Navigating the AI Landscape: Insights from McKinsey and Lessons for AI Startups

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McKinsey’s recent report, “The state of AI in 2023: Generative AI’s breakout year”, provides valuable insights into the current state of AI, particularly generative AI. As the report highlights, the adoption of AI technologies is rapidly increasing, with one-third of surveyed organisations using generative AI in at least one business function. However, the report also raises concerns about the risks associated with AI, including inaccuracy, cybersecurity, and regulatory compliance.

Navigating the AI Landscape- Insights from McKinsey and Lessons for AI Startups

For AI startups, these insights are crucial. The AI landscape is evolving rapidly, and while the opportunities are immense, so are the challenges. The recent layoffs at major tech companies, as well as the growing skepticism around AI hype, underscore the need for caution.

One key takeaway from the report is the importance of expertise. In the current market, many organisations are attempting to build products that are essentially thin wrappers over existing APIs. However, without specific expertise, these solutions are easily copied, leading to a lack of differentiation and competitive advantage.

At NotCentralised, we believe that the key to success in the AI space is to build solutions with inherent intellectual property (IP) and deep expertise. Products that we are building for clients are a testament to this approach. We’ve been creating AI solutions that are built on a deep understanding of financial operations, AI and blockchain technology.

The McKinsey report also highlights the impact of AI on the workforce, with organisations anticipating workforce cuts and large reskilling efforts. This underlines the importance of human expertise in the AI space. While AI can automate certain tasks, it cannot replace the need for deep domain knowledge and strategic thinking.

There were some key business concerns when it comes to AI that were highlighted in this report

Navigating the AI Landscape- Insights from McKinsey and Lessons for AI Startups

As we can see, Inaccuracyand Cybersecurity are two significant concerns in the AI landscape, as highlighted by the McKinsey report. At NotCentralised, we take these concerns seriously and below we suggest some strategies to address them.

Inaccuracy

Inaccuracy in AI can lead to incorrect predictions or decisions, which can have significant implications, especially in fields like finance and healthcare. To mitigate this risk, we focus on the following:

Data Quality

We ensure the data used to train our AI models is of high quality and relevant to the problem at hand. This includes rigorous data cleaning and preprocessing steps.

Model Validation

We employ robust validation techniques to evaluate the performance of our AI models. This includes using separate datasets for training and testing, cross-validation, and other statistical techniques to ensure our models generalise well to unseen data.

Continuous Monitoring

AI models can drift over time as the data they interact with changes. We have systems in place to continuously monitor model performance and retrain models as needed.

Cybersecurity

AI systems, like any digital system, are vulnerable to cyber threats. At NotCentralised, we address this risk through:

Secure Development Practices

We follow secure coding practices and conduct regular security audits of our codebase to identify and fix potential vulnerabilities.

Data Encryption

We use strong encryption for data at rest and in transit, ensuring that even if a breach occurs, the data is unreadable to unauthorised individuals.

Access Control

We implement strict access control measures, ensuring only authorised individuals can access sensitive data and systems.

Incident Response Plan

We have a robust incident response plan in place to quickly identify, respond to, and recover from any potential security incidents.

By addressing these concerns proactively, we aim to build trust with our users and stakeholders, and ensure our AI solutions are both effective and secure.

Additionally, here’s some further thoughts from us:

  1. The rapid adoption of gen AI is a testament to its potential. However, organisations must be mindful of the risks associated with it, particularly inaccuracy. Robust validation and verification processes should be in place to ensure the reliability of gen AI outputs.

  2. The impact on the workforce is a critical aspect to consider. While generative AI can automate certain tasks, it’s essential to have a plan for reskilling employees whose roles may be affected. This not only helps in managing the transition but also ensures that the organisation can fully leverage the potential of gen AI.

  3. The report highlights that high performers are leading the way in generative AI adoption. This suggests that having a strong foundation in AI capabilities can provide a competitive edge in leveraging emerging technologies like gen AI.

  4. Lastly, the fact that generative AI has become a focus for company leaders indicates its strategic importance. This underscores the need for leaders to understand AI and its implications to make informed decisions.

In conclusion, while AI will continue to be a critical driver of innovation, startups need to be mindful of the risks and challenges. Building solutions with inherent IP, deep expertise, and a focus on addressing real-world problems will be key to navigating the evolving AI landscape.

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Mark
Monfort

Mark drives innovation with his deep understanding of AI, blockchain, and data technologies. His experience spans over 15 years of contributions to finance, technology, and operational strategy across Australia, Europe, and North America.

In 2021, he transitioned from Head of Data and Technology at a leading Australian accounting firm to startups. Prior to this, he worked in equity and macroeconomic research in the capital markets space.

Mark brings a passion for data and insights to NotCentralised. His understanding of AI and blockchain technology is central to the development of workplace productivity and financial system modernisation products, including SIKE and Layer-C. Mark’s dynamic and solutions-focused methods enable the navigation of complex technological landscapes and new market potentials.

Mark holds an Executive Master’s and a Bachelor of Commerce. He led the creation of the Australian DeFi Association and serves on the advisory board for the Data Science and AI Association of Australia. His commitment to such communities demonstrates his enthusiasm for emerging technologies and vision of positive change through technology adoption.

Nick
Bishop

Nick spearheads product strategy and institutional business development, leveraging a rich background spanning 23 years in capital markets and financial services across the UK, the US, and APAC.

In 2020, Nick transitioned into startups, bringing extensive experience in asset management and corporate advisory from roles including Director, Head of Australian Fixed Income at Abrdn and Managing Director, Head of Corporate Credit at Gresham Partners. His expertise extends to client management across the government and private sectors.

With a First Class degree in Law and Criminology and Chartered Financial Analyst experience since 2002, Nick is known for his energetic and creative approach, quickly appraising business models and identifying market opportunities.

Beyond his role at NotCentralised, Nick actively contributes to multiple startups and SMEs, holding various Board and advisory positions and applying his institutional expertise to early-stage ventures. Nick is fascinated by emerging technologies with significant societal impact and loves to immerse himself in nature.

Arturo
Rodriguez

Arturo leads product development and software engineering, applying over two decades of experience in technology, capital markets, and data science. With his years of programming expertise, Arturo smoothly transitioned into blockchain, AI, and machine learning.

Arturo has built and sold technology startups across Europe, following quant derivatives roles in global investment banks. His prior experience includes data projects for the NHS in the UK, Strategic Technology Advisor at Land Insight, and Senior Advisor to OpenInsight, where he built predictive models for vessel usage in commodity markets.

A mathematics and statistics graduate from Stockholm University, Arturo’s early grounding in logic problems and data manipulation techniques is evident in his practical applications. His work building equity derivative pricing models for Merrill Lynch and Royal Bank of Scotland showcased Arturo’s highly specialised skillset.

Arturo relocated from London to Australia in 2020. Beyond NotCentralised, his passion for technology and industry involvement extends to the Australian DeFi Association, which he co-founded, and regular contributions to the Data Science and AI Association.