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Skills application

Instruction and application
Complete

Building your ML/AI horizon scanning strategy: Your first 360° scan​

In this skills application, you will conduct a comprehensive horizon scanning exercise across all four key dimensions of ML/AI development using a structured approach.

Learning this skill brings value to the workplace by helping businesses to anticipate trends, mitigate risks and seize opportunities in a rapidly evolving field like ML/AI.

Skills application illustration

Context​

Horizon scanning in ML/AI requires monitoring developments across multiple domains. In this activity, you'll practise conducting a complete scan across academia, industry, regulation and startups using a standardised template. This will help you develop a systematic approach to staying informed about emerging ML/AI trends and developments.

Instructions and materials​

Conduct a comprehensive horizon scanning exercise across all four key dimensions of ML/AI development using a structured approach. To complete the skills application, follow the steps below.

1. Review the template.​

Review the Horizon Scanning Template form at the bottom of the page. The template captures key developments across Academic research, Industry implementation, Regulatory landscape, and Startup innovation.

2. Complete a horizon scanning exercise.​

Using the ML/AI developments digest below:

  • Scan through developments in each domain.
  • Record key findings in the template form.
  • Flag high-impact developments.

3. Reflect on findings.​

Reflect on your findings and make notes in the templated form, being sure to:

  • Identify connections between dimensions.
  • Note potential impact on your organisation.

Materials​

Digest Summary: Recent ML/AI Advancements​

Recent advancements in AI and ML encompass significant strides in reinforcement learning, the integration of AI across various industries, and the emergence of ethical considerations. Notably, pioneers in reinforcement learning have been recognized with the Turing Award, highlighting the importance of this area in modern AI applications.

Academic Research:

✨

Reinforcement Learning Recognition

Andrew Barto and Richard Sutton awarded the Turing Award.

✨

AI in Proteomics

Researchers leveraging AI to enhance the study of proteins (e.g. UK Biobank).Industry Trends:

✨

AI in Finance

Major financial institutions integrating AI for predictive modeling and risk management.

✨

Algorithmic Advancements

Transformers, Federated Learning gaining widespread adoption.

Best practices​

  • Horizon scanning isn’t just about confirming what you already know — it’s about spotting weak signals andemerging trends that could disrupt or enhance the AI/ML landscape.
  • Set Up Alerts: Follow AI news, journals, and tech reports.
  • Engage with Experts: Attend AI conferences and forums.
  • Monitor Policy Changes: Stay ahead of regulations impacting AI.

Define scope and objectives

Academic research insights

Industry trends and innovations

Regulatory and ethical developments

Startup ecosystem and investments

Opportunities and risks

Define scope and objectives • What specific area of AI/ML are you scanning for? (e.g. healthcare, finance, automation, ethics, regulation) • What is your primary goal for horizon scanning? (e.g. identifying risks, spotting opportunities, monitoring competitors, staying compliant) • What is your time frame for analysis (short term: 6–12 months; medium term: 1–3 years; long term: 3+ years)?
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Academic research insights • What are the latest breakthroughs in AI/ML research? (List key papers or conferences.) • Are there emerging subfields that could impact your industry? (e.g. neurosymbolic AI, self-supervised learning) • Are universities collaborating with industry on new innovations?
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Industry trends and innovations • What new AI/ML advancements are being developed by major tech companies? • Are there any emerging technologies gaining traction? (e.g., Generative AI, Edge AI, AutoML) • How are businesses integrating AI/ML to enhance workflows?
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Regulatory and ethical developments • Are there any new AI-related regulations or policies on the horizon? (e.g. GDPR, AI Act) • How are regulators addressing AI ethics, bias and accountability? • What industry standards or governance frameworks should you be aware of?
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Startup ecosystem and investments • Which AI startups are gaining attention, and what problems are they solving? • Are there any notable funding trends or major acquisitions in AI? • What areas of AI/ML are attracting the most venture capital investment?
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Market and competitive landscape • Who are the key players in AI/ML within your industry? • Are there any new entrants disrupting the market? • How are competitors leveraging AI to gain an edge?
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Opportunities and risks • What opportunities does AI/ML present for your organisation? • What are the biggest risks or challenges associated with AI adoption? • How can these risks be mitigated?
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Actionable next steps • What actions can your organisation take based on your findings? • What further research or monitoring should be conducted? • Who within your organisation should be informed of these insights?
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In progress