Enterprise AI Productivity: Real Business Returns Beyond the Hype

In each and every boardroom, the same discussion is taking place today. The CEO brings up an example of how their competitors have implemented AI and the CFO wonders what benefits this technology has brought to the company in all the experiments. Two years after all the efforts of implementing AI, it has been realized that enthusiasm had outgrown the facts.

The Pilot Purgatory Problem

Go through any big business and you will find many more AI pilots being carried out – chatbots, documents summarizers, coding assistants, etc. Many of them are very technically impressive. Not many of them are economically important. Studies conducted by industries have found out that most AI pilots do not even go into production stage and even if they do, many do not generate any economic benefits for their companies.

The reason is usually not technical but more because pilots are used to demonstrate the feasibility rather than scaling them up. The data used in the pilot is clean, people involved in them are interested, and there is executive oversight which is lost once the demonstration is over. Pilot purgatory refers to conducting continuous pilots which use up resources without showing up any benefit in the balance sheet.

Measuring the Wrong Things

Another common reason is metrics. Many organisations focus on measuring the activity related to their processes e.g. how many prompts, licenses have been activated and how much time has been saved. Whilst saving time can be a very attractive metric, if these aren’t being translated back into delivering outcomes such as more customers served, less staff employed or quicker cycles, then it is mostly an illusion.

An analyst may save 5 hours per week by using AI to draft prompts however if this results in longer emails this does not equate to any value. Improving productivity doesn’t translate to ROI. It must be utilised or generate revenue. Those companies that get it right measure outcomes within their business i.e. cost to resolve support tickets, time to win contracts, and defects per thousand lines of code and compare against what the outcome was before AI.

Where the Money Is Actually Appearing

But despite this grim reality, returns can be achieved in very specific and well-bounded fields. The example demonstrates that value can be extracted in places where the process is volume-based, text-based, and is already measured.

The best example of this is customer support. The ability to deflect mundane questions and aid human workers in providing suggestions for responses has already been shown to provide some benefits in terms of faster resolution and higher throughput.

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Software engineering is the next domain, where generating code, testing it, and modernizing legacy systems speeds up the development process. Various back-office operations like invoices processing, claims sorting, and reviewing regulatory documents are also examples of more straightforward gains.

What all of these use-cases have in common is a clear process, an output, and a person who is responsible for it. This is not rocket science, but gradual improvement of existing processes, which already have some associated costs.

The Hidden Cost Ledger

Being honest with your ROI calculations will require you to look at the costs which aren’t often included in vendor pitches. Inference costs can vary depending on how much you’re using, data wrangling, data integration, security checks and governance will take up much more time than licensing the models themselves.

Change management is often the most overlooked cost, this includes training, process changes and ongoing leadership involvement, which can often exceed the cost of the technology. Add to this the risk aspect. Hallucinations, data leakage, regulatory and reputation risk if it goes wrong in front of people. If this tool saves you millions but creates a compliance disaster then there is no return. Now mature users are including risk in their business cases from the start.

What Separates Winners from Spectators

What these organisations have in common is a clear focus.

– Rather than looking at where AI can be applied, they ask what expensive pain points they have and whether AI provides the best solution.

– They also invest in building a solid data base. As models become increasingly common, having a clean, easy to access and well governed data set means you will have a sustainable edge.

– They also look to optimise processes. The biggest improvements will come from ensuring your processes are optimised so that humans and machines are working together at their best.

– They understand it is a people issue. Building trust with front line staff, providing practical training and providing incentives to make them use it will be key.

– Finally they invest for the long term. The benefits will take time to compound and patience is a competitive advantage.

The Road Ahead

The next stage of enterprise AI will be more serious. As agentic systems start to perform complex tasks, there’s more at stake and greater potential ROI. But also higher costs for bad governance. Boards will want more transparency and finance will want AI investments to go through the same checks and balances as any investment.

The good news is, we’re seeing this hype cycle correct. There’s no failure here, it simply means we are now having a mature conversation. Winners will be those who have measured their progress accurately, invested wisely and understand how valuable AI can be when the right management is in place around the model.

The AI hype is dying down, and it’s a good thing. It means we have a sustainable advantage that can be achieved by those putting in the hard work.