Every new process, tool and approval layer carries a cost that rarely appears on a balance sheet.
Nearly 58% of professionals spend at least three hours weekly on admin, while over half find this side of work frustrating. Teams are busy and too much effort is being absorbed by the machinery around the work. Complexity can slow decision-making and make it more fragile. Speed switches off our quality critical thinking and pushes us to simply make a decision to stop the stress.
Chief Operating Officer at Pipedrive.
AI has made execution faster but has not necessarily meaningfully improved decision quality across the board, let alone consistently. If businesses are to make the most gains from their tech choices then they need to thoroughly reevaluate their processes and training. Basic AI use, particularly gen-AI, is not going to meaningfully grow the business versus those who take more care in tailor.
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But… easier said than done. This is cognitively hard and so quite easy to neglect without even noticing. All the invisible, cultural ‘soft’ aspects of work aren’t always easily tracked and evaluated and may just be tick-boxed away if and when brought to mind. Keeping process improvement a focused aim and ensuring it evolves alongside new technologies like AI, and essential human requirements (think fulfilment, agency, flow) is not easy.
Getting it right will separate out high performers who never miss a trick from the average thinkers who scrape by.
Making big changes starts with small steps
“A journey of a thousand miles begins with a single step” is ancient wisdom echoed across many philosophies, cognitive behavioral therapy, and practical guides to business. And yet we need to be reminded of such basic principles again and again.
I’d recommend carefully cycling through the following on a regular cadence, looking at them with various departmental lenses and priorities that align with your industry, policies, customer contracts, and tech stacks. Small improvements done regularly stop drift away from best practices and organizational goals. For example:
Reducing admin. An absolute no-brainer to start with. No one likes admin, but it’s how we prove compliance and quality is taken seriously, though it’s not generally a value-add. Look at workflows as a process to be revisited at regular intervals and see what can be streamlined – while simultaneously ensuring the purpose and context of the process isn’t entirely hidden from those responsible for seeing the workflow through to completion.
Focus priorities. Do less, better, is a mantra for life. Many enterprises grow their offerings over time, but smart execs stay focused. AI tools should be used with that mindset such that all involved are uplifted to perform with excellence. There’s a place for doing everything across the board ‘X’ per cent better, but doing core delivery better by a factor is how a business delivers outperformance.
Consolidate information and reduce tool switching. This is the real bread and butter work of many a consultancy. A COO is well placed to work with the CIO and ensure that any changing data use is done smartly, without shadow AI or ad hoc workarounds that silo intelligence and ultimately make the business’ collective efforts harder for the benefit of one person or team.
Treat AI as an assistant, not a replacement. There’s definitely a cult of AI with highly inflated expectations delivering a lot of hype. AI is likely to become what they hope in time, but right now its use should be applied critically. Costs and performance are variable. Smart users are the other side of the AI ‘coin’.
With the right training in model use, prompt engineering, custom GPTs and skills, your people are given wings. Always remember though that anything can potentially happen: power cut, an API failure, a dependency removal. So if people can’t do their job without AI, then they are not in control.
Mounting hidden costs
These are the basics that many leaders build trust through clear guidance, establishing clear policies, providing regular training, sharing successful use cases, and defining where AI can add real value. But it’s rare for leaders to revisit the steps and go through a forensic process to ensure everything is still operating as intended as any part of the complex interplay changes.
With AI layered on top of data repositories, and IT infrastructure on or off premise in various types of cloud offering, businesses are dealing with a complexity that CIOs would not have dreamed of just five years ago. Staying on top of dependencies, sprawl, shadow IT, evolving models, agentic drift, and token costs, is all part and parcel of controlling costs, improving service and enhancing employee experience.
Costs of course encompass more than financial outgoings and risks. Particularly with AI-enhanced processes, the costs of time mismanagement or opportunity costs become more relevant. These pressures will encourage leaders to both research and experiment more with operations, increasingly pushing them to think more like consultants within their own businesses.
Build human intelligence too
Increasingly, I think of training as the biggest factor that will impact how AI ROI and optimal business outcomes are achieved. IT skills, systems thinking skills, the engineering mindset, and the whole spectrum of soft skills that holds together a complex organization. These are what will enhance domain expertise and allow teammates to live with the complexity of the modern business and not be fazed when it changes or aspects fail.
Employees more than ever need trained critical thinking skills to validate, evaluate and properly act on AI outputs. Producing pages and blindly trusting AI output is not going to end well, with AI slop positively counterproductive.
But underlying it all, it’s that awareness or mindfulness that will keep leaders checking and tweaking their processes to keep catching every scrap of breeze that the winds of tech and the economy send their way. AI is likely to punish the complacent as it propels the mindful to success in operational delivery.
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