Executives under pressure from AI hype

Jay Tikam: 00:00:01

You are an executive, and right now you're feeling the weight of the AI

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revolution pressing down from every side.

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Competitors seem miles ahead.

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Consultants constantly warn

you're falling behind your board.

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Anxiously wonders if you're doing enough

and your employees whisper nervously

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about losing the jobs to algorithms.

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It's relentless.

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I'm sure it's exhausting

and frankly overwhelming.

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I get it.

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So let's stop for a second.

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How are you really feeling

about all of this AI hype?

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What's running through your mind

when you're lying awake at night?

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Are you cautiously optimistic, quietly

anxious, or just plain confused about

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where to begin in this two part series?

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Just like episode three and four,

I am stepping into your shoes.

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Today we'll explore exactly what's

on your mind as an executive facing

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ai, your hopes, your fears, your

frustrations, and lay them bare.

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No jargon, no judgment, just empathy,

clarity, and real world understanding.

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And in the next episode, I

promise clarity and confidence.

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I'll provide a clear step-by-step

blueprint to confidently embrace AI

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without falling into the dreaded 70%.

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Of digital transformation failures.

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Stick with me.

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In the next two episodes, I will turn

the madness of AI into manageable,

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actionable steps, bringing not only

method, but genuine excitement,

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performance, and impact to your journey.

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I am your host, Jay Tikam.

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Welcome to episode six of Impact with

Digital, where we explored digital

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strategies from a human perspective.

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Making sense out of the noise, finding

a way to drive performance and impact.

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Making sure you are on the right track

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in this part, one of the two part series.

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I'll get into your mind trying

to understand the dilemma that

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executives face when confronted

with AI thinking they need to do

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something, not knowing where to start.

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No matter how big your business is or how

small it is, no matter what sector you

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are in, we all face the same pressures.

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I'll try my best to understand your

dilemma, and in part two, we'll

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provide a step-by-step approach to

dealing with AI in the most optimal

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way, ensuring that you get the best

return on your investment, and that

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the AI actually works in practice.

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So let's get started.

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I'm sure you're constantly

surrounded by AI noises.

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Some make perfect sense

while others seem like noise.

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Some messages raise important issues

while others make you concerned.

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Let's explore some of the AI messages

coming at you from all directions.

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On a daily basis from different

types of stakeholders.

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Of course, I use AI to

generate these voices.

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So your board is getting concerned

because , it seems like your

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company just isn't doing enough.

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This puts more pressure on you.

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You feel compelled to

start something, anything.

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The other board members and

I are increasingly concerned

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about our AI strategy.

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We are reading about these advancements

daily, and frankly, we need to see

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a clear plan for how we're going

to leverage AI to drive innovation

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and future-proof the business.

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Are we truly keeping pace?

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You read articles on how AI can

make your business more competitive.

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You wonder whether you're missing out.

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You wonder whether your slow response

may see competitors getting ahead of you.

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In the crazy world of ai.

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You're bombarded by consultants and

software vendors urging you to act

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and spend money on their solutions.

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They put the fear of God into you hinting

that if you don't do anything, you're just

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going to be left behind maybe becoming

the next Kodak in the camera market.

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Or Blockbuster in the Netflix market.

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The window of opportunity

with AI is rapidly closing.

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If you are not aggressively

implementing solutions now, you

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risk being fundamentally outcompeted

within the next 18 to 24 months.

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Laggards will simply become irrelevant.

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You're bombarded with executive level

courses from prestigious institutions.

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You wonder whether you

should take a course.

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Just to make sense of this AI stuff

or look competent in front of your

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board and staff, you wonder whether

you should be arranging training for

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your board and maybe also all staff.

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Maybe if they have the skills.

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They could be coming up with ideas

on AI rather than consultants.

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Or maybe you want your employees

to take the Batten and learn about

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AI so that they can implement

solutions in their functional areas.

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Staff are disillusioned with

yet another digital change

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program following the latest fad.

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They fear job losses or at

least disruption to adopt yet

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another new shiny tech solution.

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I.

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We've been promised all sorts

of efficiency gains with new

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technology before, and it

rarely pans out as advertised.

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This AI stuff just feels like another

buzzword that will create more work

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for us in the long run because we'll

have to fix buggy systems yet again.

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New regulation on AI emerging regularly.

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They promised to put guardrails on

something that has huge potential

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for good, but can also cause harm.

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They also put restrictions

on what you can and can't do.

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You feel compelled to follow

regulation because after all, you

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don't want to end up taking your

firm on the wrong side of the law.

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New AI principles, standards, and best

practices emerge daily, and you and

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your organization need to find a way

to stay on top of these standards.

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You see so many cases of

AI failures emerging in the

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press and in research papers.

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This can really be scary,

especially when you're just on the

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path to doing something with ai.

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So there's a lot of noise

out there in the AI world.

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There's a lot of hype as well.

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And you're not imagining

this noise, it's real.

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The pressure is mounting from within

and from outside on you as an executive,

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everyone is shouting all around you.

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What are you meant to do?

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How do you feel?

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With so much noise going on around you,

some of this could be good, some just

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hype, some serving personal interests

rather than serving your interests.

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As an executive owner of the business

or a founder, you have a public face.

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You have to show your shareholders,

investors, the market and staff that

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you have everything under control,

that you're on top of AI opportunities.

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You need to look like you are in control

and at the forefront of the AI wave.

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However, in the quiet of your office

or in the dead of the night when

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you can't sleep 'cause of all these

worries, how do you really feel?

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What are you thinking?

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What should you do next?

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Should you get internal

advice from your IT team?

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Should you get independent

external advice?

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Well, advisors just try and sell

you something you just don't need.

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Should you raise it at the board meeting?

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And seek the board's

advice on what to do next.

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Should you organize a planning

session with your board?

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If you are the owner of a small

business, should you consult your

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peers, ask a similar business like

yours, what they are doing, should

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you raise it at the next networking

meeting of your industry association?

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Should you just launch that pilot, you've

contemplated or implement the system.

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The vendors have been

forcing you to trial.

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You know you need to do something.

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Your board management and staff are

looking to you to take the lead.

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Ensuring your company is not

being left behind in the AI race.

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You're expected to act

fast, deploy something, show

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results, but you're worried.

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You're unsure.

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In the previous episode, I've already

highlighted evidence by a reputable

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research, which shows that more than

70% of digital transformations fail.

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Is AI transformation going to be any

different if you launch any initiative?

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What if it fails?

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Honestly, I don't wanna be the bearer of

bad news, but we have got to face reality.

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Let me get some facts on whether AI

initiatives are succeeding or failure.

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What are the lessons we

learned from failure of others?

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If you wanna make sure you don't

end up making the same mistakes you

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wanna be forearmed and forewarned.

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Now, research highlights, very

high failure rates for ai, just as

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they do for digital transformation.

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Gartner's research highlight

that 85% of AI projects fail.

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Many other sources report

between 70 and 80% failure rates.

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Such high failure rates will give

you cause for concern and rightly so.

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What if you have to abandon the

AI program a few months into it?

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It's going to waste money, but it's also

going to take up time and the attention

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of management and employees that could

have been better used on other strategic

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projects that drive the company forward.

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If it fails, your reputation is

on the line, and maybe your CEO or

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executive job may also be at risk.

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We talk about high failure

rates of AI programs and also

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of digital transformation.

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But what does this

actually mean in practice?

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So let's try to define what failure means.

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Does it mean a complete loss?

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Is there still some benefit

of AI initiatives, in which

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case it's worth pursuing?

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Let's dive into what research defines

as failure, and more importantly,

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how to spot the signs of failure.

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So let's look at six different

definitions of failure ranging

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on a scale from complete loss to

systems we are deployed, but they

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don't really produce the benefits.

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So number one, we have complete

abandonment of the AI project.

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In this case, the AI project is halted.

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Resources invested are written off, and

the intended solution is never deployed.

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If it's deployed, it's decommissioned.

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And the telltale signs of this kind

of a failure is project termination,

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reassignment of team members,

and no deployment to production.

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So the second type of failure is a failure

to deliver expected business value.

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Now, in this case, the AI solution

is functional and is deployed, but

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it doesn't generate the anticipated

returns, improve efficiencies, or

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increase revenue, or it doesn't achieve

the strategic goal it was intended to.

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The telltale signs of such a

failure is really low user adoption.

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Minimal impact on key business metrics,

or the bottom line high maintenance

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costs, which outweigh the benefits.

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So a third example of failure could be

technical failure, poor performance.

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Again, in this case, the AI

solution is actually deployed.

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But it faces some technical issues

such as inaccurate predictions, biases,

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instability, and integration issues.

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And you can often tell these with,

telltale signs or indicators, such

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as high error rates, inconsistent

outputs, inability to handle

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real world data variability,

security vulnerabilities, and user

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dissatisfaction due to poor performance.

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At number four, we have a pilot

purgatory and failure to scale.

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What does pilot purgatory

mean in this case?

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It it means that the pilot actually

shows promise in a controlled environment

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or in a proof of concept environment,

but it fails to be successfully

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deployed in business as usual.

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You can tell, or this kind of a failure,

when you find that the projects, uh, or

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the pilot is prolonged for a long, long

period of time, there's a difficulty

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in integrating with existing systems.

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The organization is just not ready

for widespread adoption of this

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AI system, and there's no business

value beyond this test case.

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So in the fifth case, and this is a

significant one, failure can mean ethical

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failure and negative consequences.

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So the system, again, is deployed

into business as usual, but it starts

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generating unintended consequences such

as discrimination, privacy violation,

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lack of transparency, and it erodes trust

or actually invites regulatory scrutiny.

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The telltale signs of such a

failure is really biased, outputs,

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regulatory, non-compliance, damage,

or loss of reputation and trust.

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Maybe because customers start to complain.

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And lastly, failure could mean a

failure to achieve user adoption.

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So in this case, the system is

developed, implemented, and actually

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proves valuable to the organization.

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However, users don't adopt such

a system because of resistance.

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Maybe they lack trust.

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Maybe they have insufficient training

and or poor user experience, and you can

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tell this kind of a failure often by low

usage rates, negative feedback from users.

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There's also workarounds being

developed by employees or customers

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to avoid using such a system.

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Lack of integration in daily

life is a clear sign that you're

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facing this kind of a failure.

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These are crucial signs that you have

to watch out for and hopefully this

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gives you some framework to monitor

whether your AI project when deployed

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is going down the path to failure.

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And at least once you understand the

telltale signs, you can do something

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about it to make sure that you bring

things back on track before wasting a

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huge amount of cost, time, and effort.

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So let's explore some real world

case studies of AI going wrong,

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and what are the consequences when

AI solutions actually go wrong.

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It's always great idea to keep track of

AI initiatives failing and succeeding.

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They provide valuable learning lessons.

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An interesting case study popped

up in research for this podcast.

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It involved Air Canada.

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They developed an AI chatbot to

help customers improve customer

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experience and reduce reliance on

staff, maybe also to reduce costs.

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However, in February, 2024, it ended

up costing the company money, its

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reputation and time in fighting

a small claims court case brought

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about by one of its customers.

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The chatbot gave the

customer wrong information.

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About the bereavement fair policy.

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So in this case, a customer, Jake Moffitt,

visited Air Canada's website to book

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a flight from Vancouver to Toronto.

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He needed to travel urgently to

attend his grandmother's funeral.

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He suspected that the airline

provided discounted rates for

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travel related to bereavement.

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Not sure he opened the chatbot.

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To ask if a discounted rate applied.

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The chatbot presented the following text

message, which I will read out to you.

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Air Canada offers reduced bereavement

fares if you need to travel

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because of an imminent death or

a death in your immediate family.

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If you need to travel immediately

or have already traveled and

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would like to submit your ticket

for a reduced bereavement rate.

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Kindly do so within 90 days of the date

your ticket was issued by completing

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our ticket refund application form.

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So just to be sure, Jake took

screenshots of the full AI chat bot

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conversation and he proceeded to apply

for a refund after booking the flight.

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Jake was surprised when his

claim for a refund was rejected.

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Not once, but multiple times.

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Air Canada's actual policy was

not to issue refunds after the

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ticket had already been booked.

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Remember, Jake applied for a

refund after booking the ticket.

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Air Canada maintained that the

chatbot had included a link to the

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actual policy and that the customer

should have read the policy document

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instead of relying on the chatbot.

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Naturally, Jake wasn't happy

and he launched a civil claim

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in the small claims court.

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The chatbot clearly did not

follow the company's policy and

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provided incorrect information.

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In their defense, air Canada said

that they cannot be held liable

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for the information provided

by one of its agent servants or

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representatives, including a chatbot.

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However, in the court, air Canada

lost, they were ordered to pay

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a partial refund and damages.

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A small financial cost.

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However, a heavy hit on their

reputation for poor customer service.

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While the financial loss was small, it

highlights the fear that the company,

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its staff and management, can sometimes

have little to no control over what

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AI produces, and they have to bear the

consequences of large scale errors.

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So let's go to a second example of Amazon.

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Amazon was desperately

looking for developers.

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They developed an AI recruitment

tool, which was later ditched.

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So the recruitment engine was meant to

go out and search profiles on social

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media, as well as accumulate cvs.

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Go through those cvs.

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Against the rules.

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They were expected to come up with

five of the top candidates out of

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every, for example, a hundred cvs.

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Because of the way the AI was trained

on historical data, they created biases

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against women, and therefore, they

were not referring or highlighting

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developers that were female.

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Amazon naturally shut this project down.

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With wasted time, cost, and effort.

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So let's look at the last

case study of Wells Fargo.

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They created an automated calculator

to understand whether clients facing

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foreclosure could afford the repayments.

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After the loan was modified, for

example, the term of the loan was, uh,

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increased, extended, or the interest

rates dropped just to help out the

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customers, and obviously they had to

factor in the attorney costs, which also

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they needed to claim from their clients.

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As a result of this miscalculation around

400 customers, were denied mortgage

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modifications even if they qualified,

and sadly, many lost their homes.

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why do these AI initiatives fail?

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I.

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In the past episode, I covered the reasons

for digital transformation failures.

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Let's explore the research to learn some

common reasons for AI initiatives failing.

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I'll touch on what research

highlights, and there are many

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research papers out there.

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This topic, which I urge you to look at.

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These are just some of the examples, and

I'm sure you will find many other reasons

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which I don't cover in this episode.

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So, firstly, as I already highlighted

before, the AI project could

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completely be on the wrong path.

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It could be misaligned

with business objectives.

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The AI could also be technology led.

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A solution looking for a

problem lacking clear goals.

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When the uh AI system is adopted, perhaps

the executives or the organization

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gets caught up in the hype and the

slick sales by vendors and consultants.

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Data issues, poor quality of data

or lack of data is also a cause

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for ai, uh, initiatives failing.

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Insufficient expertise is one

of the common reasons for AI

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projects failing or halting.

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There's a lot of demand for AI skills and

there's a lack of supply in the market.

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You also need to train up employees,

for example, with specialized

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knowledge, which takes time.

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Um, and that slows down the, uh, adoption

and deployment of the AI solution.

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Inadequate infrastructure.

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AI is very hungry for computer power,

and if your systems can't cope, you need

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to upgrade that which takes you time.

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Alternatively, the system that

you implement just doesn't work.

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: 00:23:34

Um, there are also some issues integrating

with legacy systems for example.

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: 00:23:41

Oftentimes unrealistic expectations

is the cause for ai project failures,

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: 00:23:49

getting caught up in the hype and

expecting AI to solve all problems

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: 00:23:54

and when it doesn't actually deliver

what you intended to deliver.

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: 00:23:59

This can be partial failure,

or executives may decide to can

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the whole project altogether.

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Change management is something I've

covered in the past, and whilst

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the system could be all flashy, all

singing, all dancing, if employees,

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uh, customers, staff, partners,

don't use it, is then pretty useless.

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And finally, AI is not the

panacea solution to everything.

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In some cases, a simple system

could solve the problem.

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: 00:24:33

And executives get caught up again in

the hype and develop an AI solution.

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: 00:24:40

It's an overkill.

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: 00:24:41

Um, and executives find out later on

that they could have just implemented a

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: 00:24:48

potentially much cheaper solution, which

may, they may well do so in future because

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it's simpler to implement, simpler to

adopt, and simpler for users to, use.

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: 00:25:05

So we've heard the noise, we've seen

the failures, we've tried to understand

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: 00:25:09

the causes of failures, and really

you are bombarded by the noise driving

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: 00:25:14

you to do something related to AI

or risks staying behind forever.

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: 00:25:19

You see the groundbreaking opportunities

of AI to shift your business.

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: 00:25:23

However, you can't ignore the risks,

challenges, and most importantly,

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: 00:25:28

the very high failure rates.

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: 00:25:30

It's okay to feel overwhelmed,

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: 00:25:38

so what are you thinking right now?

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: 00:25:40

What questions come to mind?

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In the quiet of your office,

in the dead of the night.

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: 00:25:46

When worries of AI keep you awake.

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: 00:25:49

Many questions come to mind.

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: 00:25:51

Let's dive into the mind of the executive

CEO, founder or owner of the business.

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Let's listen to some of the self-talk

expressing fears, uncertainties,

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: 00:26:06

and confusion of what to do next.

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: 00:26:11

What if I'm the only one holding

back and I miss the one opportunity

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: 00:26:16

that could have changed everything.

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: 00:26:20

We've been burned before by

big promises and buzzwords.

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: 00:26:25

How is this any different?

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: 00:26:29

I tell my board and my team we're

exploring ai, but the truth is I don't

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: 00:26:36

really know what that even means.

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: 00:26:42

If this fails, what do

I really stand to lose?

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: 00:26:47

Not just money, but the trust of my

team, the confidence of my board, maybe

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: 00:26:55

even my own belief in what I'm doing.

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: 00:27:02

So you've heard the noise, you've seen

the headlines, you've felt the pressure,

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: 00:27:07

and maybe just maybe you ask yourself

some of those same late night questions.

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: 00:27:13

The real risk isn't just in doing

AI wrong, it's in doing it for the

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: 00:27:18

wrong reasons, without clarity,

without alignment, without purpose.

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: 00:27:24

That's what part two of the series is all

about because AI isn't the starting point.

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: 00:27:29

Your problem is.

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: 00:27:31

In the next episode, we'll

show you how to cut through the

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: 00:27:34

noise and begin with clarity.

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: 00:27:36

How do you find the right

problems worth solving?

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: 00:27:39

And how do you know if

AI is really the answer?

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: 00:27:43

Step by step?

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: 00:27:43

No jargon, no hype.

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: 00:27:45

It's time to replace anxiety with

alignment, panic with purpose,

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: 00:27:49

and confusion with clarity.

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: 00:27:52

See you in part two.

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: 00:27:54

This is your host, Jay Tikam, signing off.