The Future of Value Creation in Business Strategy with AI Augmentation

Artificial Intelligence (AI) is rapidly transforming the business landscape. According to a recent report by McKinsey, AI could potentially deliver an additional $13 trillion to the global economy by 2030, increasing global GDP by about 1.2% annually. This growth is not just about automation but increasingly about augmentation – the use of AI to enhance human intelligence and capabilities rather than replace them. Understanding this distinction is crucial for strategic planning and monetization.

The Current State of AI in Business

Automation vs. Augmentation

Most firms are now focusing on AI for automation, which means utilising AI to execute repetitive activities more effectively. This is mostly owing to the quick cost savings and relative simplicity of introducing automation vs augmentation. However, AI augmentation, which improves human decision-making and creativity, has the potential to increase long-term value generation.

When comparing AI automation with augmentation, numerous significant distinctions arise. Automation is best suited for well-defined, repetitive activities, whereas augmentation excels at managing complicated, dynamic circumstances that need human intervention. Automation can result in immediate cost savings and increased efficiency, but it may also cause skill atrophy as people become less involved in mundane work. In contrast, augmentation improves and broadens human capabilities, resulting in a more engaged and talented workforce.

The impact on job roles is another important consideration. While automation often raises concerns about job displacement, augmentation is more likely to transform job roles by enabling humans to focus on higher-value activities. For example, AI-powered tools can handle routine data analysis, allowing data scientists to concentrate on interpreting results and developing new models.

In terms of innovation potential, augmentation has a greater ability to drive new product and service development. Organisations may experiment with new business models and provide more personalised consumer experiences by combining human innovation with AI skills. Netflix, for example, utilises AI to supplement its recommendation system, offering customers personalised content choices that improve their viewing experience and increase consumer loyalty.

Long-term Value Creation

The long-term value generating potential of AI augmentation is enormous. Organisations can increase efficiency, innovation, and customer happiness by developing people talents. AI augmentation allows firms to provide personalised goods and services, enhance decision-making processes, and cultivate a more flexible and adaptive workforce. 

Companies who engage in AI augmentation now stand to gain a considerable competitive edge in the future years.

Amazon, for example, employs artificial intelligence to improve its logistics operations by optimising delivery routes and inventory management to provide timely and efficient service. This not only increases operational efficiency, but also improves the client experience. Similarly, in the automobile business, Tesla employs AI to supplement its manufacturing processes, combining human experience with AI-powered automation to make high-quality electric vehicles.

The Role of AI Strategists and C-level Leaders

Effective AI implementation requires leadership with hands-on AI experience. According to Gartner, 62% of CEOs have chosen growth as their top business priority, driven by AI’s potential to transform business operations. However, only 34% of CEOs believe their organizations are prepared to leverage AI effectively. This gap underscores the importance of having AI strategists and C-level leaders who understand both the technical and business aspects of AI.

The Emergence of AI Augmentation

Definition and Scope

AI augmentation refers to the use of AI to enhance human intelligence and capabilities. Unlike automation, which replaces human tasks, augmentation supports and amplifies human efforts. This approach is gaining traction across various industries:

Software Development: GitHub Copilot, an AI-powered tool developed by GitHub in collaboration with OpenAI, functions as a real-time coding assistant. By offering context-aware code suggestions, it significantly boosts developer productivity. Described as a “pair programmer,” GitHub Copilot provides real-time suggestions, speeds up repetitive tasks, and enhances code maintainability.

GitHub Copilot allows developers to focus on higher-level design and problem-solving by taking over routine coding tasks. This not only increases productivity but also fosters innovation by enabling developers to concentrate on the more creative aspects of their work. One of GitHub Copilot’s standout features is its ability to understand the context of the code being written. Developers can set the context and let GitHub Copilot manage routine coding tasks. For instance, by specifying a bubble sort algorithm, GitHub Copilot instantly generates the necessary code, saving valuable time.

Marketing: Salesforce Einstein improves efficiency and personalisation throughout the Customer 360 by leveraging predictive and generative AI. It enables the seamless design and implementation of assistive AI experiences within Salesforce, allowing customers and workers to connect directly with Einstein for faster problem resolution and better workflows. It provides AI capabilities to sales teams, agents, marketers, and other professionals based on customer data, making each customer engagement more meaningful and productive. Furthermore, Salesforce’s Einstein AI analyses customer data to discover emerging patterns and consumer behaviours, allowing marketers to create better focused ads.

Sales: LinkedIn Sales Navigator is a robust sales prospecting tool that leverages artificial intelligence (AI) and machine learning to help sales professionals find and connect with potential customers. By utilizing natural language processing (NLP) to analyze text data, predictive analytics to identify promising leads, and machine learning to provide personalized recommendations, Sales Navigator enhances engagement and drives business growth. Key features include lead scoring, relationship mapping, and account targeting, enabling sales professionals to prioritize efforts, build relationships, and leverage their network effectively. Additionally, Sales Navigator offers insights and analytics to optimize sales strategies and monitor competitors, ensuring a competitive edge in B2B sales.

Practical Examples

Stitch Fix: This online personal styling business employs AI to supplement its stylists’ talents, resulting in more personalised suggestions and higher client satisfaction. Stitch Fix has seamlessly integrated data science into its operations from its inception, using AI and machine learning to improve personalisation in styling, logistics, inventory management, and product creation. The company’s recent deployment of generative AI has improved its operations by automating jobs and freeing up human specialists to focus on more creative and judgment-based activities. Stitch Fix employs huge language models, such as OpenAI’s GPT-4, to evaluate substantial client feedback, resulting in faster and more accurate suggestions. GPT-3 helps to generate interesting advertising text and thorough product descriptions, considerably lowering the time necessary to do these jobs. The Outfit Creation Model (OCM) presents personalised outfit combinations, giving clients design ideas based on their tastes and previous purchases. Stitch Fix continues to improve client experiences and deliver higher personalisation at scale by combining AI efficiency with human knowledge. Stitch Fix CEO Matt Baer believes personalisation will make it stand out in retail.

Netflix: Netflix, an American international entertainment corporation started on August 29, 1997 in Scotts Valley, California, specialises in streaming and DVD distribution. By 2017, it has premiered original shows such as “Stranger Things” and “Narcos.” Netflix, the world’s largest television network with 130 million customers, credits its success to artificial intelligence. Netflix uses machine learning algorithms to analyse watching data and propose content based on individual interests, improving user experience and engagement. This recommendation algorithm has saved Netflix $1 billion per year by lowering customer churn and boosting ongoing content discovery.

High-Value Complex Workflows

AI augmentation excels in managing complex operations that need human judgement and creativity, as seen in healthcare. For example, AI improves medical imaging analysis by scrutinising X-rays, MRIs, and CT scans with high accuracy, finding tiny abnormalities and flagging possible concerns for radiologist assessment. This not only improves diagnostic accuracy but also speeds up throughput, allowing clinicians to care for more patients more effectively. In treatment planning, AI uses patient medical histories, genetic data, and symptoms to provide personalised treatment options, such as optimising chemotherapy regimens matched to specific disease profiles in oncology. Furthermore, AI speeds drug discovery by analysing large molecular databases to predict the efficacy of compounds against specific illnesses, hence expediting pharmaceutical research and development procedures.

Impact on Business Outcomes

AI augmentation enhances efficiency and effectiveness by automating routine tasks, facilitating enhanced decision-making through rapid data analysis, and enabling predictive maintenance in industries like manufacturing. According to PwC, AI is projected to contribute $15.7 trillion to the global economy by 2030, with $6.6 trillion from increased productivity and $9.1 trillion from consumption-side effects. On the productivity front, AI streamlines operations by handling repetitive tasks, thereby allowing human workers to focus on more complex and creative endeavors. It also empowers managers with data-driven insights for informed decision-making and preemptive equipment maintenance. AI’s consumption-side impacts include personalized product offerings that boost customer satisfaction and loyalty, leveraging AI-driven market analysis for new product development, and improving customer service through AI-powered virtual assistants and chatbots that ensure round-the-clock support and enhance customer experiences.

Challenges And Oppurtunities

While AI augmentation offers significant potential for enhancing business operations and decision-making, it also presents several challenges that organizations must address. Let’s explore these challenges in more detail, along with examples of companies facing and addressing them:

Data Privacy and Security

As AI systems process vast amounts of data, ensuring data privacy and security becomes paramount. Organizations must implement robust data protection measures to comply with regulations like GDPR and CCPA.

Example: In 2018, Facebook faced a major data privacy scandal when it was revealed that Cambridge Analytica had harvested the personal data of millions of users without their consent for political advertising purposes. This incident highlighted the importance of data privacy and security in AI-driven systems and led to increased scrutiny of data handling practices across the tech industry.

To address these concerns, companies like Microsoft have implemented comprehensive data protection measures. Microsoft’s Azure AI platform, for instance, includes built-in privacy and security features such as data encryption, access controls, and compliance certifications to ensure the responsible use of AI technologies.

Ethical Concerns

The use of AI in decision-making processes raises ethical questions about bias, fairness, and transparency. Companies need to develop ethical AI frameworks to address these concerns.

Example: Amazon faced criticism in 2018 when it was revealed that its AI-powered recruiting tool showed bias against women. The system, which was trained on resumes submitted to the company over a 10-year period, had learned to prefer male candidates due to the historical predominance of men in tech roles.

In response to such challenges, IBM has been at the forefront of developing ethical AI frameworks. The company launched its AI Ethics Board and published the “Everyday Ethics for Artificial Intelligence” guide to help developers and organizations create AI systems that are fair, transparent, and accountable.

Workforce Disruption

While AI is expected to create more jobs than it eliminates (97 million new jobs vs. 85 million displaced by 2025, according to the World Economic Forum), there will be significant workforce disruption. Organizations need to invest in reskilling and upskilling programs to prepare their workforce for the AI-augmented future.

Examples of companies addressing this challenge:

a) Amazon: Through its “Upskilling 2025” initiative, Amazon is investing $700 million to retrain 100,000 employees for in-demand jobs. The program includes Machine Learning University, which offers employees the opportunity to learn AI and machine learning skills.

b) AT&T: The company’s “Future Ready” program aims to reskill and upskill its workforce for the digital age. AT&T has invested over $1 billion in this initiative, which includes online courses, nanodegrees, and partnerships with universities to help employees acquire new tech skills, including AI and data science.

Future Prospects

The opportunities presented by AI augmentation are significant. Companies that invest in AI augmentation now are likely to gain a significant competitive advantage in the coming years. According to a survey by McKinsey, 73% of US companies have already adopted AI in some areas of their business, with generative AI leading the way.

The Need for Technical Expertise in Strategy Planning

Unique Skill Set

The demand for AI strategists with both technical and business acumen is rising. According to LinkedIn’s 2020 Emerging Jobs Report, AI Specialist roles have seen a 74% annual growth over the past four years. This highlights the growing need for professionals who can bridge the gap between technical AI knowledge and business strategy.

Companies like Google, Amazon, and Microsoft are actively seeking AI strategists to guide their AI initiatives and create new value propositions. The global AI market is projected to reach $1.8 trillion by 2030, reflecting the rapid adoption of AI across various industries.

Leaders like Andrew Ng, founder of deeplearning.ai and Landing AI, emphasize the importance of teaching AI strategy and monetization to non-technical professionals. AI strategists play a critical role in organisations by designing comprehensive AI plans that align with business objectives. They bridge the gap between the business and technical sectors, converting business demands into technological requirements and ensuring that AI efforts satisfy strategic goals. 

Strategists find high-impact use cases for AI augmentation, with an emphasis on improving decision-making, increasing productivity, and delivering useful insights to human workers. They evaluate data quality and infrastructure readiness before recommending enhancements to assist AI adoption. They promote ethical AI use by picking relevant AI technologies such as natural language processing and predictive analytics, which address issues about bias and privacy. AI strategists drive cultural change and adoption by fostering AI literacy and presenting its advantages to employees, as well as monitoring impact and ROI using established metrics and KPIs.

Conclusion

AI augmentation is the next frontier for company strategy and value development. As the technology evolves and becomes more accessible, organisations who engage in AI augmentation and build the appropriate skills will be well-positioned to seize new possibilities and drive innovation in their particular sectors. With AI anticipated to increase labour productivity by up to 40% by 2035, the moment to embrace AI augmentation is here. Companies who do not do so risk falling behind in an increasingly AI-powered corporate world.