Executives' guide to investing in artificial intelligence: Understanding the value proposition in 2025
L'AI automatizzerà 300M posti lavoro equivalenti globalmente, 92M eliminati entro 2030 (WEF), 60% lavori paesi alto reddito influenzati—ma saldo netto positivo: 170M nuovi ruoli emergeranno (+78M totale). Lavori più suscettibili: amministrativi 46% attività automatizzabili, back-office, call center, contabilità. Risultati settoriali già misurabili: finanza -40% costi operativi +40% efficienza gestione rischio, sanità -30-50% tempi diagnosi con scoperta farmaci da 5 anni a <1 anno (-60% costi), software -56% tempi sviluppo con +30-60% accelerazione time-to-market, manifattura -80% downtime con +8% profitti annui, marketing +30% conversioni con -30% costi acquisizione clienti. Polarizzazione salariale estrema: avvocati con competenze AI guadagnano +49% vs colleghi tradizionali. Italia caso demografico: gap 5.6M posti lavoro entro 2033, automazione 3.8M diventa necessità vs rischio. Competenze 2025: pensiero analitico, creatività, intelligenza sociale—94% responsabili marketing riporta impatto positivo vendite, 91% aziende con AI assumerà nel 2025. Questione centrale: non se AI sostituirà umani ma quali umani si adatteranno vs resisteranno cambiamento.
As AI investment trends evolve further into 2025, executives face increasing pressure to make strategic decisions aboutAI implementations. With the rapid adoption of AI tools by companies-22 percent are implementing them extensively and 33 percent are using them in a limited way-understanding how to evaluate and implement AI solutions has become critical to maintaining competitive advantage. In the book"The Executive Guide to Artificial Intelligence" by Andrew Burgess, the author provided a comprehensive guide for business executives who wish to understand and implement AI solutions in their organizations.
This book was published in 2017 by Springer International Publishing and provides a practical overview of how companies can leverage artificial intelligence. What has changed today?
Current investment trends in AI 2025
The AI landscape is experiencing unprecedented growth, with organizations making more significant investments to remain competitive.
The basics:
Burgess emphasized the importance of starting by defining clear goals aligned with business strategy, a principle that remains valid today. In the book, he identified eight core AI capabilities:
Image recognition
Voice Recognition
Search and information extraction
Clustering
Natural Language Understanding
Optimization
Prediction
Understanding (today)
Evolution from 2018 to 2025:
Since the book was written, AI has gone from an emerging technology to a mainstream technology. The "Understanding" capability that Burgess considered futuristic has seen significant advances with the advent of Large Language Models (LLM) and generative AI technologies, which had not yet emerged in 2018.
Strategic framework for investment decisions in AI
The four essential questions
When evaluating investments in AI, it is critical to focus on these critical questions:
Business problem definition
Metrics of success
Requirements for implementation
Risk assessment
Note: This four-question framework comes from current knowledge and is not explicitly presented in Burgess' book.
Building an effective AI strategy
The adoption framework:
Burgess proposes a detailed framework for creating an AI strategy that includes:
Alignment with business strategy - Understanding how AI can support existing business objectives
Understanding of AI ambitions - Define if desired:
Improve existing processes
Transforming business functions
Create new services/products
IA maturity assessment - Determine the organization's current level of maturity on a scale of 0 to 5:
Tactical implementation of automation tools (Level 3)
Tactical implementation of various automation technologies (Level 4)
End-to-end strategic automation (Level 5)
Creating an IA heat map - Identifying areas of greatest opportunity
Business case development - Assessing "hard" and "soft" benefits
Change management - Planning how the organization will adapt
Developing an IA roadmap - Creating a medium- to long-term plan.
Evolution from 2018 to 2025:
Burgess' framework remains surprisingly relevant today, but needs to be supplemented with considerations of:
AI ethics and regulations (such as the EU AI Act)
Environmental sustainability of AI
Responsible AI strategies
Integration with emerging technologies such as quantum computing
Measuring ROI in AI investments
The determinants of return on investment:
Burgess identifies different types of AI benefits, categorized as "hard" and "soft."
Hard benefits:
Cost reduction
Avoiding costs
Customer satisfaction
Compliance
Risk mitigation
Loss mitigation
Mitigation of revenue loss
Revenue generation
Soft benefits:
Cultural change
Competitive advantage
Halo effect
Enabling other benefits
Enabling digital transformation
The measurement of AI ROI has become more sophisticated, with specific frameworks for assessing the impact of generative AI, which did not exist when Burgess wrote the book.
Technical approaches to AI implementation
Types of solutions:
Burgess presented three main approaches to implementing AI:
Off-the-shelf AI software - Pre-packaged solutions
AI platforms - Provided by large technology companies
Growth of generative AI and models such as GPT, DALL-E, etc.
Rise of AutoML solutions that automate parts of the data science process
Risk considerations and challenges
The risks of artificial intelligence:
Burgess devoted an entire chapter to the risks of AI, highlighting:
Data quality
Lack of transparency - The "black box" nature of algorithms
Unintentional bias
Naiveté of AI - Limits of contextual understanding
Overdependence on AI
Choosing the wrong technology
Malicious acts
Evolution from 2018 to 2025:
Since the book was written:
Concerns about algorithm bias have become a critical issue (pending)
AI security has become critical as threats increase
AI regulation has emerged as a key factor
The risks of deepfakes and generative AI disinformation have become significant
Privacy concerns have increased with the more pervasive use of AI
Creating an effective IA organization
From the book by Burgess (2018):
Burgess proposed:
Building an AI ecosystem with suppliers and partners
Establish a Center of Excellence (CoE) with dedicated teams
Consider roles such as chief data officer (CDO) or chief automation officer (CAO).
Evolution from 2018 to 2025:
Since then:
The role of chief AI officer (CAIO) has become commonplace
AI is now often integrated throughout the organization instead of being isolated in a CoE
The democratization of AI has led to more distributed operating models
The importance of AI literacy for all employees emerged
Conclusion
From the book by Burgess (2018):
Burgess concluded with the importance of:
Don't believe the hype but focus on real business problems
Start the IA pathway as soon as possible
Future-proof the company through understanding AI
Adopt a balanced approach between optimism and realism
Evolution from 2018 to 2025:
Burgess' call to "don't believe the hype" remains incredibly relevant in 2025, especially with the excessive hype surrounding generative AI. However, the speed of AI adoption has become even more critical, and companies that have not yet begun their AI journey now find themselves at a significant disadvantage compared to those that followed Burgess' advice to start early (in 2018!).
The AI landscape in 2025 is more complex, more mature, and more integrated into business strategy than could have been predicted in 2018, but the core principles of strategic alignment, value creation, and risk management that Burgess outlined remain surprisingly valid.
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