I think we can focus on Chapter 5: Strategic Decisions. There are 7 strategic decisions that include: 1) AI Digital Infrastructure, 2) Data, 3) AI R & D and Innovation, 4)Talent Development, 5) Governance, 6) Investments and 7) Ethics, Equity and Inclusion. Each Strategic Decision has a goal, an ultimate outcome and objectives. Each objective has corresponding flagship projects, intermediate outcomes and Key Performance Indicators (KPIs).

For feedback, one can select one of the Strategic Decisions, pick any of its objectives and flagship projects and make suggestions (providing alternative text with justification). Additionally, for each objective, one can select an intermediate outcome and / or KPIs and make suggestions. One can also provide additional points that should be included in a specific section. For ease of reference, the information is reproduced below:

5.2. GOALS, OBJECTIVES, FLAGSHIP PROJECTS, OUTCOMES, KPIs

1. AI DIGITAL INFRASTRUCTURE

Goal 1: Modernise the national digital infrastructure for AI access and development

Ultimate Outcome 1: High-capacity digital infrastructure for AI access and development

Objective 1

1.1 Invest in the setup and expansion of AI-ready digital infrastructure across the country

Flagship Projects

1.1.1 Implement a National Broadband Expansion Program (high-speed internet, 5G networks, etc.)

1.1.2 Build robust edge computing capabilities to support AI research, development, and deployment

Intermediate Outcomes

Increased and uninterrupted access and coverage of broadband connectivity

Increased penetration of Edge AI contextualised devices

 

KPIs

% national broadband connectivity coverage

No. of devices that can run AI models on the edge infrastructure

 

Objective 2

1.2 Enhance High Performance Computing (HPC) clusters

Flagship Projects

1.2.1 Build three TIA 942 (ANSI standard) AI-capable data centres within 5 years

 

Intermediate Outcomes

1. Increased local compute power

2. Enhance capacity for training models at a local infrastructure level

 

KPIs

No. of local data centres meeting the TIA 942s standard (ANSI standard) / (GPUs)

% of public sector data centre compute capacity integrated into the AI HPC cluster

 

Objective 3

1.3 Increase the supply and use of green energy sources to power AI infrastructure, ensuring sustainability and reducing environmental impact

 

Flagship Projects

1.3.1 Review power supply to digital infrastructure and enhance the ratio of green energy

 

Intermediate Outcomes

Increased % contribution of green energy powering AI infrastructure.

 

KPIs

% of green energy powering data centres

 

Objective 4

1.4 Develop domestic manufacturing facilities for AI-specific hardware, such as specialised chips and semiconductors, to reduce reliance on foreign technology providers

 

Flagship Projects

1.4.1 Establish more local device assembly centres

1.4.2 Establish a national semiconductor manufacturing facility to produce AI-specific chips domestically within five years

1.4.3 Leverage on AfCTA and regional regulatory frameworks and incentives for regional trade to support the manufacturing sector

 

Intermediate Outcomes

Reduced cost of smart devices utilised in the AI lifecycle

Increased availability of semiconductors for use locally

Export semiconductors regionally and continentally

New jobs from technology assembly and manufacturing

 

KPIs

Decrease in average cost of a computing device

Number of semiconductors manufactured

Number of semiconductors exported

No. of new jobs in technology assembly and manufacturing

 

Objective 5

1.5 Establish robust national cybersecurity infrastructure

 

Flagship Projects

1.5.1 Establish a multistakeholder taskforce within the proposed NSOC framework to respond to AI-specific emerging threats

1.5.2 Implement advanced AI- specific threat detection and response systems across critical sectors

 

Intermediate Outcomes

Enhanced national information security through advanced AI-specific threat detection and response systems across critical sectors

 

KPIs

100% pass rate on cyber security audits and effective resolutions of detected threats

 

Objective 6

1.6 Forge partnerships and collaborations to develop and improve AI hardware and software

 

Flagship Projects

1.6.1 Create partnerships with other countries and global tech companies for knowledge transfer and development of digital infrastructure development for AI

1.6.2 Create collaborations between the government and the private sector to ensure that AI technology supply chains remain robust and innovative

1.6.3 Create partnerships with global tech companies to combat AI threats emerging from misinformation and disinformation

 

Intermediate Outcomes

Implemented PPP projects

 

KPIs

2 PPP projects in the next 5 years

 

2. DATA

Goal 2: Establish a robust and sustainable data ecosystem for AI and innovation

Ultimate Outcome 2: Enhanced dataset quality, useability, shareability and sovereignty

Objective 1

2.1 Create a robust and responsive data governance framework

 

Flagship Projects

2.1.1 Develop a national data policy and strategy informed by best practices

2.1.2 Create a legal sharing framework for all stakeholders to share data with appropriate incentives

2.1.3 Establish an AI-task force within the proposed Data Governance Office Coordination Committee with representation from key data actors in selected in MCDAs, counties (CoG), private sector and civil society representatives, a Data Governance Office in the Ministry, and Data Officers

2.1.4 Enhance data residency requirements and ensure compliance with national data laws and regulations regarding data handling and storage

2.1.5 Classify, categorise and regulate access to data collected within Kenya and from Kenyans

 

Intermediate Outcomes

1. Enhanced assessment of data sovereignty

KPIs

 

Comprehensive national data policy and strategy adopted

% reduction in data silos in the public sector

% of locally produced data meeting the standards of sovereignty

% compliance with national data laws and regulations

 

Objective 2

2.2 Develop and implement secure data sharing, data access and data interoperability protocols

 

Flagship Projects

2.2.1 Create comprehensive national standards and protocols for data and metadata to ensure consistency and facilitate seamless data integration and exchange across government ministries, departments, and agencies (MDAs) as well as the private sector

2.2.2 In compliance with relevant laws, incentivize data sharing and collaboration among stakeholders, including private sector, research institutions, government agencies, and civil society organisations

2.2.3 Implement data initiatives (within guardrails/frameworks to prevent misuse and restricted national access and controlled cross border data flows)

2.2.4 Develop and implement secure data access frameworks that use encryption and authentication to safeguard sensitive data while allowing for wider accessibility

 

Intermediate Outcomes

1. Increased data sharing and access

 

KPIs

No. of local datasets openly shared in compliance with national laws and policies

No. of formal data sharing agreements between institutions

 

Objective 3

2.3 Incentivize the creation of open high quality AI training datasets

 

Flagship Projects

2.3.1 Design and implement national data quality standards and protocols for data collection, cleaning, validation, and integration across sectors

2.3.2 Create local data labs with clean, validated, and integrated datasets for access by researchers and AI model developers

 

Intermediate Outcomes

1. Enhanced quality datasets for AI training

 

KPIs

% of usable datasets for AI models training

Number of local data labs for AI training datasets

 

3. AI R&D AND INNOVATION

 

Goal 3: Drive the development of cutting-edge localised AI models and solutions through a thriving local R&D and innovation

Ultimate Outcome 3: Increased contribution of AI businesses to GDP in priority sectors

 

Objective 1

3.1 Enhance and expand AI research capabilities at universities, TVETs, research centres, and innovation hubs across Kenya

 

Flagship Projects

3.1.1 Nurture R&D for public sector innovation 3.1.2 Establish AI research centres of excellence and innovation clusters across different regions

3.1.3 Engage the young workforce to advance research and development through research programs with more local innovation

3.1.4 Establish partnerships between academia, industry, and government to facilitate AI R&D, innovation and evaluation

 

Intermediate Outcomes

Accelerated local AI R&D and innovation capacity

 

KPIs

No. of published AI journal papers from local authors

Number of patents, trademarks and copyrights registered for AI

 

Objective 2

3.2 Launch and implement an AI Innovators Program that fosters a robust innovation ecosystem for cutting-edge AI model experimentation

 

Flagship Projects

3.2.1 Position Kenya as a regional hub for localised AI model development

3.2.2 Enhance science parks and innovation districts to attract and support tech and AI companies and startups

3.2.3 Upgrade tech hubs to provide mentorship and incubation services

3.2.4 Develop and use local data sources for AI development ultimately building AI models tailored to priority local problems

3.2.5 Prioritise the development of AI models that solve pressing social problems and are inclusive, focusing on edge and small AI models

 

Intermediate Outcomes

2. Increased AI entrepreneurs in the AI innovation ecosystem

3. Increased local AI models

 

KPIs

No. of AI entrepreneurs

% of AI models addressing local challenges

 

Objective 3

3.3 Develop the market for local AI solutions

 

Flagship Projects

3.3.1 Promote adoption and commercialization of locally developed AI solutions by creating regional and global market access opportunities

3.3.2 Incentivize local and regional markets to purchase locally manufactured AI products

3.3.3 Review Public Procurement Regulations to ensure that the Government prioritises the procurement of locally developed AI products

3.3.4 Incentivize edge AI model development and innovations

 

Intermediate Outcomes

Expanded markets for local AI solutions

 

KPIs

% of market penetration for local AI solutions in Kenya

No. of new regional markets for local AI solutions

 

Objective 4

3.4 Create an enabling environment for local AI companies to start and scale

 

Flagship Projects

3.4.1 Create collaborative AI innovation hubs

3.4.2 Create incubation and acceleration opportunities for new AI startups

3.4.3 Develop an AI resource toolkit for startups to provide resources on online platforms (e.g. access to data centres, access to public data, free cloud credits and development tools to 500 AI startups)

 

Intermediate Outcomes

Enhanced commercialization of local AI models

New AI jobs in model development

 

KPIs

No. of local AI models commercially deployed in priority sectors

No. of jobs created with AI models

 

4. TALENT DEVELOPMENT

 

Goal 4: Build a robust pipeline of competent and agile AI workforce for Kenya

Ultimate Outcome 4: Robust pipeline of competent and agile AI workforce

 

Objective 1

4.1 Integrate AI and data science education into school curricula at all levels

 

Flagship Projects

4.1.1 Develop an AI awareness and foundational skills awareness for schools

4.1.2 Roll out the AI awareness and foundational skills curricula in schools

4.1.3 Develop and implement as Training program for AI trainers program across different levels

 

Intermediate Outcomes

Enhanced awareness of AI skills

Enhanced foundational AI skills

 

KPIs

No. of primary schools running AI and data awareness courses

No. of secondary school with foundational AI and data skills training

 

Objective 2

4.2 Develop AI talent to meet emerging demands of the AI ecosystem

 

Flagship Projects

4.2.1 Develop and implement common courses on AI in tertiary education

4.2.2 Design and implement industry-driven quality specialised training programs that nurture local AI/data talent to meet industry needs (includes cybersecurity)

4.2.3 Develop and implement an AI training of trainers (AI TOT) program

4.2.4 Incentivize the development of AI talent

4.2.5 Create partnerships for AI talent development and placement

4.2.6 Create a policy for knowledge and skills transfer for specialised skills in implementing complex AI projects

4.2.7 Map AI talent for relevance and gaps

 

Intermediate Outcomes

Enhanced deep AI and data skills

 

KPIs

● % of workforce who achieve advanced AI and data certifications

● No. of universities with common courses on AI and data

● No. of TVET institutions with common courses on AI and data

 

Objective 3

4.3 Create partnerships for AI talent development and placement

 

Flagship Projects

4.3.1 Create a mentorship, apprenticeship and career development program to ensure desired growth

4.3.2 Enhance the Presidential Digital Talent Program (PDTP) and the Public Service Internship Program (PSIP) to allow for specialisation in AI and Data Analytics

4.3.3 Partner with existing innovation hubs and research centres regionally and globally to enhance access to postgraduate opportunities in AI

4.3.4 Implement PPPs in talent development

 

Objective 4

4.4 Acquire quality foreign AI talent

 

Flagship Projects

4.4.1 Develop and implement a national AI talent acquisition program to fill gaps

4.4.2 Review foreign policy regarding work visas to prioritise AI talent acquisition

 

Intermediate Outcomes

Increased AI capacity in Kenya

 

KPIs

No. of foreign AI professionals acquired

 

5. GOVERNANCE

 

Goal 5: Establish an agile governance and adaptable legal framework for AI

Ultimate Outcome 5: Agile governance and adaptable legal framework for AI

 

Objective 1

5.1 Develop a harmonised national policy framework for AI and emerging technologies

 

Flagship Projects

5.1.1 Develop a national AI and emerging technologies policy that aligns with the AI strategy

5.1.2 Develop a national cybersecurity policy

 

Intermediate Outcomes

1. Smooth implementation of AI and emerging technology projects

 

KPIs

No. of policies implemented that form a harmonised framework for AI and emerging technology.

 

Objective 2

5.2 Develop risk and safety frameworks to govern AI development and deployment (technical)

 

Flagship Projects

5.2.1 Develop local ethical and safety standards in AI development and deployment

5.2.2 Implement AI ethical and safety standards through conformity assessment schemes and technical specifications/regulations

5.2.3 Develop a national AI risk and safety institute

 

Intermediate Outcomes

2. Enhanced risk and safety standards for trustworthy AI development and employment.

 

KPIs

No. of ethical and safety standard policies and regulations adopted.

 

Objective 3

5.3 Revise and develop agile legal and regulatory frameworks to meet the demands of AI

 

Flagship Projects

5.3.1 Review relevant legislations (employment and labor relations, IP, computer misuse and crimes, etc.) to reflect the demands of the AI and emerging technology

5.3.2 Harmonize East and Central Africa data laws, tax laws, cyber security laws for secure and compliant cross-border data transfer and to enhance competitiveness in AI

5.3.3 Proactively implement a soft regulatory framework for AI

5.3.4 As AI matures in Kenya, develop an AI and Emerging Technology Act and regulations

5.3.5 Develop a flexible regulatory environment through the use of regulatory sandboxes to inform the development of AI regulatory framework and standards

 

Intermediate Outcomes

3. Enhanced conformity of legislation and regulations to meet dynamic development of AI and emerging technology.

 

KPIs

No. of laws reviewed and enacted to meet the context of AI and emerging technology.

 

Objective 4

5.4 Pursue collaborative intra- and inter-government, non-governmental, and private sector AI governance approaches

 

Flagship Projects

 

5.4.1 Develop and implement an AI and emerging tech diplomacy program

5.4.2 Promote regional and international cooperation to share knowledge, align AI standards and collaborate on AI challenges ensuring Kenya’s active participation in regional and global AI and emerging tech ecosystem

5.4.3 Enhance participation in AI policy making and programming processes to include the public, developers and consumers

 

Intermediate Outcomes

Enhanced partnerships and collaborations in AI and emerging tech

Enhanced public support and buy-in of AI policies and programs

 

KPIs

No. of government led AI programs that target public buy-in of policies and programs.

 

6. INVESTMENTS

 

Goal 6: Strategically accelerate public and private investments in AI

Ultimate Outcome 6: Increased investments in AI

 

Objective 1

6.1 Incentivize investments in AI from both local and foreign private investors

 

Flagship Projects

6.1.1 Leverage on PPPs to advance investment in localised AI and tech solutions (e.g. public and private sector partnering with Venture Capital firms)

6.1.2 Review and update AI policy and regulatory frameworks to create a favourable investment environment for AI development

6.1.3 Incentivize pension funds, public capital markets, and local and international private sector to invest in the local AI R&D and innovation ecosystem

6.1.4 Implement through PPPs an investor education program to train at least 1,000 potential investors on evaluating and investing in AI solutions

 

Intermediate Outcomes

Increased investments in the AI ecosystem

 

KPIs

Amount of investments flowing into the 100 promising AI-related ventures (US$ m)

Amount of private sector investments in government-led AI initiatives (US$ m)

 

Objective 2

6.2 Re-orient public resource allocation to prioritise investments in AI

 

Flagship Projects

6.2.1 Create a national AI and emerging tech innovation fund (from the R&D 2% of GDP in NRF) to provide grants and financial support in AI development

6.2.2 Create AI Special Economic Zones (SEZs)

 

Intermediate Outcomes

Growth in the local tech/AI ecosystem

 

KPIs

No. of AI startups in SEZs

No. of AI startups funded with at least $10m in 5 years

 

Objective 3

6.3 Position Kenya as an investment destination for AI

 

Flagship Projects

6.3.1 Market Kenya as an investment destination for AI

6.3.2 Promote success stories to showcase at least 50 successful tech startups, inspiring investment in the AI sector

 

Intermediate Outcomes

Increased foreign direct investment (FDI) for AI

 

KPIs

Amount of FDI into AI SEZs

No. of success stories of AI investment

 

7. ETHICS, EQUITY AND INCLUSION

 

Goal 7: Foster a culture of ethical, equitable, and inclusive AI development and deployment

Ultimate Outcome 7: Enhanced ethicalness, equity and inclusiveness of AI solutions

 

Objective 1

7.1 Promote ethical, responsible and inclusive AI development and deployment

 

Flagship Projects

7.1.1 Establish a mandatory ethical impact assessment process for AI technologies for public sector

7.1.2 Update and develop guidelines for accountable public sector AI procurement and deployment

7.1.3 Develop sector-specific standards and requirements on ethical AI development and deployment that are aligned to the national values and includes vulnerable groups

7.1.4 Develop a complaints and redress mechanisms for citizens to report AI-related concerns (e.g. CAJ/Ombudsman)

 

Intermediate Outcomes

1. More humancentric, safer and inclusive AI solutions

 

KPIs

Reduction in reported No. of incidents involving unsafe or harmful AI behaviours (such as data breaches, privacy violations, etc.)

 

Objective 2

7.2 Promote inclusivity and national values in AI development and deployment

 

Flagship Projects

7.2.1 Implement data labelling and classification policies that address bias

7.2.2 Develop an ethical framework and define ethical principles and considerations

7.2.3 Sponsor and spearhead inclusivity in the data value chain

7.2.4 Maintain a public repository of ethical AI use-cases and best practices to guide development and deployment of AI in the country

 

Intermediate Outcomes

2. Demonstrated tangible benefits of AI solutions for all Kenyans

 

KPIs

Increased % of representative datasets

No. of ethical AI use-cases

 

Objective 3

7.3 Enhance public AI literacy

 

Flagship Projects

7.3.1 Launch a public awareness campaign on AI rights, disinformation, misinformation, protection and safe development

7.3.2 Educate policymakers and civil servants on ethical, equitable and inclusive AI

 

KPIs

Increase in Number of public awareness campaigns and training workshops on inclusive AI