Part V — Technology Evaluation in Developing Economies, Responsible Technology Adoption, and the Future of Technology Evaluation

Chapter 19. The Future of Technology Evaluation

19.1 Introduction

Technology evaluation is itself being transformed by technological change.

The technologies being evaluated are becoming faster, more interconnected, more autonomous, more adaptive, and more difficult to understand. Artificial intelligence can modify its outputs according to data and context. Digital platforms can affect millions of users. Autonomous systems can make decisions without continuous human intervention. Biotechnology can alter biological processes. Quantum technologies may eventually transform computing and communication. Climate technologies may affect energy and environmental systems at large scale.

Consequently, technology evaluation must evolve.

The future of technology evaluation will require greater emphasis on:

continuous assessment;

real-time monitoring;

adaptive governance;

scenario analysis;

interdisciplinary expertise;

stakeholder participation;

AI-assisted evaluation;

sustainability;

systemic risk;

resilience;

human values; and

long-term societal consequences.

19.2 From Static Evaluation to Continuous Evaluation

Traditional evaluation often follows a sequence:

Evaluate → Decide → Implement

Future technology evaluation will increasingly require:

Evaluate → Implement → Monitor → Learn → Re-evaluate → Adapt

This is particularly important for technologies that change through software updates, machine learning, new data, or changing user behaviour.

19.3 The Dynamic Nature of Technology

Technology is increasingly characterized by continuous change.

Software can be updated weekly or even continuously.

AI models can be retrained.

Cybersecurity threats evolve.

Users develop new practices.

Regulations change.

Markets change.

Competitors introduce new capabilities.

Consequently, an evaluation conducted at one point in time may become outdated.

Technology evaluation should therefore include an explicit validity period.

19.4 Real-Time Technology Assessment

Real-time technology assessment seeks to evaluate technologies while they are developing and being deployed rather than waiting until their impacts are fully established.

This approach is particularly relevant to:

AI;

biotechnology;

digital platforms;

autonomous systems;

social media;

emerging energy technologies; and

rapidly changing digital infrastructures.

The traditional distinction between development and assessment becomes increasingly difficult to maintain.

19.5 Anticipatory Governance

Anticipatory governance involves preparing for possible technological futures before those futures fully materialize.

This does not mean predicting the future with certainty.

Instead, it involves:

identifying plausible scenarios;

identifying risks;

considering alternative pathways;

developing governance mechanisms;

monitoring indicators; and

preparing adaptive responses.

The approach recognizes that uncertainty is unavoidable.

19.6 Scenario-Based Technology Evaluation

Scenario planning can help decision-makers examine alternative futures.

For example, an organization considering an AI platform could evaluate:

Scenario A: Rapid Adoption

AI becomes deeply integrated into organizational processes.

Scenario B: Regulated Adoption

Regulatory requirements increase substantially.

Scenario C: Low Adoption

Users resist the technology.

Scenario D: Security Disruption

Major cybersecurity events affect trust.

Scenario E: Technological Replacement

A competing technology makes the selected platform obsolete.

The purpose of scenarios is not to predict which future will occur. It is to test the resilience of the decision under different conditions.

19.7 Artificial Intelligence and the Future of Evaluation

AI will increasingly be both the subject and instrument of technology evaluation.

AI can assist with:

evidence synthesis;

literature analysis;

risk identification;

scenario generation;

anomaly detection;

document review;

simulation;

forecasting;

monitoring; and

decision support.

However, AI-assisted evaluation creates its own risks.

AI-generated analysis can contain errors, omissions, biases, fabricated references, or inappropriate assumptions.

Therefore:

AI can support evaluation, but responsibility for evaluation cannot automatically be delegated to AI.

Human verification remains essential, particularly for consequential decisions.

19.8 AI-Assisted Evidence Synthesis

AI systems can potentially analyze large volumes of:

academic literature;

technical reports;

regulatory documents;

user feedback;

incident reports;

financial data;

environmental information; and

operational records.

This can improve the speed of evaluation.

However, evaluation systems must distinguish between:

retrieved evidence;

generated summaries;

inferred conclusions;

assumptions; and

unsupported statements.

Evidence provenance will therefore become increasingly important.

19.9 Evidence Provenance

Evidence provenance refers to the ability to trace an evaluation claim back to its source.

A future technology evaluation system should ideally allow an evaluator to ask:

Where did this claim come from?

What evidence supports it?

When was the evidence collected?

What population or context did it concern?

What assumptions were used?

Has the evidence been independently verified?

This is particularly important when AI systems are used to produce analytical summaries.

19.10 Explainable Evaluation

The evaluation process itself should become more explainable.

An organization should be able to explain why a technology was:

selected;

rejected;

piloted;

expanded;

restricted;

modified; or

discontinued.

This creates decision transparency.

19.11 Digital Twins and Simulation

Digital twins can provide new opportunities for technology evaluation.

A digital twin represents a physical system or process digitally and can be used to model different conditions.

Potential applications include:

manufacturing;

transportation;

energy;

healthcare;

cities;

infrastructure; and

environmental systems.

Digital simulation can enable organizations to test scenarios before making physical investments.

However, simulations are only as reliable as their assumptions and data.

Therefore:

Simulation does not eliminate uncertainty; it changes the way uncertainty is represented.

19.12 Quantum Technologies

Quantum computing, communication, and sensing represent emerging technology domains requiring specialized evaluation.

Potential evaluation dimensions include:

technical performance;

scalability;

error correction;

infrastructure requirements;

energy consumption;

cybersecurity implications;

economic value;

workforce requirements;

strategic dependency; and

maturity.

Quantum technologies also illustrate the importance of avoiding hype-driven evaluation.

Technology assessment should distinguish demonstrated capability from theoretical potential.

19.13 Biotechnology and Synthetic Biology

Advances in biotechnology create opportunities in:

medicine;

agriculture;

environmental management;

materials;

food production; and

industrial processes.

They also create complex ethical, safety, ecological, and governance questions.

Technology evaluation must therefore address:

biological safety;

containment;

unintended effects;

long-term consequences;

ethical boundaries;

access;

equity;

regulation; and

public trust.

19.14 Autonomous Systems

Autonomous systems can make or execute decisions with varying degrees of human intervention.

Examples include:

autonomous vehicles;

robotic systems;

AI agents;

industrial robots;

autonomous drones;

automated financial systems; and

intelligent infrastructure.

Evaluation must therefore address the allocation of responsibility between humans and machines.

Questions include:

Who authorizes the system?

Who monitors it?

Who can override it?

Who is responsible for failure?

What happens when the system encounters an unexpected situation?

19.15 AI Agents and Autonomous Decision Processes

AI agents may increasingly perform sequences of tasks rather than simply respond to individual prompts.

This creates new evaluation requirements.

An AI agent may:

interpret a goal;

generate a plan;

interact with software;

retrieve information;

execute actions;

evaluate results; and

modify its approach.

Evaluation must therefore move beyond output accuracy to process-level evaluation.

Important dimensions include:

goal alignment;

action authorization;

error containment;

tool use;

data access;

security;

auditability;

reversibility; and

human intervention.

19.16 Technology and Human Agency

As technology becomes increasingly capable, preservation of human agency becomes an important evaluation issue.

Human agency means that individuals and institutions retain meaningful capacity to understand, choose, act, and exercise responsibility.

A technology should not unnecessarily reduce human control over important decisions.

This does not mean that automation should never be used.

Rather, evaluation should determine where automation is appropriate and where human judgment remains necessary.

19.17 Technology and Work

Automation and AI are likely to change occupational structures, although the precise effects will vary across sectors and contexts.

Evaluation should therefore consider:

task automation;

job redesign;

skill requirements;

productivity;

employee autonomy;

workplace surveillance;

training;

income distribution; and

organizational change.

Autor (2015) emphasizes the importance of examining the relationship between technology and tasks rather than treating employment effects as a simple technology-versus-human equation.

19.18 Future Technology Evaluation and Education

Educational institutions will increasingly need to evaluate technologies that affect:

teaching;

learning;

assessment;

academic integrity;

research;

student services;

administration;

doctoral education; and

quality assurance.

AI makes this particularly important.

UNESCO (2023) emphasizes the need for human-centred and appropriate approaches to generative AI in education and research.

Universities will therefore require technology evaluation systems capable of considering educational value alongside academic integrity, privacy, equity, accessibility, and governance.

19.19 Doctoral Education and Technology Evaluation

Doctoral education provides a particularly important setting for technology evaluation because doctoral researchers increasingly use:

reference-management software;

statistical tools;

qualitative-analysis systems;

AI-assisted literature tools;

generative AI;

data visualization;

simulation;

laboratory technologies; and

digital research platforms.

Evaluation should distinguish between using technology as a research tool and delegating scholarly judgment to technology.

The doctoral researcher remains responsible for:

research questions;

methodological decisions;

evidence interpretation;

argumentation;

ethical compliance;

originality;

attribution; and

research integrity.

19.20 Future Technology Evaluation and Research Integrity

As AI-generated text and analysis become more common, research integrity will require greater emphasis on:

source verification;

data provenance;

transparent methodology;

disclosure of AI assistance where required;

reproducibility;

human authorship responsibility; and

independent validation.

Technology should strengthen scholarship rather than replace scholarly responsibility.

19.21 Environmental Limits and Technology

The future of technology evaluation must also recognize ecological limits.

Technological innovation can generate environmental benefits but also consume:

energy;

water;

minerals;

land;

infrastructure; and

other natural resources.

The assumption that every technological improvement is environmentally beneficial is therefore inappropriate.

Evaluation must examine net environmental effects over the technology lifecycle.

19.22 Circular Technology Systems

The future of technology evaluation will increasingly consider circularity.

A circular technology system seeks to reduce waste through:

durability;

repair;

reuse;

remanufacturing;

recycling;

resource recovery; and

responsible product design.

Geissdoerfer et al. (2017) describe the circular economy as an alternative to linear patterns of production and consumption.

Technology evaluation should therefore ask:

What happens to the technology when its useful life ends?

19.23 Systemic Risk

As technologies become interconnected, risks can spread across systems.

For example:

Cloud infrastructure → financial services → healthcare → communications → public services

A failure in one component may affect others.

This creates systemic technology risk.

Evaluation should therefore move beyond individual products toward ecosystem-level assessment.

19.24 Technology Ecosystem Evaluation

A technology ecosystem can include:

hardware;

software;

users;

suppliers;

regulators;

infrastructure;

data;

financial systems;

standards;

platforms; and

complementary technologies.

Evaluating only one component may miss important dependencies.

19.25 Resilience as a Future Evaluation Criterion

Future technology systems should be evaluated not only for efficiency but also for resilience.

Efficiency asks:

Can the system perform effectively under normal conditions?

Resilience asks:

Can the system continue functioning or recover when conditions change?

Resilience includes:

redundancy;

adaptability;

recovery;

diversification;

contingency planning;

interoperability; and

learning.

19.26 Technology Evaluation and Trust

Public and organizational trust can determine whether technology is adopted.

Trust should not be treated simply as a communications problem.

Trust depends partly on:

demonstrated performance;

transparency;

accountability;

fairness;

privacy;

security;

institutional credibility; and

user experience.

A technically excellent technology can fail if stakeholders do not trust it.

19.27 The Future AMTEF Model

The future development of AMTEF can be represented through five levels.

Level 1: Technology

What is the technology?

Level 2: Organization

Can the organization adopt it?

Level 3: Society

What are the wider consequences?

Level 4: Ecosystem

How does it interact with other systems?

Level 5: Future

What long-term consequences and pathways may emerge?

This creates a five-level evaluation architecture:

Technology → Organization → Society → Ecosystem → Future

19.28 AMTEF 2.0: A Dynamic Evaluation Model

The original ten AMTEF dimensions remain central:

Technical performance

Functional effectiveness

Economic value

Organizational readiness

Human impact

Social consequences

Ethical responsibility

Environmental sustainability

Risk and security

Governance and regulation

For future applications, these dimensions can be evaluated dynamically across four stages:

Stage 1 — Anticipate

Identify potential benefits, risks, scenarios, and stakeholders.

Stage 2 — Evaluate

Collect evidence and assess the ten dimensions.

Stage 3 — Govern

Establish safeguards, accountability, monitoring, and decision rules.

Stage 4 — Learn

Monitor outcomes, update evidence, and re-evaluate.

19.29 Figure 19.1

The cycle then returns to anticipation.

This represents the central philosophy of the book: technology evaluation is not an endpoint. It is a continuous learning process.

19.30 From Technology Readiness to Responsible Readiness

Technology Readiness Levels (TRLs) primarily address technological maturity.

However, a mature technology may still be unsuitable for a particular organization.

AMTEF therefore proposes a broader concept:

Responsible Technology Readiness

Responsible Technology Readiness refers to the extent to which a technology is sufficiently mature, effective, economically viable, organizationally prepared, socially acceptable, ethically defensible, environmentally sustainable, secure, and governable for a particular context.

A technology should therefore pass through several readiness dimensions:

This distinction is especially important for emerging technologies.

19.31 Technology Evaluation as Organizational Learning

Technology evaluation should not merely determine whether a technology succeeds or fails.

It should generate knowledge.

Organizations should ask:

What did we learn?

Which assumptions were correct?

Which assumptions were incorrect?

What unexpected effects occurred?

What should be changed?

What evidence is still missing?

This creates an organizational learning loop.

19.32 Technology Evaluation and Strategic Management

Technology evaluation is increasingly a strategic management function.

Senior leaders need to understand:

technological opportunities;

risks;

competitive implications;

investment requirements;

organizational capabilities;

workforce consequences;

regulatory change;

sustainability;

geopolitical dependencies; and

long-term resilience.

Technology decisions should therefore be connected to organizational strategy.

19.33 The Role of Interdisciplinary Evaluation

No single discipline can adequately evaluate every emerging technology.

A technology evaluation team may require expertise from:

engineering;

computer science;

economics;

management;

sociology;

psychology;

law;

ethics;

environmental science;

public policy;

education;

medicine; and

data science.

Interdisciplinary evaluation reduces the risk of narrow technological thinking.

19.34 Independent Evaluation

For high-impact technologies, independent evaluation can strengthen credibility.

Independent evaluators may:

challenge assumptions;

identify conflicts of interest;

test evidence;

examine unintended consequences;

assess compliance; and

provide external assurance.

Independence should be proportionate to the level of risk.

19.35 The Future Technology Evaluation Professional

The emergence of complex technologies creates demand for professionals capable of integrating multiple forms of evidence.

The future technology evaluator will require competence in:

technology;

research methods;

data analysis;

economics;

risk;

ethics;

sustainability;

governance;

communication; and

strategic management.

This suggests the emergence of technology evaluation as an interdisciplinary professional field.

19.36 A Professional Competency Model

Table 19.1 — Technology Evaluation Competency Framework

19.37 Technology Evaluation and Public Policy

Governments will increasingly need technology evaluation capabilities to address emerging technologies.

Policy makers may need to evaluate:

AI;

biotechnology;

digital infrastructure;

energy technologies;

autonomous systems;

cybersecurity;

quantum technologies;

climate technologies; and

digital public services.

OECD (2023) emphasizes the need for technology assessment to provide strategic intelligence in environments characterized by uncertainty and rapid technological change.

19.38 International Cooperation

Technology increasingly crosses national borders.

International cooperation may therefore be required for:

cybersecurity;

AI governance;

data governance;

biotechnology;

climate technologies;

digital trade;

technical standards;

research integrity; and

environmental sustainability.

Global technology governance will require cooperation among governments, universities, businesses, civil society, researchers, and international organizations.

19.39 The Future Evaluation Dashboard

Organizations can develop dashboards that integrate AMTEF indicators.

Table 19.2 — Illustrative Future Technology Evaluation Dashboard

The dashboard should not replace expert judgment. It should support it.

19.40 Technology Evaluation and Human Development

The ultimate purpose of technology is not technology itself.

Technology is a means through which human beings pursue goals.

Sen's (1999) capability approach provides a useful philosophical foundation for this perspective because it shifts attention from resources alone toward what people are actually able to do and be.

Technology evaluation should therefore ask:

Does this technology expand meaningful human capabilities?

This question does not replace technical or economic evaluation. It complements them.

19.41 A Final AMTEF Decision Model

The complete AMTEF decision process can be expressed as:

Step 1 — Define the problem

What problem requires attention?

Step 2 — Define the technology

What technology is being considered?

Step 3 — Identify alternatives

What technological and non-technological alternatives exist?

Step 4 — Identify stakeholders

Who may benefit, contribute, or be affected?

Step 5 — Establish evidence requirements

What evidence is required?

Step 6 — Evaluate the ten AMTEF dimensions

Conduct multidimensional assessment.

Step 7 — Identify critical risks

Determine whether any risk requires special treatment.

Step 8 — Conduct stakeholder deliberation

Consider different perspectives and concerns.

Step 9 — Pilot where appropriate

Generate real-world evidence.

Step 10 — Decide

Adopt, modify, defer, restrict, or reject based on the evidence and governance process.

Step 11 — Monitor

Track outcomes and unintended effects.

Step 12 — Re-evaluate

Update the evaluation as circumstances change.

19.42 The Central Proposition of the Book

The central proposition developed throughout this book can now be stated clearly:

Emerging technologies should be evaluated not according to technological novelty alone, but according to their technical performance, functional effectiveness, economic value, organizational readiness, human consequences, social effects, ethical responsibilities, environmental sustainability, risks and security, and governance requirements within their specific context.

This proposition forms the foundation of AMTEF.

19.43 Final Research Agenda

Future research on technology evaluation should investigate several areas.

First, researchers should examine how multidimensional evaluation models can be operationalized quantitatively without reducing complex social and ethical questions to simplistic scores.

Second, research should investigate how AI can support technology evaluation while preserving evidence quality and human accountability.

Third, researchers should examine how evaluation frameworks can be adapted to developing economies.

Fourth, research should examine how technology evaluation can incorporate long-term environmental and intergenerational consequences.

Fifth, researchers should investigate how stakeholders can participate meaningfully in technology evaluation.

Sixth, research should examine how technology evaluation can be integrated into organizational governance.

Seventh, researchers should develop methods for evaluating systemic technology risks.

Eighth, doctoral research can investigate AMTEF empirically across industries, countries, technologies, and organizational contexts.

19.44 Suggested Future Research Questions

How effective is AMTEF in evaluating emerging technologies across different industries?

How does AMTEF perform in developing and emerging economies?

How can AI assist technology evaluation without compromising human accountability?

How can technology evaluation incorporate long-term environmental effects?

How should social and ethical consequences be operationalized in technology evaluation?

How can organizations measure responsible technology readiness?

How can technology evaluation identify systemic risks?

How can participatory technology assessment be integrated with organizational decision-making?

How should technology evaluation adapt to autonomous AI systems?

How can universities institutionalize multidimensional technology evaluation?

How can technology evaluation support sustainable development?

How can technology evaluation contribute to technological sovereignty and resilience?

How can technology evaluation frameworks be validated empirically?

What evaluation methods are most appropriate for high-uncertainty technologies?

How can technology evaluation become a continuous organizational learning process?

19.45 Part V Integrative Synthesis

The three chapters of Part V bring the book's central argument together.

Chapter 17 demonstrated that technology evaluation must be context-sensitive. Developing and emerging economies require particular attention to infrastructure, affordability, skills, digital inclusion, local capacity, technological dependency, environmental vulnerability, and development outcomes.

Chapter 18 established that responsible technology evaluation should become institutionalized. Technology should be evaluated before adoption, governed during implementation, monitored after deployment, and re-evaluated when circumstances change.

Chapter 19 demonstrated that technology evaluation itself must evolve as technologies become more autonomous, interconnected, adaptive, and systemic.

These arguments lead to a broader understanding of technology evaluation.

Technology evaluation is not simply:

“Does the technology work?”

It is:

“Does the technology work for the intended purpose, in this context, for these stakeholders, at an acceptable cost and risk, with appropriate ethical and environmental consequences, under adequate governance, and with sufficient evidence to justify responsible adoption?”

That question is the foundation of AMTEF.

19.46 The AMTEF Ten-Dimension Framework

Table 19.3 — Complete AMTEF Framework

The ten dimensions should not always be treated as equally weighted numerical variables.

Their relative importance may depend on the technology and context.

For example, a medical technology may require particularly rigorous attention to safety and clinical evidence. A financial technology may require greater attention to cybersecurity and financial risk. A university AI platform may require greater attention to academic integrity, privacy, human oversight, and educational effectiveness.

AMTEF is therefore designed as a flexible multidimensional framework rather than a rigid scoring instrument.

19.47 AMTEF as a Decision Philosophy

AMTEF can also be understood as a decision philosophy based on seven propositions.

Proposition 1: Technology is contextual

A technology cannot be evaluated independently of the environment in which it operates.

Proposition 2: Technology is multidimensional

Technical performance alone does not determine technological value.

Proposition 3: Technology creates both benefits and risks

Evaluation must therefore consider opportunities and consequences.

Proposition 4: Evidence is incomplete

Emerging technologies often involve uncertainty and limited historical evidence.

Proposition 5: Stakeholders matter

Technological consequences are experienced by people and communities, not only organizations.

Proposition 6: Evaluation must continue

Technology changes after adoption.

Proposition 7: Responsible adoption is conditional

A technology should be adopted only when its evidence, risks, capabilities, governance, and context are sufficiently understood for the intended purpose.

19.48 A Practical AMTEF Evaluation Template

Organizations can use the following template.

Technology

Name:

Purpose:

Developer/vendor:

Technology maturity:

Intended users:

Deployment context:

Problem

What problem is being addressed?

What alternatives exist?

Technical Evaluation

Performance:

Reliability:

Availability:

Security:

Interoperability:

Scalability:

Functional Evaluation

Problem-solution fit:

Expected outcomes:

Measured outcomes:

Economic Evaluation

Initial investment:

Operating cost:

TCO:

Expected benefits:

Financial uncertainty:

Organizational Evaluation

Leadership readiness:

Skills:

Infrastructure:

Processes:

Culture:

Human Evaluation

User impact:

Workload:

Agency:

Accessibility:

Social Evaluation

Inclusion:

Equity:

Community impact:

Distribution of benefits and risks:

Ethical Evaluation

Fairness:

Transparency:

Privacy:

Accountability:

Human oversight:

Environmental Evaluation

Energy:

Emissions:

Water:

Materials:

Waste:

Lifecycle:

Risk Evaluation

Technical:

Operational:

Cybersecurity:

Financial:

Legal:

Systemic:

Governance

Regulatory compliance:

Auditability:

Responsible owner:

Monitoring:

Review date:

Decision

Adopt:

Pilot:

Modify:

Defer:

Restrict:

Do not proceed:

The decision should be accompanied by documented evidence and rationale.

19.49 Final Reflection

Technology has never been merely a collection of machines, systems, algorithms, infrastructures, or tools. Technology represents choices about how human beings organize knowledge, resources, work, communication, production, health, education, and social life.

Consequently, technology evaluation is ultimately concerned with choices.

The central challenge is not to resist innovation, nor to accept innovation uncritically. The challenge is to understand innovation sufficiently well to make responsible decisions.

Emerging technologies create enormous possibilities. Artificial intelligence may transform knowledge work. Renewable energy may contribute to decarbonization. Biotechnology may transform healthcare and agriculture. Digital platforms may expand access to services. Advanced educational technologies may increase opportunities for learning. Autonomous systems may improve productivity and safety.

Yet each technological opportunity also raises questions concerning cost, access, privacy, security, employment, inequality, environmental impact, human agency, ethics, governance, and long-term consequences.

The responsible evaluator therefore occupies an important position between technological enthusiasm and technological pessimism.

The evaluator's task is evidence.

The evaluator asks:

What do we know?

What do we not know?

What evidence supports the expected benefits?

What risks have been identified?

Who benefits?

Who may be disadvantaged?

What environmental consequences may occur?

What safeguards are required?

Who is accountable?

What happens if the technology fails?

How will we know whether the technology continues to create value?

These questions transform technology evaluation from a procurement exercise into a discipline of responsible decision-making.

The Adams Multidimensional Technology Evaluation Framework provides one structured approach to this challenge.

Its fundamental principle can be expressed simply:

Evaluate technology before scaling it, govern technology while using it, monitor its consequences, and remain willing to re-evaluate it as evidence and circumstances change.

The future of technology should not be determined solely by what technology can do.

It should also be shaped by what human beings decide technology ought to do.

That is the ultimate purpose of technology evaluation.