AI for Humans: Assessing Readiness, Setting Clear Goals

Before we get started with part 1 of our AI for Humans series, it’s worth sharing how we view AI at Human Kind:

• At its core, AI is fundamentally a technology and should be viewed as such.
• We believe a far too simplistic use of the term ‘AI’ is clouding meaningful debate about when and where this technology can provide value.
• AI should be used to improve the human experience.

When introducing AI into your business, it is essential to assess whether the foundations are in place. Not every organisation is ready to act, and forcing progress without clarity on capabilities and constraints increases the likelihood of failure.

This post outlines three exercises to help you identify what to tackle first, with clear criteria to inform when and where to apply AI tools effectively. These are not theoretical steps. They are designed to surface practical blockers and inform immediate next steps.

We also highlight opportunities to align this early-stage work with sustainability goals, rather than treating environmental impact as a separate concern.

Exercise 1: The AI Readiness Assessment

Too many AI projects begin with solution thinking before the problem is well defined. This exercise is designed to cut through that instinct by evaluating five critical readiness areas.

Use the scoring system below to identify whether your organisation should focus on foundational improvements, limited-scope experiments, or broader initiatives:

Sustainability check: As you assess your systems and data, it’s also worth asking: are you aware of the energy use behind your digital tools? They all have a footprint and understanding this early on helps you make choices that balance performance with impact.

Once you have given yourself a score for each of the questions above, total your score and assess against:

• 0-10: Focus on foundational data practices before AI implementation
• 11-17: Ready for targeted, limited-scope AI solutions
• 18-25: Well-positioned for broader AI initiatives

A low score on this assessment does not mean failure. It may reflect that your priorities lie elsewhere, or that conditions are not yet right. If that’s the case, the most valuable next step may not involve AI at all. Instead, focus on strengthening data quality, simplifying systems, or resolving operational bottlenecks. These improvements are often overlooked but will create far better conditions for AI adoption if and when the time is right.

Exercise 2: Identifying Specific Business Problems for AI

Broad goals like “improve efficiency” or “reduce admin” are too vague to guide AI adoption. This step requires identifying 1 to 3 concrete problems that meet the following criteria.

• The problem is clearly defined and well understood
• Relevant data exists or can be collected without major disruption
• Solving the problem would create tangible business value

Use the table below to provoke thinking across functions:

Document your top three priorities. If you list more, treat them as backlog candidates, not active workstreams.

Not every efficiency gain is worth pursuing. Before selecting a use case, consider whether solving it with AI is more effective than solving it with a simpler process change. AI should not be the default solution.

Sustainability check: Looking for a double win? Try identifying areas where AI can reduce both cost and environmental impact, whether that’s cutting unnecessary print, reducing redundant processing, or automating low-value, high-energy tasks.

Exercise 3: Setting Measurable Objectives

For each identified use case, define success in concrete terms. Abstract benefits like “streamline operations” are not sufficient.

Apply the SMART framework to define your objectives:

• Specific: Clearly define what improvement looks like
• Measurable: Include quantifiable metrics
• Achievable: Realistic given your current capabilities
• Relevant: Aligned with broader business goals
• Time-bound: Include timeline expectations

Example objectives:

• Reduce customer response time by 30% within 6 months
• Decrease document processing errors by 25% during the next quarter
• Improve forecast accuracy by 15% over the next year
• Reduce manual document handling and associated printing by 50% over the next 3 months

Be honest about whether the metric is a proxy for impact or impact itself. For example, reducing “response time” is only valuable if it correlates with customer satisfaction or cost reduction. Otherwise, it is a vanity measurement.

Summary and Next Steps

The three exercises above are designed to move your organisation from general interest to focused intent. If your score in Exercise 1 is low, do not skip ahead. Foundational gaps in data or systems will undermine even the best pilot.

In our next post, we will address those foundations directly, including quick wins for better data practices and practical steps for improving system readiness, without large-scale change programmes.

If you would like support with a realistic and practical approach to AI in your business. Our Digital Product and AI service.