Stop Saying "We Need AI." Say This Instead for Results

By Jordan Hauge — Published August 15, 2025 — Category: AI Strategy, Digital Transformation, Automation, Product Management

I hear it over and over again: "We need an AI strategy." Here's the problem with this statement - saying "We need AI," is akin to saying "we need transportation," when you're trying to decide between a delivery truck, a company car, or a helicopter. Each serves completely different purposes, has different costs, and solves different problems.

I have been hearing it everywhere over the past few months:"We need an AI strategy." "How do we get more AI into our operations?" "What's our AI roadmap?"While it's easy to understand the intent, its lacking critical information. Simply stating, "we need AI," is akin to saying, "we need transportation," when you're trying to decide between a delivery truck, a company car, or a helicopter.Each serves completely different purposes, has different costs, and solves different problems.The good news?Once you know what types of AI actually exist and what they're good at, those strategic conversations become much more productive. You'll move from vague aspirations to concrete implementation plans.I've learned that the most successful leaders don't ask for "AI." They ask for specific solutions to specific problems.Here's how to make that shift.The Real Challenge: AI Means Everything and NothingWhen your team says "we need AI," they might mean:Automating repetitive tasksGetting better insights from dataImproving customer serviceStreamlining workflowsMaking better predictionsThe problem is that different types of AI solve these problems in completely different ways, with different implementation timelines, costs, and success rates.Let me walk you through the six types of AI you'll actually encounter in business, what they're genuinely good at, and how to talk about them productively (and how to communicate exactly what you need without inaccurate interpretation).The Six Types of AI That Actually Matter for Business1. AI Tools: Your Productivity MultipliersWhat to say instead of "we need AI": "We need to reduce time spent on [specific task] by X%"What they are: Point-and-click applications that use AI to solve specific problems. Think ChatGPT, Midjourney, or Grammarly.Best conversations to have:"Our content team spends 60% of their time on first drafts. What if we could cut that to 20%?""Our sales team takes notes in every meeting. Can we automate that and focus on the conversation?""We're spending too much on stock photos. What are our options?"Business examples:Marketing teams using Jasper for initial ad copy draftsSales teams using Otter.ai for automatic meeting transcriptionHR using AI screening tools to shortlist resumesWhat this actually costs: Usually $10-100 per user per monthImplementation timeline: Days to weeksThe beauty of AI tools is immediate results.Companies are increasingly looking for proven results from generative AI, rather than early-stage prototypes, and AI tools deliver measurable value quickly.2. AI Workflows: Your Process OptimizersWhat to say instead: "We need to eliminate handoffs between [System A] and [System B]"What they are: Multiple AI tools working together automatically. When one task finishes, it triggers the next one without human intervention.Best conversations to have:"Our lead qualification process involves five manual steps across three systems. How do we streamline this?""We're re-entering the same data multiple times. What's possible?""Invoice approvals are taking two weeks because of routing delays. Can we fix this?"Real business example: At Botpress, they use a bot called Gordon to handle demo scheduling.It monitors Hubspot and shares prospects' info with other actions directly as a enterprise chatbot that saves our sales team hours every week.Typical workflows that work well:Lead qualification → CRM entry → Personalized follow-upInvoice receipt → Data extraction → Approval routingCustomer support ticket → Classification → AssignmentWhat this actually costs: $30-300 per month for most business setupsImplementation timeline: Weeks to monthsGartner predicts that by 2025, 70% of newly developed applications by enterprises will utilize low-code or no-code technologies, a significant increase from less than 25% in 2020.This means your team can often build these without extensive IT involvement.3. AI Agents: Your Decision-Making PartnersWhat to say instead: "We need something that can handle judgment calls for [specific situation]"What they are: AI that can take actions and make decisions based on changing circumstances. Unlike workflows, agents adapt their behavior.Best conversations to have:"Our customer service team answers the same 20 questions differently every time. How do we standardize while maintaining personalization?""Lead qualification requires too much human judgment to automate with simple rules. What are our options?""We need something that can escalate complex issues but handle routine ones independently."Real business examples: Microsoft's Case Management Agent for Dynamics 365 Customer Service automatically creates a case, fills in relevant fields, and updates them as the conversation progresses.The agent tracks cases requiring attention, sends follow-ups, and even resolves cases autonomously.The key difference: Workflows follow "if this, then that" rules. Agents think: "Given this situation and these goals, what's the best action?"What this actually costs: $100-1,000+ per month depending on complexityImplementation timeline: MonthsAccording to Gartner, by 2028, at least 33% of enterprise software will depend on agentic AI, but overcoming the 85% failure rate requires these new paradigms.Success depends on starting with well-defined, specific use cases.4. Machine Learning Models: Your Pattern PredictorsWhat to say instead: "We have data about [X] and need to predict [Y] to improve [business outcome]"What they are: AI trained specifically on your business data to find patterns and make predictions.Best conversations to have:"We have two years of sales data. Can we predict which prospects are most likely to buy?""Our inventory costs are killing us. Can we predict demand more accurately?""Customer churn is hurting revenue. Can we identify at-risk customers before they leave?"Real business examples:Retail inventory forecasting: A regression model that will be able to predict when and how many products to buy, considering the expiration date of different productsDynamic pricing: Uber's surge pricing, where prices increase when demand goes up, is a prominent example of how companies use ML algorithms to adjust prices as circumstances changeWhen this makes sense: You need substantial data (usually 1,000+ examples) and a clear prediction target that drives business value.What this actually costs: $50,000-500,000+ for custom modelsImplementation timeline: 6-18 monthsAccording to a recent report by Grand View Research, the global machine-learning market size is expected to reach $6 billion by 2027, growing at a CAGR of 43.8% from 2020 to 2025.The investment is significant, but so are the potential returns for the right use cases.5. AGI (Artificial General Intelligence): The Future VisionWhat to say instead: "That doesn't exist yet, but here's what we can do today..."The reality: Despite marketing claims, AGI that can learn and perform any intellectual task like humans simply doesn't exist yet.Why this matters: Traditional machine learning is now an established technology in many organizations, and today leading firms are focusing on use cases for generative AI, not waiting for AGI.The productive conversation: Focus on specific problems you can solve today rather than waiting for sci-fi solutions.6. Understanding Narrow AI: What You're Actually Working WithWhat to say instead: "This AI is designed specifically for [task]. It won't automatically be good at [other task]."The key insight: Machine Learning is the foundation on which Generative AI has been built. Algorithms like Transformers, LSTM, Decision Trees, and Support Vector Machines. These ML models still power most real-world AI applications.Why this matters for planning: Each AI system needs to be designed and trained for its specific job. Your customer service AI won't suddenly become good at financial forecasting.Real Success StoriesMicrosoft's Strategic Approach: Aker BP implemented Microsoft 365 Copilot and Copilot Studio to create AI agents that streamline daily tasks, enhance tool accessibility, and establish a foundation for scalable automation.Healthcare Efficiency: At Acropolium, they have their own machine learning use case examples for healthcare businesses.When cooperating with a proteomics company, their dedicated team integrated ML technology into biomaterial analysis processing as part of big data processing app development.As a result, their partner reported a 40% increase in data processing accuracy.Customer Service Evolution: In 2025, 80% of customer service and support organizations will use generative AI to improve agent productivity and overall customer experience, according to Gartner.But the winners combine multiple AI types strategically.Common Implementation Challenges (And How to Avoid Them)Challenge 1: "Pilot purgatory" Companies often struggle to move generative AI projects, whether internal productivity tools or customer-facing applications, from pilot to production.Solution: Start with AI tools that deliver immediate value, then build complexity gradually.Challenge 2: Expecting magic Agents tend to be very ineffective because humans are very bad communicators. We still can't get chat agents to interpret what you want correctly all the time.Solution: Set realistic expectations and start with well-defined, specific tasks.Challenge 3: Skipping the foundation Many companies jump to complex agents before mastering basic AI tools.Solution: Follow the progression: tools → workflows → agents → ML models.What to Say in Your Next AI Strategy MeetingInstead of: "We need an AI strategy" Try: "Let's identify our three biggest productivity bottlenecks and explore AI solutions for each"Instead of: "How do we implement AI?" Try: "Which repetitive tasks could we eliminate, and what would that be worth?"Instead of: "We're falling behind on AI" Try: "What specific business outcomes are we trying to improve, and how might AI help?"Instead of: "What's the latest in AI?" Try: "What problems are we trying to solve, and what tools are available?"The Bottom LineThe most successful AI implementations don't start with the technology - they start with specific business problems and work backward to the right solution.When you shift from asking for "AI" to asking for solutions to specific challenges, everything becomes clearer:Your timeline becomes realisticYour budget becomes predictableYour success metrics become measurableYour team becomes alignedThe AI revolution isn't about deploying the fanciest technology. It's about using the right type of AI to solve specific problems, implemented thoughtfully, with clear business value.What specific problem are you ready to solve?