Stop chasing AI skills. Start fixing the workflows

As organisations across the globe race to embed AI skills into their workforce, not all skills are actually implemented in the daily workflow. For instance, when employees are enrolled in AI certification programs while the underlying workflows remain disorganised, the impact shows up in abandoned tools, frustrated employees, and AI implementations that solve problems nobody actually had.

The challenge for HR leaders is to recognise exactly where process readiness determines the value of AI skill-building, and why their organisations have launched ambitious upskilling programs without first fixing the workflows those skills are meant to improve. HR teams rolling out company-wide AI training, building prompt engineering capabilities, and tracking adoption metrics without addressing the process chaos underneath are discovering that their learning investments produce negligible productivity gains.

Why AI skills training fails without a workflow foundation?

Although most organisations invest in AI literacy programs, sponsor certifications in automation platforms, and hire consultants to teach prompt engineering, they consistently encounter situations where employees who completed all the training still do not use AI effectively in their actual work. When HR teams investigate this issue, they realise that the problem is that their workflows are so fragmented, their responsibilities so poorly defined, and their processes so dependent on individual effort that there is nowhere for AI capability to attach productively. This pattern reveals a fundamental sequencing error: organisations tried to layer AI skills onto broken workflows rather than fixing the workflows first, so AI could actually make them better.

The problem compounds because organisations measure AI success through adoption metrics rather than outcome improvements. Training completion rates show how many employees went through programs, tool activation numbers show how many have access to AI platforms, and usage dashboards show how many prompts employees are running. None of these metrics reveals whether AI is actually making work better or just adding complexity to already chaotic processes. An employee might be actively using AI daily while being less productive than before because they are now spending time fighting with automation that does not fit their actual workflow, rather than just doing the work manually.

What workflow readiness for AI truly requires?

Organisations often describe wanting to be AI-ready without specifying what their processes need before AI can improve them. Workflow readiness for productive AI adoption means having processes designed with clear handoffs, minimal unnecessary steps, and explicit decision points that AI can either automate or augment without requiring constant human intervention to work around workflow gaps.

• Process clarity: A workflow description that says ‘stakeholders review the proposal’ is useless to AI because it does not specify which stakeholders, what the review criteria are, how long they have to respond, or what happens if they disagree. AI cannot automate or assist a process it cannot understand, and vague workflow descriptions that rely on humans to fill in the gaps do not provide the specificity AI requires. Effective workflow documentation defines every decision point, every approval requirement, every data input, and every output explicitly enough that an employee unfamiliar with the process could execute it correctly.
• Exception handling: Workflows designed by HR teams that assume that everything will run smoothly break immediately when AI encounters situations not covered in the documented process. Effective workflow design anticipates the common deviations, such as approvals delayed, data missing, requirements changed mid-process, and specifies how these exceptions should be handled rather than leaving it to human judgment to figure out. 
• Cycle time definition: When processes have no defined timeline, work gets piled up because nobody knows whether waiting three days for approval is normal or a problem. AI workflow management tools need explicit duration expectations for each step so they can escalate delays appropriately rather than either interrupting processes that are progressing normally or allowing genuine bottlenecks to persist undetected.

Designing workflow fixes that ensure smooth AI implementation 

Layering AI onto broken workflows wastes both the AI investment and the underlying work effort. Smart organisations are fixing workflow dysfunction before deploying AI rather than hoping AI will somehow compensate for process problems.

• Eliminating unnecessary steps: Many workflows accumulate steps over time that made sense when they were added, but no longer serve a purpose. It can be approvals that duplicate other reviews, or data entry that replicates information already captured elsewhere. AI will automate these wasteful steps just as readily as valuable ones unless an employee removes them first.
• Standardising data formats and structures: When candidate information is captured differently in the ATS and the HRIS, AI attempting to work across these systems either fails or produces incorrect outputs. Fixing these data inconsistencies creates the clean inputs AI needs to produce reliable outputs.
• Defining decision criteria explicitly: When approval processes rely entirely on undefined judgment, like proposals getting approved or rejected based on unstated criteria that vary by approver, AI cannot assist because there is nothing to learn from. Making decision criteria explicit means specifying what factors matter, how they should be weighted, and what thresholds apply. This explicitness allows AI to support decisions rather than being unable to engage because the decision process is entirely opaque.

What happens when AI gets deployed onto broken workflows?

Despite knowing that workflow foundation matters, organisations often deploy AI before fixing processes, due to pressure to show AI progress, underestimating workflow dysfunction, or believing AI will somehow fix the processes itself. What happens next determines whether the organisation learns from the failure or compounds the mistake.

• Rapid identification of workflow gaps: When one team deploys AI and discovers their workflow is too undefined for automation to work, treating this as a learning opportunity rather than a failure prevents other teams from making the same mistake. Flagging these workflow gaps quickly, documenting what needs fixing, and sharing those lessons enables other parts of the organisation to address their own workflow problems before attempting AI implementation.
• Willingness to pause AI deployment: When it becomes clear that workflows need fundamental repair before AI can help, organisations face a choice: continue pushing AI adoption to justify the investment already made, or pause, fix the workflows, and then reapply AI to processes that are actually ready. Organisations that choose the latter waste less money and time overall than those that compound bad investments, trying to make AI work on top of processes that cannot support it.
• Documenting successful AI adoption: When organisations track which processes AI improved significantly, and which it complicated, patterns emerge about what workflow characteristics matter. Clear handoffs, explicit decision criteria, standardised data, and defined timelines consistently predict AI success. Ambiguous responsibilities, subjective judgment, inconsistent formats, and undefined processes consistently predict AI failure. Capturing these patterns allows organisations to pre-assess workflow readiness.

Conclusion

Organisations seeing real productivity gains from AI investments are those that fixed their workflows first, that built process clarity and efficiency before layering on automation, and that recognised AI amplifies whatever processes it touches, including the dysfunction.

Effective AI enablement acknowledges that human flexibility has been compensating for terrible workflows that AI cannot navigate, that training people on tools without fixing the processes they will apply those tools to wastes both investments, and that adoption metrics without outcome improvements indicate AI is being used but not creating value. When organisations fix workflows before AI adoption, with process mapping, bottleneck elimination, handoff clarification, and exception handling definition, AI becomes the productivity multiplier it promises rather than expensive complexity layered onto chaos.