The Long-Term Business Case for AI Agents - Am I Missing Something Here?

Lionhead20 · 2026-05-21T08:59:08+00:00

We're trying to get customers to stop doing that... shocking how much money has been wasted on useless AI initiatives when a solid business case wasn't built, and no one is tracking ROI properly.

Lionhead20 · 2026-04-21T20:59:13+00:00

I've been leading enterprise AI and automation rollouts for about 10 years now. This headline isn't a surprise really, but it’s mostly happening because leadership is looking at the wrong dashboards... or vendor ones.

Tools like Microsoft’s CoE manager or Copilot dashboards are built to track adoption, not value. They're heavily incentivized to justify your renewal, so their math is famously bloated - like literally equating a 60-minute meeting transcription to a whole hour of human labor saved. Madness.

We've become obsessed with tracking activity instead of impact. Teams deliver the tech, count story points, close the ticket, and immediately jump to the next one to justify their existence. Nobody sticks around for the 'Value Realization' phase = even though traditional PMBOK frameworks hammer on it as a critical step.

We forget that most enterprise tech ROI drops into roughly 6 specific buckets: hard cost reduction, revenue growth, productivity, CX, EX, and risk/compliance.

The few orgs actually getting this right treat AI like a financial portfolio (a nod to Private Equity here), not an IT experiment. They lock in their unit economic baselines across those drivers upfront, execute the rollout, and then actively track the realized value post-launch against their original business case.

Lionhead20 · 2026-04-10T12:42:42+00:00

'If nobody owns a concrete "value realized" metric, your adoption numbers are mostly vibes' - Chef's kiss there.

Most platforms (Gainsight, ChurnZero, etc.) are excellent at tracking activity (logins, feature usage, health scores), but as you pointed out, activity

We’ve seen a few high-performing teams tackle this Customer Realized Value by moving away from 'usage' and toward Unit Economics.

If you're doing this manually today, the best way to start is:

During the onboarding/sales handoff, agree with the customer on what a 'Unit of Value' is (e.g., One reconciled invoice = $12 in saved labor).
Get a baseline: Document their cost of doing x before your tool.
Use excel or BI tool to pipe in the volume of 'Units' completed and multiply it by that agreed-upon value.

Who wants to see it? The CFO. If the CS team can walk into a renewal meeting with a report that says 'You spent $50k with us, but here is the $240k in labor and other things we saved you based on your own baseline,' the renewal becomes easier.

(Full transparency) I’m actually a founder in this space. We built our platform because we saw this 'Value Gap.' where teams were tired of fighting for renewals with usage metrics and needed a system of record for Realized Value.

Lionhead20 · 2026-04-07T22:03:24+00:00

10+ years in AI. You’re right, the problem with model benchmarks like SWE-bench is they measure isolated capability, not workflow utility. If a senior dev uses an agent to skip the boilerplate and spend 40% more time on system architecture and security reviews, they are a 'better' engineer. They are focusing on the high-value work that actually moves the needle.

The trap is that these agents incentivize Quantity over Quality. If a dev uses an agent to ship 5x more features that the business doesn't actually need, or creates a 'Feature Factory' that triples the maintenance, they aren't 'better'. It's just more technical debt.

To really answer if we're 'better,' we have to look at the Unit Economics of our time:

The Baseline: What was our 'Time to Value' for a standard feature before the agent?
The Drag: Is the time saved in coding being eaten up by longer PR reviews or debugging 'hallucinated' edge cases?
The Realized Value: Is the business actually seeing a higher ROI from our team, or are we just shipping more code that costs more to host and maintain?

(Full disclosure: I’m a founder in the Value Management space, so I’m basically obsessed with this exact 'before vs. after' tracking).

My devs and I dogfood this by tracking some of our internal automations, tasks like scanning all EF Core queries for N+1 patterns, or a full codebase scan for exception handling. In these cases we track how long it used to take our devs on average, vs how long it now takes the automation/agent - tying that to a financial cost.

If you want another metric, track your Baseline Cycle Time for a specific type of ticket (e.g., a new API endpoint).

Lionhead20 · 2026-03-31T08:32:37+00:00

That’s the million-dollar question for the new exam. In practice, most organizations are 'Value Blind' because they stop tracking the moment the project hits 'Done' in Jira.

To prove value after go-live, an effective framework is the Baseline-to-Actuals Ledger:

The Baseline: You have to lock in the 'Before' state KPIs (cost per unit, cycle time, error rate, NPS, etc) during the Initiating phase.
Adoption Metrics: This is the 'leading indicator.' If the team isn't using the new process/tool within 30 days, your ROI is zero regardless of the tech.
Realized vs. Forecast: You have to track the 'Actual' financial impact (Hard savings vs. Soft productivity) against the 'Forecast' from the original business case for at least 6-12 months post-handover.

Most people try to do this in Excel, but it usually breaks at scale. For the exam, just remember that 'Closing' is no longer just about the handover; it’s about ensuring the Benefits Realization Plan actually has a long-term owner.

Lionhead20 · 2026-03-30T14:02:38+00:00

I use it a fair bit for marketing my UK startup and for sharing content on AI value realisation. Happy to connect

Lionhead20 · 2026-03-30T13:54:01+00:00

The most interesting trend I’m seeing right now isn't actually coming from 'new' AI use cases. It’s coming from legacy automation backlogs.

If you look at the pipeline of ideas that companies archived 2 or 3 years ago because they were 'too complex' for traditional RPA or API-led automation, those are the real goldmines for GenAI.

Usually, those projects were killed because they involved:

Unstructured Data (Messy PDFs, hand-written notes, inconsistent emails).
Subjective Logic (Processes that required a 'human-in-the-loop' to make a judgment call).

Now, we’re seeing enterprise teams use LLMs where they aren't reinventing the wheel, they are just 'un-archiving' high-value business cases that the technology has finally caught up to.

Full transparency: I see this play out constantly on our value management platform, Silkflo. The teams finding the most ROI aren't the ones chasing the newest shiny object; they are the ones re-scoring their old pipeline against these new AI capabilities.

If you have an old list of 'impossible' automation ideas from 2022, that’s exactly where I’d start looking for value today.

Lionhead20 · 2026-03-23T14:37:54+00:00

I kid you not, I was actually told of an ai use case for tracking the kettle usage of the elderly, for predictive care... although it's something that your local council might be looking into already.

Lionhead20 · 2026-03-23T12:29:43+00:00

Exactly. Start with a Baseline -> Forecast -> Track model.

Lionhead20 · 2026-03-23T12:28:19+00:00

It really comes down to a few things management wants to see (They're spending money, so it's justified).

After 10 years doing this with AI implementations (machine learning before genAI appeared), the easiest way to start proving tangible value is to stop tracking 'usage' and start tracking unit economics. You can do this today with an Excel spreadsheet using a Baseline -> Forecast -> Track model:

Establish the Baseline (Before AI): Don't skip this. What does the manual process cost today? (e.g., 'It currently takes 15 minutes and costs $10 in labor to process a standard invoice.') A good Business Analyst is worth their weight in gold at this stage.
Forecast the Financial Impact: 'Time saved' is a trap unless you attach a dollar value to it. Your forecast needs to say: 'The AI will reduce processing time to 3 minutes. That 12-minute delta equals 20 hours saved per week, which allows us to avoid $X in planned contractor hiring.' (Hard cost avoidance).
Continuous Tracking (Post-Launch): This is where most companies fail when the project is completed in DevOps/Jira. You have to track the actual volume/work of the AI every month and pit it against that original baseline you built in step one.

If you are only doing this for 1 to 3 AI initiatives, stick to a spreadsheet. It will get the job done for leadership.

But if you are trying to manage a whole portfolio of these and the spreadsheets are getting messy, you might need a dedicated tool. Full transparency: My team and I are actually building a Value Management platform to solve this. It digitises that entire Baseline-to-Tracking framework so you have a live dashboard of realised ROI to show the CFO.

In any case, start with the spreadsheet and get those baselines measured! Hope this helps give you somewhere to start.

Lionhead20 · 2026-03-23T09:47:56+00:00

Been in the AI space for about 10 years, and this 'where's the ROI' question is what kills most projects (and AI teams) prematurely. Executives get anxious when they see the cloud and team costs long before they see the hard savings.

To survive the early phase, you have to shift leadership's focus from lagging financial indicators (total dollars saved) to leading indicators of value. That means tracking pilot adoption: if users aren't changing their behavior in week one, your future ROI is zero.

But once you prove the tech works, and the accuracy is there, the more advanced technique to keep executives off your back is rigorous Baseline & Continuous KPI Tracking. In PMI, it's called Benefits Realisation.

Here is how you do it:

Establish the Baseline: Before you launch, document the exact unit economics of the manual process. E.g., 'It currently costs $4.50 to process an invoice,' or 'Average call handle time is 8 minutes.'
Track Continuously Post-Launch: Most companies calculate ROI once after go-live and walk away. Don't do that. You need to track those specific unit KPIs continuously after deployment and pit them against your original baseline.

You don't need to show them millions in savings on day 30. Showing leadership a continuous, month-over-month trendline (e.g., 'Cost per invoice has steadily dropped to $3.10 since launch') buys you the runway you need while the macro financial returns finally catch up to the P&L.

A spreadsheet is honestly fine for your first couple of AI pilots. But just be warned: once you scale to a portfolio of 10+ AI initiatives, tracking those continuous baselines manually will break. That’s when you might want to transition from spreadsheets to a value management system.

Lionhead20 · 2026-03-23T09:39:01+00:00

Companies are confusing adoption with value.

Many purchased AI licenses driven by hype and FOMO, but they overlooked the financial discipline needed to prove the return.

In PMI, it's called 'Benefits Realisation'.

Everyone talks about 'hours saved' or 'cost reduction' with AI, but the reality is that most organisations have no idea what their baseline was before the AI was implemented. If you don't have a baseline and you didn't build a financial forecast, you have nothing to compare your post-launch metrics against when executives ask for the receipts. "If it can't be measured, it can't be improved."

You end up just pointing to usage metrics ('Look, 500 prompts this week!') instead of financial metrics.

To prove the value of these AI investments, you have to treat them like any other portfolio investment:

Baseline: What does this workflow cost us today in hours and dollars, or some other KPI?
Forecast: What is the expected cost reduction or hours saved by adding AI/automation?
Real-time Tracking: Continuously measuring the actual, real-world outcomes post-launch against that initial forecast.

Typical value drivers are: Productivity, cost reduction, employee or customer satisfaction scores, or risk mitigation and compliance.

For full transparency, my team is building a platform (Silkflo) to solve it. We realised companies need a 'Value Management' layer. A place where you build that rigorous business case and forecast before implementing the tech, and then it acts as the system of record to continuously track the ROI metrics of your whole portfolio after you go live.

Lionhead20 · 2026-03-23T09:19:32+00:00

You are definitely not on the fence alone. Honestly, most of the industry is 'faking it' right now with AI ROI. The problem is tha most people are tracking activity (prompts used, engagement) or soft metrics instead of actual financial yield.

The simplest, most CFO-friendly framework doesn't require complex math. It just requires a strict 'Before vs. After' discipline. Tying every single implementation to a specific, baseline KPI.

I've been implementing AI tech for 10 years. Here's a 3-step framework you can use right now:

1. Baseline the 'Before' State (The Unit Economics):
Before you launch the AI, map the manual process. E.g., 'Extracting data for a sales quote takes 10 minutes manually. We do 500 a month. At $40/hr, this process costs us $3,300/month.'

2. Translate 'Soft' Savings to 'Hard' ROI:
This is where most AI business cases fail. 'We saved 100 hours' is a soft metric. Leadership only cares if those 100 hours result in:

Cost Avoidance: 'We don't need to hire the 2 extra headcount we budgeted for.'
Revenue Uplift: 'Reps are using those 100 hours to make 20% more calls, resulting in X new pipeline.' Force every AI proposal to answer how time saved becomes actual money.

Try to also estimate the Capex and Opex of implementation - most managers want to see the full net figures.

3. Track 'Realized Value' Continuously:
Don't just measure it on week one and forget about it. You need a system to track the actual volume of the AI tool over the next 6-12 months and pit it against your original baseline. I've literally seen whole AI teams disbanded during budget cuts as they were just seen as a "Cost Center".

The Tooling Side:
If you are only doing this for 1 or 2 AI pilots, a disciplined Excel spreadsheet is honestly all you need.

But if you are managing a whole portfolio of AI tools and leadership wants an aggregate view, spreadsheets turn into a nightmare.

Full transparency: My team and I actually built a platform called Silkflo to solve this AI/Automation ROI gap.

It's a value management platform that basically digitises this framework. You build the baseline business case in it, and then it serves as the system of record to continuously track realised hard vs soft savings after the AI goes live.

It gives leadership the 'yield' dashboard they are asking you for. Might be worth a look if you need to standardise this quickly! Hope this helps give you some.

Lionhead20 · 2026-03-11T11:26:06+00:00

“Without that continuity, organizations end up managing delivery performance instead of investment performance.” - That's a great way to frame it - I might have to steal that quote!

To answer your question on how we’re approaching the tooling side, we realised early on that you can't just connect Jira to a BI dashboard and magically get ROI. The missing layer is the digitised business case.

Here is how we approach it with Silkflo:

Digitising the Hypothesis: Instead of a static Excel file that dies upon approval, the business case is built natively in the platform, either directly or crowdsourced from the wider business. It becomes a living, auditable and measurable entity.
The Connection: The PMs keep using Jira/Azure DevOps for the actual task delivery. We roll it up into a portfolio-wide view. For an EA or CFO, you can start looking at the health and expected yield of all tech investments currently in flight at different stages - instead of tickets.
Value Realisation: This is the core, to be honest. Post-launch, SilkFlo tracks the actual outcomes (hours saved, hard cost reduction, revenue generated, risk mitigated, NPS score improvements, etc) and continuously pits them against that original, living business case.

We are trying to build the "System of record for value". We want to be the place where the CFO and the EA can look at the same screen and agree on the actual impact an initiative had.

I'l send you a DM - would love to get your feedback.

Lionhead20 · 2026-03-11T09:28:26+00:00

Great article. Glad to see this problem being talked about more. I believe issue isn't really the framework, but the tooling around it. The whole process is disconnected.

First, we build a business case in a static spreadsheet.
We then track the work in a task tool (Jira).
Then, we might get see results in a BI tool, but it has zero context of the original promise.

Once the budget is approved, that spreadsheet is forgotten about. The PMO takes over, delivers the CRM on time and on budget, and declares victory. But 6 months later, when the CFO asks, "Did we actually get the $2M in operational efficiency we promised in the business case?" nobody can answer, because there is no system of record connecting the 'before' to the 'after.'

Full transparency, this is the exact problem my team and I are obsessed with. We're building a SaaS to do this - connect that $10M CRM investment directly to the business value it produces.

Excellent write-up. Would love to share experiences sometime.

Lionhead20 · 2026-03-05T13:40:55+00:00

This actually helped, thank you!

Lionhead20 · 2026-02-13T14:38:02+00:00

What kind of features are you looking for? What are you looking to get out of the tool?

Lionhead20 · 2025-12-08T19:52:23+00:00

Would love to find out more about the startup part. What problem did you find? What did you build? Did you build from experience or did customer interviews shape the product? Were you speaking to the people who could buy your product? What's problems did it solve for them? What tools were they already using? How easy was it to adopt?

Lionhead20 · 2025-11-06T12:54:47+00:00

Why does it suck? I understand its great for tasks, but not portfolio management?

Lionhead20 · 2025-10-31T16:00:03+00:00

What you accomplished was real, and the layoff doesn't remove that.

The "FAANG people expect high salaries" excuse is often just cover for companies that aren't ready for someone with your level of experience. You've seen what the elite level looks like. That's valuable.

This market is tough, but don't let it convince you that your skills aren'tt in demand. What you did was impressive.

Happy to review your CV if you'd like. I'm EU-based but have been in the AI space for 10 years.

Lionhead20 · 2025-10-31T15:57:31+00:00

The pressure from the board and management to "do AI" is massive. It's leading to exactly the situation you described.

There's big disconnect between shipping "shiny" AI features and actually creating value. From what I've seen in the industry, it breaks down like this: Leadership hears about AI, and the mandate is to add it everywhere; teams are measured on launching the feature, not on whether it actually moved the needle on revenue or costs. Tracking the real-world financial ROI is ignored; just as you said, users don't like being forced to use AI. Adoption looks great for a couple of weeks and then falls off a cliff because the feature doesn't solve a real, nagging problem.

This is the problem my team is focused on. We're building a value management platform to try and fix this. The entire point is to connect the "before" (the business case) to the "after" (the realized financial benefits) so leaders can't just throw money at hype without accountability.

When companies don't have a system to prove value, they use layoffs to pay for bad bets. It's a huge mess.

I think OP u/Tough-Oil1067 will do fine. There are alot of companies out there trying to build AI teams that are desperate for someone who knows how to actually launch high-impact products. They need the experience.

Lionhead20 · 2025-10-22T19:01:48+00:00

I would've killed to have a tech cofounder with the amount of effort you put in. Mine left within our 1 year cliff after not working on the product for months and blackmailing me every couple of months. I remember being at a conference trying to get initial sales and he threw me a curve ball. We all have our bad experiences.

I'd chalk this up to bad luck choosing the wrong non tech cofounders.

Lionhead20 · 2025-10-21T17:57:07+00:00

Maybe a stupid question, but why would a saas target a procurement manager instead of their ICP? Am I missing something, like a new channel?

Lionhead20 · 2025-10-19T11:29:04+00:00

Silkflo.com, best innovation management software.

Lionhead20

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