Categorie
Lavori da fare JTBD

Lavori da fare (JTBD) + agilità dell'intelligenza artificiale

jobs-to-be-done jtbd ai agility
lavori da fare jtbd ai agilità

Introduzione

Oggi le aziende raccolgono più dati sui clienti che mai, eppure la maggior parte delle innovazioni fallisce. Secondo McKinsey (2023), 94% di dirigenti si dichiarano insoddisfatti delle prestazioni della loro azienda in termini di innovazione e la Harvard Business Review (2019) rileva che 85% dei nuovi prodotti di consumo falliscono entro due anni.

Il motivo principale? Le aziende si concentrano troppo su chi sono i loro clienti piuttosto che sul perché acquistano. Il marketing tradizionale enfatizza demografia, psicografia e analisi dei clienti basate su sondaggima non riescono a cogliere le motivazioni più profonde che stanno alla base del comportamento dei consumatori.

Il Quadro di riferimento per i lavori da eseguire (JTBD), pioniere di Clayton Christensen, offre un causale comprensione del comportamento dei clienti, aiutando le aziende a creare prodotti, servizi e strategie di marketing migliori concentrandosi sulle vere ragioni per cui le persone prendono decisioni di acquisto.

In questo articolo esploreremo:
✅ Le origini della JTBD e come è emerso dallo studio delle innovazioni fallite.
✅ Come i clienti "assumono" e "licenziano" i prodotti in base alle loro esigenze.
✅ I principi chiave della JTBD e il loro impatto sulla strategia aziendale.
✅ Casi di studio del mondo reale che mostra le innovazioni di successo basate sulla JTBD.
✅ Come le aziende possono implementare la JTBD per ottenere un vantaggio competitivo.

Le origini del lavoro da fare (JTBD)

Perché l'innovazione tradizionale fallisce

Per decenni le aziende si sono affidate a Personaggi dei clienti, focus group e sondaggi per guidare lo sviluppo del prodotto e il marketing. Tuttavia, nonostante questi sforzi, molte aziende non riescono ad anticipare le reali esigenze dei consumatori.

💡 Esempi chiave di innovazione fallita:

  • Segway (2001) - Commercializzato come mezzo di trasporto futuristico, non è riuscito a identificare un "lavoro" pratico da risolvere.
  • Nuova Coca Cola (1985) - Si presumeva che il gusto fosse il fattore chiave per l'acquisto di bevande analcoliche, ignorando i fattori emotivi e di fedeltà alla marca.
  • Google Glass (2014) - Si concentra sui progressi tecnologici piuttosto che sulla soluzione di un problema reale del cliente.

Clayton Christensen e l'innovazione dirompente

Il Struttura JTBD nasce dal lavoro di Clayton Christensen, professore della Harvard Business School e autore di Il dilemma dell'innovatore (1997). La teoria di Christensen teoria dell'innovazione dirompente explains how market leaders often fail by focusing on incremental improvements rather than solving real customer problems.

Christensen and his research team discovered that customers don’t buy products for their features—they “hire” them to fulfil specific jobs. This realization led to the Jobs-to-Be-Done approach, a methodology that focuses on why customers switch products rather than who they are.

How Customers “Hire” and “Fire” Products

The Core Principle of JTBD

🔹 Customers don’t buy products; they hire them to make progress in a given circumstance.
🔹 If the product does the job well, they “hire” it again. If not, they “fire” it and look for an alternative.

💡 Example: McDonald’s Milkshake Case Study
Clayton Christensen’s team conducted a famous JTBD study with McDonald’s to understand why people bought milkshakes.

📌 Traditional Approach:
McDonald’s initially focused on customer demographics and flavor preferences. They conducted focus groups to tweak their milkshakes’ taste and consistency, yet sales remained flat.

📌 JTBD Approach:
Researchers discovered that most milkshake sales happened in the early morning. Customers weren’t just buying them as a drink—they were hiring milkshakes as a convenient, mess-free, long-lasting breakfast for long commutes.

📌 Outcome:
McDonald’s redesigned milkshakes to be thicker and more filling, making them last longer in the morning commute—sales increased significantly without changing flavours or branding.

Il risultato principale: Customers don’t buy products based on features alone. They choose products that help them achieve a specific goal in their daily lives.

The Three Dimensions of Customer Jobs

To fully understand why customers hire products, businesses must consider three types of jobs-to-be-done:

1️⃣ Functional Jobs – The practical reason behind a purchase.
✅ Esempio: A customer buys a waterproof jacket to stay dry in the rain.

2️⃣ Emotional Jobs – The feeling associated with the product.
✅ Esempio: Someone buys a premium raincoat to feel confident and stylish.

3️⃣ Social Jobs – How the purchase affects social perception.
✅ Esempio: A customer chooses an eco-friendly raincoat to appear environmentally conscious.

💡 Example: Tesla’s JTBD Strategy
Tesla doesn’t just sell electric cars; it sells a vision of technological innovation and sustainability.
✔️ Functional Job: A high-performance, fuel-efficient car.
✔️ Emotional Job: A feeling of being a pioneer in sustainability.
✔️ Social Job: Status and prestige from driving an advanced vehicle.

Applying JTBD to Business Strategy

Traditional Marketing vs. JTBD

Traditional MarketingJobs-to-Be-Done Approach
Focuses on customer demographicsFocuses on customer intent and needs
Uses focus groups & surveysUses deep interviews & observational research
Compares product featuresIdentifies customer pain points
Competes with direct market rivalsConsiders all competing solutions to the same job

💡 Example: Netflix vs. Blockbuster

Blockbuster (Traditional Approach)Netflix (JTBD Approach)
Focused on DVD rentals and late feesFocused on removing rental inconvenience
Assumed customers wanted varietyUnderstood customers wanted instant access
Competed with video rental storesCompeted with cable, DVDs, and even video games
Ignored the job of convenienceMade entertainment on-demand & frictionless

Result: Blockbuster filed for bankruptcy in 2010, while Netflix became a $250B company by focusing on the customer’s job-to-be-done.

Intersection of JTBD, AI, and Business Agility

Businesses today are experiencing rapid shifts due to digital transformation, artificial intelligence (AI), and evolving consumer expectations. Yet, despite these advancements, many companies still struggle with innovation and customer engagement.

Il Quadro di riferimento per i lavori da eseguire (JTBD), originally pioneered by Clayton Christensen, provides a causale understanding of why customers make purchasing decisions. It helps businesses design AI-driven solutions and agile business models that align with real customer needs rather than relying on outdated market segmentation techniques.

With the rise of AI-driven decision-making e business agility, companies must integrate JTBD thinking into their strategies to remain competitive. In this article, we’ll explore:

✅ How AI enhances JTBD analysis for better customer insights
✅ How JTBD principles align with Business Agility and adaptive business models
✅ Real-world case studies where AI-driven JTBD strategies have led to success
✅ How businesses can leverage AI-powered JTBD insights for competitive advantage

Why Do Most AI-Driven Innovations Fail?

Despite AI’s potential, many AI-driven business initiatives fail because they lack a deep understanding of customer needs.

🔹 McKinsey (2023) reports that 94% of executives are dissatisfied with their company’s innovation performance.
🔹 Harvard Business Review (2019) states that 85% of AI-driven products fail due to misalignment with actual customer needs.
🔹 AI models are often trained on correlation-based data, rather than causal customer behavior insights.

Where AI Falls Short Without JTBD Thinking

1️⃣ AI Predictive Analytics Overemphasize Correlation:

  • AI can identify patterns (e.g., “People who buy luxury cars also buy premium coffee”).
  • However, correlation does not explain why customers buy (e.g., “Customers buy luxury cars for social status, but premium coffee for sensory experience and convenience”).

2️⃣ AI Chatbots and Virtual Assistants Lack Contextual Awareness:

  • Many AI chatbots fail to provide meaningful customer support because they don’t recognize the true “job” the customer needs done.
  • Instead of repeating scripted responses, AI systems must be trained to recognize customer struggles and emotional needs.

3️⃣ AI-Powered Marketing Misses Emotional and Social Jobs:

  • AI-driven ad targeting focuses on demographic similarities, ma fails to capture customers’ deeper motivations.
  • Esempio: Recommending a fitness app based on age and gender ignores the emotional and social reasons behind fitness motivation (e.g., health concerns, self-esteem, community belonging).

📌 Solution: AI must be paired with JTBD analysis to move from correlation-based prediction to causation-driven insights.

AI-Driven JTBD: The Future of Customer-Centric Business Strategy

How AI Enhances JTBD Insights

✅ AI-Powered Behavioural Analytics → Helps businesses analyse customer struggles and uncover hidden Jobs-to-Be-Done.
✅ Elaborazione del linguaggio naturale (NLP) → Extracts deep emotional and social motivations behind customer purchases.
✅ Machine Learning for Customer Segmentation → Moves beyond demographics to segment customers based on jobs and pain points.
✅ Conversational AI & Sentiment Analysis → Helps companies understand why customers “fire” products and what causes dissatisfaction.

Real-World Example: AI-Powered JTBD in Action

📌 Netflix’s AI-Powered Personalization (JTBD Success)

  • Traditional recommendation systems categorized viewers by demographics.
  • Netflix shifted to a JTBD-based model, recognizing that:
    • Some customers “hire” Netflix to relax after work.
    • Others “hire” Netflix to bond with family o learn something new.
  • AI-driven personalization now tailors recommendations based on viewing behaviours and inferred customer jobs.

📌 Spotify’s AI and JTBD Strategy

  • Spotify’s AI doesn’t just recommend music—it recommends based on customer “jobs.”
  • Recognizing that music is often hired to manage emotions, Spotify introduced mood-based playlists and AI-curated daily mixes.

AI-Powered JTBD in B2B Contexts

📌 Salesforce’s AI-Driven Customer Relationship Management (CRM)

  • AI-powered Salesforce Einstein analyses customer interactions to determine:
    • Why certain customers are at risk of churn.
    • What “job” the customer is trying to accomplish.
  • Instead of relying on static customer profiles, Salesforce uses real-time AI insights to adjust strategies dynamically.

💡 Key Insight: AI alone cannot replace human intuition and strategy—but when combined with JTBD thinking, it becomes a powerful tool for predicting and fulfilling customer needs.

JTBD + AI Business Agility: Perfect Match in Digital Age

Why Business Agility Needs JTBD Thinking

Agile businesses thrive by adapting to customer needs and iterating quickly. JTBD helps agile teams by:
✔️ Clarifying customer priorities → Teams focus on what truly matters to customers.
✔️ Avoiding feature creep → Prevents businesses from adding unnecessary AI features that don’t solve real jobs.
✔️ Supporting rapid prototyping → Businesses test whether a product actually fulfils a job before scaling.

Case Study: How Agile Businesses Use JTBD

📌 Amazon’s AI-Powered JTBD Approach

  • Amazon doesn’t just sell products—it optimizes for different customer jobs.
    • Prime members “hire” Amazon for ultra-fast, convenient delivery.
    • Kindle users “hire” Amazon for access to instant digital reading.
  • Amazon’s AI identifies changing customer jobs and adapts product offerings dynamically.

📌 Tesla’s AI and JTBD Strategy

  • Tesla’s autonomous driving AI isn’t just about self-driving—it’s about solving the job of reducing driver fatigue and increasing convenience.
  • Instead of competing with traditional car brands, Tesla focuses on software-based agility, continuously updating features based on evolving customer jobs.

How Businesses Can Implement AI-Powered JTBD for Competitive Advantage

Step 1: Identify Customer Jobs with AI-Powered Behavioural Data

📌 Use AI-driven customer journey mapping to analyse how people interact with products and services.

Step 2: Align AI and Business Agility with JTBD Insights

📌 Design agile business models that adapt to customer job changes dynamically.

Step 3: Integrate AI-Driven Personalization Based on Customer Jobs

📌 Use AI-powered recommendation engines to match products/services to real customer jobs.

Step 4: Leverage Conversational AI & Sentiment Analysis for Customer Feedback

📌 Monitor AI chatbots and support interactions to detect customer struggles and pivot business strategy accordingly.

Future of JTBD, AI, and Business Agility

✅ AI is a powerful tool, but it must be guided by Jobs-to-Be-Done insights.
✅ Business agility is essential for adapting to evolving customer needs.
✅ JTBD thinking transforms AI-driven business models from feature-driven to truly customer-centric.

Citations & References

  • CB Insights. (2023). The Top Reasons Startups Fail.
  • Christensen, C. M., Hall, T., Dillon, K., & Duncan, D. S. (2016). Competing Against Luck: The Story of Innovation and Customer Choice. Harper Business.
  • McKinsey & Company. (2023). The State of Innovation in Global Business.
  • Harvard Business Review. (2019). Why Most New Products Fail: Lessons from 40,000 Launches.
  • Netflix AI Personalization Case Study, MIT Technology Review (2022).
  • Tesla AI Strategy Report, Forbes (2023).
  • The Innovator’s Dilemma. Christensen, C. (1997). Harvard Business School Press.

JTBD PDF Explanation

Understanding-Jobs-to-Be-DoneScaricare

Foto di Evangeline Shaw

it_ITItalian
it_ITItalian en_CAEnglish fr_CAFrench arArabic zh_HKChinese cs_CZCzech hi_INHindi ko_KRKorean pt_BRPortuguese es_MXSpanish