to optimize sales across its huge customer base, an advantage a smaller competitor can’t replicate without the data volume.
Build Structure Around AI: A hallmark of enterprise AI success is organizational structure. Many set up centralized Centers of Excellence or federated innovation hubs. This avoids reinventing the wheel in each department and accelerates company-wide adoption. It also helps with governance and ethical standards, important for risk management at scale.
Scaling Training and Change Management: Enterprises also focus on upskilling their massive workforce to be AI-enabled. A big company can invest in extensive training programs to turn thousands of analysts into “citizen data scientists” who know how to use AI tools. This is crucial for realizing growth. Even the best AI strategy fails if employees don’t use the tools.
SMB AI Growth Opportunities
Smaller and medium-sized businesses (SMBs) can use AI to compete with larger players by being more agile and targeted. The democratization of AI through platforms like V7 Go makes enterprise-grade capabilities accessible to smaller organizations without the need for large in-house data science teams.
Agility and Niche Focus: SMBs have the advantage of agility, and they can implement new tech faster without bureaucracy. While a bank might take a year to evaluate and roll out a new AI software, a 50-person fintech startup might integrate it in a week. This speed means SMBs can be early adopters of the latest AI innovations, sometimes leapfrogging larger rivals in specific areas. Plus, many SMBs operate in niche markets where tailored AI solutions can have outsized impact.
Democratization of AI Talent: While SMBs can’t hire massive AI teams, the increased availability of AI education and community means hiring one or two savvy data scientists or even upskilling existing staff can go a long way. As AI tools become more user-friendly, SMBs benefit proportionally more.
Implementation Strategy for Sustainable Growth
While nearly four out of five leaders agree their company needs to adopt AI to stay competitive, about 60% also worry they lack a clear plan to do so, and how to measure the ROI.
To win with AI—and to be clear, you really won’t be able to win without it—you have to move forward now. As with every monumental transformation, you must have a solid strategy, you have to gain buy-in from your team, and you have to equip them to bring your strategy to life.
Amy Bernstein, Editor in Chief, Harvard Business Review
Having a bold vision for AI is important, but execution is where many organizations stumble. To ensure AI actually drives business growth (and not just efficiency or, worse, become a money sink), leaders need a concrete strategy that ties AI initiatives to growth outcomes. This means identifying high-impact opportunities, sequencing projects for quick wins and long-term value, and aligning the whole effort with business goals.
In this section, we provide actionable guidance for developing an AI strategy focused on tangible growth results.
Identifying High-Impact AI Opportunities
Not every AI use case is created equal. Companies should prioritize AI opportunities based on potential impact on revenue, competitive advantage, scalability, and strategic differentiation. Start by asking: Where could AI move the needle most for our business?
There few criteria to consider: Will applying AI in this area directly or indirectly generate more revenue? For example, AI that improves lead conversion or upsell (sales growth), or AI that enables a new product line (new revenue stream), scores high.
Scalability is another consideration. Favor AI initiatives that, if successful, can be scaled across the organization or to new markets.
It’s also important to balance projects that yield quick wins with those that set the foundation for long-term transformation. Quick wins (e.g., automating a simple back-office task with clear ROI) are valuable to build momentum, show success, and fund further investment. However, to truly leverage AI for growth, you also need to pursue longer-term strategic initiatives that might take time but can redefine your business.
The Implementation Roadmap for Growth
Once high-impact opportunities are identified, the next challenge is implementation. Many companies get stuck in “pilot purgatory” with lots of experiments, no scaled impact. To avoid that, you need a practical roadmap that starts with high-value, low-risk pilots and then scales up.
For instance, an organization can start their AI growth journey with V7 Go by first automating document-heavy workflows. This initial step can deliver immediate efficiency gains and lay the data foundation for more advanced AI capabilities. Once pilots are successful, the next phase is to develop a scaling framework to deploy AI capabilities across the enterprise.
After a pilot, do a retrospective. What worked, what didn’t, what adjustments are needed? Often pilots will surface data issues, process tweaks, or user experience improvements needed.
Measuring AI-Driven Growth: KPIs and Success Metrics
To ensure AI initiatives are delivering on their promise, it’s essential to measure their impact with clear metrics. Defining the right key performance indicators and success criteria does two things: it keeps AI projects accountable to business results, and it provides evidence to support further investment in AI.
Growth Metrics: These include direct revenue growth, market share expansion, customer acquisition cost (CAC), and improvements in customer lifetime value (LTV).
Operational & Efficiency Metrics: Track reductions in process cycle times, error rate decreases, and productivity gains per employee. For example, AI can reduce the time needed for certain tasks by up to 80%.
Competitive Metrics: Measure improvements in time-to-market for new products, speed of response to market changes, and quality enhancements compared to competitors.
ROI Calculation: A comprehensive ROI model should account for both direct cost savings and indirect benefits like increased innovation capacity and enhanced customer loyalty. Organizations should define these metrics before implementation to establish a clear baseline for success.
Drive AI Adoption
One of the leading reasons organizations stall after early pilots is because employees don’t fully understand or embrace the systems being introduced. Build a clear business case that ties AI projects directly to strategic goals, and communicate that vision consistently from leadership down. When employees see how AI supports their work rather than threatens it, resistance turns into engagement.
Establish feedback loops, celebrate quick wins, and make transparency a core principle.
How Could AI Help Your Organization Grow?
Just as digital transformation became essential over the past 20 years, AI transformation is the mandate for the next 10. Embracing AI today is essential for remaining competitive tomorrow. And beyond competition, it’s about fundamentally doing more for your customers and stakeholders than you could before.
By reading this, you’re already signaling your intent to be among those success stories. The next step is action: What’s your AI roadmap? Where will you deploy it first? How will you empower your team to leverage it? What partnerships or investments do you need?
If you're ready to take the next step, begin your AI transformation with V7 Go.
