Why Companies are Really Failing at AI

TL;DR: Despite $30–40 billion in enterprise AI spend, 95% of projects deliver no ROI. The reason is not model quality or regulation but failed approaches: systems that don’t learn, pilots that don’t integrate, budgets that chase optics, and builds that never scale. The only proven path out of the GenAI Divide is linking individual value with organisational ROI through training, governance, and adoption frameworks that raise competence, autonomy, and trust.

AI is not the first General Purpose Technology to underwhelm in its early years. Computers and the internet both looked promising but delivered little productivity until workflows were re-engineered around them. Economists call this the Productivity J-curve: a period where costs rise, returns flatten, and intangible investments distort the statistics. Shoutout to my Marcro-economics lecturer for that one!

Generative AI is now following that curve. Enterprises have invested tens of billions. Tools like ChatGPT and Copilot are widely used. Yet 95% of firms report no measurable return. The result is the GenAI Divide: a minority of companies extract millions, while the majority stall in pilots, fail to scale, and see no impact on the P&L. To be fair...a lot of companies are still undergoing "Digital Transformations: - another peach sold by larger consutling firms to no avail.

The divide is not explained by model quality or regulation.

It is explained by approach.

The Four AI Failure Factors

1. Learning Gap
Enterprise systems don’t learn. They forget context, reset each session, and fail to improve with feedback. Employees sidestep them with personal subscriptions that provide faster gains. Ninety percent of staff use consumer AI informally, while only 40 percent of companies fund official subscriptions. This shadow AI economy exposes the real issue: productivity is shifting to individuals, while enterprises remain blind to the value.

2. Integration Complexity
Most enterprise pilots never make it to production. They don’t align with how work is actually done. Eighty percent of companies test AI tools, but only 5 percent of custom builds survive into workflows. Staff embrace consumer tools because they integrate instantly into daily tasks. Leadership, meanwhile, keeps pushing projects that create extra steps rather than removing them.

3. Build–Buy Dilemma
Internal builds are favoured for control, but they fail at double the rate of vendor solutions. Projects drag, staff turnover wipes knowledge, and budgets evaporate. Vendors that customise by process and tie outcomes to business metrics succeed twice as often. Yet many firms confuse ownership with progress and end up funding initiatives that never clear pilot stage.

4. Investment Misdirection
Budgets flow to visible functions like marketing because they are easy to justify. But the highest returns are in back-office automation: contract review, reporting, finance, and customer support. Firms that reallocate see measurable savings in BPO and agency spend. Those that chase optics end up with demos and dashboards that never touch productivity.

The Productivity Paradox Repeats

Despite tens of billions spent, only 5% of enterprise pilots scale. Seven of nine major industries show no meaningful structural change. Tech and media are the only sectors showing signs of disruption. For the rest, AI adoption looks busy on paper but delivers little transformation in practice.

This is not a temporary quirk. It is a structural failure. Boards are told AI is everywhere, yet the P&L shows nothing. Leaders sign off pilots, watch them stall, and then rationalise it as “early days.” But history is clear: firms that stay trapped in pilot purgatory fall behind those that rewire processes fast (and that is hard, and long).

The Shadow AI Economy
While official initiatives stall, employees are already crossing the divide. Over 90% of workers report using personal AI tools, but only 40% of companies pay for enterprise subscriptions. Staff are delivering ROI quietly and individually — often without IT’s knowledge or approval.

This creates two risks.

First, productivity gains are invisible to leadership and unmeasured by the organisation.

Second, the lack of governance leaves firms exposed to ethical, security, and compliance failures.

The workforce is moving ahead while leadership lags behind, widening the adoption gap inside the enterprise itself.

Blind Spots in Exec Focus

Executives cling to visible investments. Budgets flow to sales dashboards and marketing pilots that show well in board packs. But the evidence is always straight up: the real gains are in back-office automation - workflows - the end. Contracting, compliance, reporting, and some level of customer support generate measurable savings.

But, as usual, most companies overspend on flashy front-end tools while their cost bases remain bloated. Internal builds fail at double the rate of vendor solutions. Integration is treated as optional. Training is treated as an afterthought. Governance is left till someone in legal goes on a webinar about AI guardrails and ISO 42001 compliance.

And so it goes... Adoption is high, disruption is low, and organisations remain trapped in the same paradox that has defined every major General Purpose Tool in its early phase - I count electricity, the car and if I was around...probably fire too.

So, what can you do?

Escaping the GenAI Divide means recognising that enterprise ROI depends on individual value.

Yes...tell the CFO with his CPA...people value.

Research across industries confirms the link: organisations are nearly six times more likely to report significant financial returns when employees personally derive value from AI. That value shows up in three dimensions competence, autonomy, and relationships.

Most firms underestimate this. They talk about “using AI” as if it were binary, like picking up a stapler. In practice, workers use AI in many invisible ways. CRM platforms, analytics suites, and customer tools already embed machine learning, yet staff may not even realise they’re relying on it. What matters is not whether employees know they are “using AI,” but whether they feel it improves their performance and connects them more strongly to their colleagues and customers.

This is why so many official deployments fail. Leaders design systems for business value and ignore whether individuals gain anything from them. Employees then bypass clunky enterprise tools in favour of consumer-grade AI that fits better into daily workflows. The result is measurable productivity that leadership never sees, and official projects that produce nothing but cost.

The numbers are blunt:

• 64% of workers already report personal value from AI, and these workers are 3.4 times more likely to be satisfied in their jobs.
  
• 60% view AI as a coworker, not a threat — proof that adoption can build confidence rather than fear if managed well.
  
• Mandating use triples adoption rates, and even begrudging adoption leads to higher personal value than leaving AI optional.
  
• 85% of organisations that get ROI also report employees getting personal value: showing that enterprise outcomes do not come at the expense of individual benefit.

This evidence points to a simple conclusion: organisations only achieve AI value when their people do. Without individual competence, autonomy, and connection, enterprise ROI remains out of reach.

The way forward is not another round of pilots or one more vendor platform. It is building adoption frameworks that deliberately connect individual benefits to organisational outcomes.

That means:

• Training workers so AI raises their capability and confidence, not their workload.
  
• Embedding governance, explainability, and ISO-aligned standards so adoption stays within risk thresholds.
  
• Recognising hidden AI use across the enterprise and integrating it into official practice rather than ignoring it.

Treating AI as a coworker in design and communication, so employees see it as augmenting their role rather than threatening it.

My view is that AI will not deliver value simply because a company deploys tools. To be honest, almost 90% of the use cases I have seen do not need AI and are pretty much softward automations. 

AI delivers value when employees themselves get value, and when leadership aligns that personal gain with measurable organisational outcomes. Yet, this is so hard to see...maybe beause IT is being handballed the AI strategy..maybe because AI experts have been unearthed by multiple tech integraton failure points in the 2010's, and are the first feet on the street.

My Advice on Closing this Gap

I learned this lesson early. My first role was in a treasury dealing room for a merchant bank. The project was to digitise a deal-making workflow across back, middle, and front office. We built and deployed one of the first online trading systems of its kind in South Africa and globallly. The technology itself was not the breakthrough, it was the change in how people worked around it on implementation. Fluency came first at the individual level, then collaboration linked functions, and only then did the organisation see systemic benefit.

AI adoption follows the same pattern. It starts like a single-player video strategy game (StarCraft anyone?). Each individual learns the controls, tests moves, and builds confidence. At first it is fragmented, inconsistent, and sometimes frustrating. But once fluency spreads, collaboration begins. Teams link their usage, processes adjust, and workflows change shape. At that point, orchestration at the organisational level becomes possible. The productivity J-curve starts to bend upward.

This is why firms stuck on the wrong side of the GenAI divide cannot wait for technology alone to deliver returns. The research is clear: organisations only see measurable ROI when employees themselves derive value.

Workers who feel more competent, more autonomous, and more connected through AI drive adoption that scales. Companies that ignore this repeat the same errors we saw in IT and the internet’s first wave: pilots without outcomes, systems without uptake, and budgets without return (kind of like those million dollar CRM installs today!).

The answer is straightforward:

• Training so individuals build fluency and confidence.
  
• Explainability to ensure adoption stays within risk thresholds.
  
• Integration of personal and shadow use into formal workflows (rather than treating it as a problem to stamp out).
  

This is why I run two entry points for organisations serious about crossing the divide. The AI Fundamentals Masterclass builds literacy across teams, while the 5-Day AI Bootcamp drives practical fluency by embedding use cases directly into daily work. Both are designed to turn individual value into organisational outcomes, the only proven path to measurable ROI.

The J-curve is real, but it is not permanent. Companies that invest in their people, align adoption with governance, and treat AI as both a tool and a coworker will be the ones that turn early disruption into lasting advantage.

FAQ

Q: How long does it realistically take for AI adoption to show measurable ROI?
Most organisations underestimate the J-curve. Expect 12–24 months before early investments translate into measurable productivity gains, provided adoption frameworks are in place.

Q: What role should boards play in ensuring AI adoption succeeds?
Boards need to treat AI as both a technology and a people investment. Oversight should cover training budgets, ISO-aligned governance, and measurement of adoption at the individual level — not just vendor contracts.

Q: If employees are already using AI personally, why should companies formalise?
Shadow use creates risk. Without governance, explainability, and integration into workflows, productivity gains remain invisible and compliance gaps widen. Formal adoption ensures value is captured and controlled.

Q: Should AI use be mandated inside organisations?
Evidence shows mandating use triples adoption rates and still leads to higher individual value than leaving it optional. The key is to preserve agency — staff must be able to override AI recommendations when needed.

Q: Where should organisations prioritise budget if they want early ROI?
Focus on back-office and process automation: contracting, compliance, reporting, and customer support. These areas consistently deliver measurable savings faster than front-line marketing pilots.

Q: How can you measure whether employees are actually deriving value from AI?
Go beyond usage stats. Survey staff for changes in competence, autonomy, and connection in their roles. Organisations where employees report personal value are nearly six times more likely to show ROI.

Reference:
The Productivity J-Curve: How Intangibles Complement General Purpose Technologies, MIT
AI in Business Report 2025, MIT