AI Strategy: A Practical Framework Using Jobs-to-be-Done (JTBD)

Artificial intelligence. It's the buzzword dominating headlines, promising revolution, and maybe causing a little anxiety. We see the hype, the futuristic possibilities, but back in the day-to-day reality of running a business, the question becomes practical: How do we actually use this strategically? How do we move beyond chasing shiny objects and integrate AI in a way that delivers real, measurable impact?

If you're feeling overwhelmed or unsure where to start, you're not alone. Many businesses dive into AI reactively, buying tools without a clear purpose, leading to wasted resources and frustrating results. This post offers a different approach – a structured framework to help you develop a practical, effective AI integration strategy grounded in understanding the real needs of your business.

The Core Job: Why Are You Really Looking to AI?

Before evaluating any AI tool, let's step back and apply a concept from innovation theory called Jobs-to-be-Done (JTBD). Instead of focusing on features, JTBD asks: what 'job' is your business, your team, or your customer trying to get done? What progress are they trying to make, and where are they struggling?

Businesses don't adopt AI for its own sake. They 'hire' it to achieve specific outcomes like:

• "Increase operational efficiency to reduce costs."

• "Enhance customer experience to improve loyalty."

• "Unlock new revenue streams by identifying market gaps."

• "Improve employee satisfaction by reducing tedious work."

Framing the need in terms of the 'job' shifts the focus from technology to the underlying problem or opportunity.

Understanding AI's Role Through the Jobs-to-be-Done Lens

Thinking with a JTBD mindset helps clarify where AI can provide the most value. It forces us to look past the surface and understand the functional and emotional dimensions of the jobs within our business.

• Functional Jobs: These are the core tasks that need accomplishing (e.g., "Analyze monthly sales data," "Schedule team meetings," "Process customer invoices").

• Emotional Jobs: These relate to how people feel while doing the job (e.g., "Reduce frustration from manual data entry," "Feel confident in forecasting accuracy," "Avoid burnout from repetitive tasks").

AI can potentially address both. But the real strategic power often lies in elevating the level of abstraction. Today, accomplishing a complex job like "Nurture a sales lead to closure" might involve multiple tools (CRM, email platform, calendar, analysis software) and significant manual effort. Future AI solutions might consolidate these steps, transforming the job into something simpler and more integrated, like "Orchestrate successful lead nurturing." This doesn't just automate; it fundamentally changes the work, potentially requiring different skills and even changing who performs the job.

Beginner Note: When we talk about AI in business, we often mean different types like:

• Automation AI: Handles repetitive tasks (like data entry).

• Analytics AI: Finds patterns and insights in data.

• Generative AI: Creates new content (text, images).

• Understanding the basic types helps match the right AI to the right 'job'.

SMB/Executive Note: The key is identifying high-impact jobs where current solutions are inefficient, costly, or cause significant struggle. Where are the biggest points of friction in your value chain? That's often where AI can offer a strategic advantage.

Identifying High-Impact AI Opportunities

Once you start thinking in terms of 'jobs', you can systematically identify where AI can make the biggest difference.

1. Map Key Business Processes: Outline your core value streams – how you deliver value to customers, manage operations, etc.

2. Pinpoint Struggles: Within these processes, where do things break down? Look for bottlenecks, inefficiencies, errors, high costs, employee frustration, or unmet customer needs. Think like a JTBD interviewer: what are the workarounds people use? What causes delays? What outcomes are consistently missed?

3. Prioritize Opportunities: You can't tackle everything at once. Evaluate potential AI applications based on criteria like:

   • Potential Impact: How significantly could this improve efficiency, revenue, customer satisfaction, or employee experience?

   • Feasibility: Is the necessary technology mature and accessible? Do we have the data required?

   • Alignment: Does this support our overall business strategy and goals?

   • Cost/Resources: What is the expected investment (time, money, people)?

   • Desired Outcomes: Can we define clear metrics AI should improve? (e.g., "Reduce average customer support resolution time by 30%," "Increase the accuracy of financial forecasts by 15%").

Executive Focus: Ensure these opportunities align with your long-term competitive positioning. Is this AI application building a defensible advantage or just keeping pace?

Building Your AI Integration Strategy

With prioritized opportunities, you can build a coherent strategy.

• Vision & Goals: What does success look like for AI integration in your business? Set clear, measurable, achievable, relevant, and time-bound (SMART) objectives linked to the jobs and outcomes identified earlier.

• Phased Approach: Avoid the 'big bang'. Start with pilot projects targeting one or two high-priority jobs. Learn, adapt, and then scale what works.

• Technology & Tools: Now is the time to evaluate specific AI solutions. Consider:

  • Build vs. Buy: Develop custom AI or use off-the-shelf tools?

  • Platforms vs. Point Solutions: Integrated suites or best-of-breed tools?

  • Crucially: Choose technology that best serves the identified 'job', not the other way around.

• Data Strategy: AI is data-hungry. Ensure you have a plan for:

  • Data Quality: Is your data accurate, complete, and relevant?

  • Data Governance: Who owns the data? How is it secured and managed ethically?

  • Data Accessibility: Can the AI systems access the data they need?

• Talent & Skills: AI requires human oversight and expertise. Plan for:

  • Upskilling/Reskilling: Training your current team.

  • Hiring: Bringing in new talent with AI skills.

  • Partnering: Working with external consultants or vendors.

• Ethical Considerations: Proactively address potential issues:

  • Bias: Ensuring AI models don't perpetuate unfair biases.

  • Privacy: Protecting customer and employee data.

  • Transparency: Understanding how AI decisions are made (explainability).

  • Responsibility: Defining accountability for AI outcomes.

Implementation & Change Management

A great strategy is useless without effective execution.

• Roadmap: Develop a detailed implementation plan with clear timelines, milestones, responsibilities, and budget allocation.

• Integration: Plan how new AI tools will fit into existing systems and workflows. Avoid creating new silos.

• Change Management: This is critical and often underestimated.

  • Communicate the 'Why': Explain the benefits (addressing the 'job' and struggles) to gain buy-in.

  • Training: Provide adequate training and support for users.

  • Address Concerns: Be transparent about potential impacts and listen to feedback.

  • Foster Adoption: Encourage and reward the use of new AI-powered processes. Treat the implementation itself as a 'job' your employees are doing – understand their potential struggles with the change.

SMB Focus: Look for lean implementation approaches. Can you leverage existing platforms with AI add-ons? Start with tools requiring minimal technical overhead.

Measuring Success & Iteration

How will you know if your AI strategy is working?

• Key Performance Indicators (KPIs): Track metrics directly tied to your initial goals and the desired outcomes for the 'jobs' AI is addressing. Examples:

  • Cost savings from automation.

  • Efficiency gains (e.g., time saved per task).

  • Improved customer satisfaction (CSAT) or Net Promoter Score (NPS).

  • Increased sales conversion rates.

  • Reduced error rates.

• Feedback Loops: Regularly collect feedback from the people using or interacting with the AI systems (employees, customers). How well is the AI actually performing the job? Where is it still falling short?

• Continuous Improvement: Your AI strategy shouldn't be static. The technology evolves, and your business needs change. Build in regular reviews to evaluate performance, identify new opportunities, and refine your approach.

From Reactive Tactics to Strategic Integration

Integrating AI effectively isn't about having the latest tech; it's about deeply understanding the 'jobs' your business needs to do better and strategically applying AI to address the struggles involved. By adopting a Jobs-to-be-Done mindset and following a structured framework for identification, prioritization, strategy building, implementation, and measurement, you can move beyond the hype and harness AI for tangible, sustainable business value.

Stop the random acts of AI. Start integrating with purpose.

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I’ve been trained by the best in Outcome-Driven Innovation. Part of that training involved how to understand what the future should look like. As a result, I’ve taken what I’ve learned and begun innovating so I can get you to the outcomes you’re seeking faster, better, and even more predictably. Anyone preaching innovation should be doing the same; regardless of how disruptive it’ll be.

How am I doing this?

• I’ve developed a complete toolset that accelerates qualitative research to mere hours instead of the weeks or months it used to take. It’s been fine-tuned over the past 2+ years and it’s second-to-none (including to humans). That means we can have far more certainty that we’ve properly framed your research before you invest in a basket of road apples. They don’t taste good, even with whipped cream on top.

• I’m also working on a completely new concept for prioritizing market dynamics that predict customer needs (and success) without requiring time-consuming and costly surveys with low quality participants. This is far more powerful and cost effective than the point-in-time surveys that I know you don’t want to do!

I believe that an innovation consultant should eat their own dog food. Therefore, we must always strive to:

• Get more of the job done for our clients

• Get the job done better for our clients

• Get the job done faster for our clients

• Get the job done with with fewer features for our clients

• Get the job done in a completely different and novel way for our clients

• Get the job done in a less costly manner for our clients

You could be an early tester of the latest developments, but at a minimum take advantage of an approach that is light years ahead of incumbent firms that are still pitching a 30 year old growth strategy process but haven’t grown themselves. 👈🏻It's worth thinking about.

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Mike Boysen - www.pjtbd.com
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