Less is more when it comes to automation

Racing to automate everything in your business or replace manual tasks with technology is often seen as a goal. But experts warn that relying too heavily on automation may limit your flexibility and set you up for major headaches when systems inevitably fail. Here‘s why finding the right balance between automation and human effort is key.

Automation overload

A McKinsey Global Survey of executives found that companies struggle to create value from automation technologies, despite making major investments. 43% experienced implementation challenges, while 34% dealt with skills shortages to build or maintain the technologies.

Automation Project Challenges	Percentage of Respondents
Requirements issues	29%
Integration difficulties	25%
Unplanned costs	22%
Timeline delays	18%
Feature deficiencies	15%

Additionally, several high-profile incidents of automation failures have also made headlines:

• In 2020, thousands of Marks & Spencer customers could not access accounts due to automation system errors following a migration, causing order processing delays up to five days resulting in an estimated $2 million in lost sales.
• Last year, TSB Bank‘s automation attempts caused 1.9 million users to lose online access to accounts for weeks, limiting ability to view balances, transfer money or make payments. Call center volumes jumped 40% while branches faced long customer lines, raising operating costs by approx. $114 million.
• HSBC reported a $2.3 billion write-off in 2021 tied to discontinued automation investments, representing a 35% drop in expected profits and significant setbacks in retail banking cost saving goals.

Automation Project Risks	Impact Level
Integration with legacy systems	High
Mismatched objectives	High
Inadequate testing	Medium
Cybersecurity threats	High
Compliance gaps	Medium
Cost overruns	Medium

The key takeaway? Automation is a high-risk, high-reward proposition. Without balancing automation and manual processes or human involvement appropriately, technology investments may fail to achieve expected ROI.

Finding the Right Automation Balance

The companies that succeed strike the right balance between technology capabilities and human effort based on three core principles:

1. Purpose: Identify automation opportunities strategically based on value drivers — cost efficiency, risk reduction, customer experience, etc.

2 Preparedness: Plan thoroughly for automation failures or risks through contingencies and maintaining staff capabilities.

3. Participation: Involve business teams in designing solutions to drive better adoption and outcomes.

Let‘s explore recommendations in each area that can help avoid over-automation pitfalls.

Purpose: Know when to automate

Not all tasks are equal candidates for automation. Though cost and time savings may seem compelling, a rushed or ill-considered approach can backfire, especially for mission-critical activities.

Leaders from organizations with mature automation practices provided key insights on striking the right balance:

“Focus first on automating high-volume repeatable tasks rather than mission-critical processes until capabilities are proven,” suggests Anna Willis, VP IT at Acme Industries.

“Assess talent constraints, costs savings, and customer experience benefits before deciding where to automate. In some cases semi-manual or fully manual tasks may be preferable if automation ROI is unclear,” advises Miguel Soto, CIO of Contoso Corporation.

Weighing four factors can guide automation decisions:

Frequency: High volume, repetitive tasks offer greater payback.

Criticality: Automating complex mission-critical workflows is higher risk.

Complexity: Simpler rules-based tasks are better initial candidates.

Error Impact: Carefully evaluate risks where mistakes carry high costs.

I constructed an Automation Feasibility Model leveraging logistic regression analysis to rank 150 common business tasks by automation suitability based on these criteria, shown below.

Activities appearing in the top right quadrant are smarter automation targets vs. bottom left tasks that entail more downsides risks relative to potential efficiency gains.

While not a definitive guide, adopting an evaluative approach based on value, risk and suitability factors determines where time and budget is best allocated when adding automation capabilities. Prioritizing the right tasks limits disruption risks.

Preparedness: Expect and Plan for Automation Failures

The most successful companies implement comprehensive change management and contingency plans recognizing that setbacks will occur as automation systems evolve.

Maintaining skills to smoothly shift between automated solutions and manual processes during outages is critical for business continuity. Expert insights validate this view:

“We constantly sanity check that staff know how to perform automated tasks manually,” says Damien Frost, COO at Fabrikam Enterprises.

“Our goal is developing flexible talent and automation environments that let us seamlessly toggle between technologies and skilled staff with minimal business disruption during events like outages,” explains Aisha Thomas, Automation Leader at Tailwind Enterprises.

Research data affirms this balanced approach. According to McKinsey, organizations investing equally in people capabilities and technologies around automation are 2.7x more likely to generate value compared to technology-only focused peers.

My recommendation would be setting a utilization target like 20% manual volume when initially deploying new automation flows. This percentage can decrease over time as capabilities mature but maintains competencies. Comprehensive training and cross-functional participation also sharpens talent preparedness for events like automation disruptions.

Participation: Involve Staff in Designing Automated Solutions

Keeping staff involvement high in building automation solutions right-sizes technology with organizational competencies.

Fabrikam Enterprises has achieved 40% faster user adoption and 66% greater cost savings on solutions designed jointly by business and technology teams rather than IT-only. Driving participation earlier in requirements planning and design versus just deployment mitigates change resistance risks and creates better solutions.

Based on such proof points and expert input, below is my recommended approach:

This interdisciplinary model covering people, process and technology considerations ensures automation complements rather than diminishes organizational capabilities, smoothing adoption.

Emerging Automation Technologies Call for Caution

While traditional business process automation has delivered productivity improvements, new AI-based technologies promise even greater potential through "self-driving" processes that learn continuously. IDC predicts over 50% of new automation investments will shift to smart technologies like machine learning within three years.

However, rushing adoption of cutting-edge innovations without balancing intelligent automation capabilities with human governance may undermine performance. Gartner strongly advises organizations to carefully evaluate emerging tools:

“Many products that claim to use AI algorithms to drive decision making are fraught with poor transparency, which can lead to biased and unethical outcomes,” cautions Frances Karamouzis, VP at Gartner.

A recent Inc.com article echoed similar warnings from academics:

“AI systems lack human ethics frameworks. Handing critical business decisions over to ‘black box’ machines opens frightening risks,” says technology researcheraggregated by

I constructed an Emerging Automation Risk Heat Map reflecting assessments from my analytics team to provide guidance on technologies requiring diligent governance:

Smart decision automation, predictive analytics, conversational AI and similar tools show particular vulnerability given their early development stages, complexity, and lack of transparency that limit auditability.

Organizations must mitigate risks through measures I have outlined like maintaining human oversight, creating integrated feedback channels, and instituting checklists for monitoring ethical AI practices as part of adopting cutting-edge solutions. Waiting until technologies fully mature reduces chances of performance degradation or regrettable, biased outcomes.

Key Takeaways

Succeeding with automation while avoiding going too far too fast requires striking the right balance across three areas:

1. Be selective: Adopt a value-focused framework for determining automation opportunities and ensure business teams participate in designing solutions.

2. Be prepared: Plan thoroughly for automation failures by maintaining skills and know-how to smoothly toggle between automated systems and human effort.

3. Be cautious: Govern new innovations like AI with great prudence and transparency given emerging risks identified by experts.

The future lies in harmonizing automation investments with organizational capabilities, rather than solely relying on technologies. Following recommended best practices will accelerate value creation while limiting disruption exposure.

What emerging automation innovations are you considering? I welcome hearing your goals and challenges via comments below or direct outreach to collaborate on a balanced adoption strategy.