All eyes are on AI. The waters have turned a little choppy since the technology surfed into the mainstream business world on a wave of awe and excitement a few years ago. Now businesses are struggling to convert promise into impact. Despite relentless pilot projects, experiments, and demos, the MIT starkly reports 95% of generative AI pilots deliver zero return on investment.
To turn the tide, IT teams are changing tack. Rather than dabbling with low-value GenAI experiments (customer service chatbots being the poster child) they’re beginning to develop agentic AI solutions that can autonomously orchestrate end-to-end business-critical processes.
This is the route to millions – even billions – of dollars in AI ROI. But with mounting pressure from stakeholders, CIOs and IT teams need to prove it, which is easier said than done. Here’s what AI project owners are up against and how they can overcome the challenges of turning AI investments into measurable business impact.
Why measurable ROI matters more than ever
C-suites have waited patiently while AI has been put through its paces by IT teams. But there are two main reasons why boards are ramping up the AI urgency: costs and competitors.
Like every area of investment, AI is feeling the pinch from businesses scrutinizing their spending as macroeconomic forces inflate costs. While it was easier to gamble budget amid the heady hype that first greeted AI, funding will dry up if investments don’t bear fruit after a couple of years. Without demonstrable ROI, it’s hard to justify committing stretched budgets to AI over other competing capital priorities.
But there’s a catch-22. The latest and most valuable evolution of AI is agentic systems, which incur higher development and implementation costs than their predecessors. In other words, these solutions are the best way to prove value and win funding for AI, but they require more funding in the first place.
Meanwhile, businesses feel locked into a technological race with their competitors. Just as there was a stampede to be the first AI mover in the market, businesses are now aiming to retain pole position by being an early adopter of the technology’s latest, most promising waves. Claiming this accolade can help companies position themselves as future-proof, which ultimately drives business growth.
These pressures are converging to demand a higher standard of focus and discipline in AI development that results in measurable ROI.
The key roadblocks to turning AI into business value
Businesses worldwide are running aground in similar ways when it comes to achieving measurable business impact with AI. Here are five of the most common reasons why AI projects underdeliver.
1. Use case identification
First up is that classic question for any digital transformation initiative: where to start? If businesses can’t prioritize their AI opportunities, investment can be spread too thinly across a series of projects or wasted on low-value initiatives. Establishing the most valuable areas to apply AI sets businesses on course for measurable ROI from the get-go. AI Labs are a great proving ground where teams can test and trial prototype solutions targeted at their most pressing business use cases.
2. CIO and CFO alignment
The best AI use cases marry technical feasibility with financial potential. But without CIO and CFO alignment, AI use-case selection criteria become too dominated by factors like ease of implementation for IT teams. Stakeholder alignment is essential to ensure all projects target the most important and relevant business objectives and AI performance metrics.
3. Executive ownership/sponsorship
The truth is AI projects can’t reach their full potential if they’re handed to CIOs and IT alone. Successful AI initiatives are fueled by a powerful alchemy of internal champions across business functions – technical, operational, financial, and beyond – right from the planning phase. A Center of Excellence (CoE) should form the engine room of the operation, comprising the team responsible for driving best practices, monitoring, and adoption throughout the enterprise.
4. Data quality and availability
If AI solutions don’t make decisions that deliver business value, a lack of clean, connected data is likely to blame. The clue’s in the name: measurable ROI needs to be quantifiable. And for that, you need high-quality, harmonized, and accessible data. If data is fragmented in siloed systems, insights are lost and performance is hard to track, masking the true picture of AI ROI.
5. System integration
No matter how sophisticated the AI solution, if it’s not seamlessly integrated with existing business systems, it’s not going to deliver maximum business impact. In fact, there’s a risk of business disruption. Rather than teams being freed from manual work, they can find themselves having to fix errors and redo tasks. Integrated systems, on the other hand, enable AI ecosystems and tech stacks to be perfectly orchestrated, triggering agents at precisely the right time.
With so many potential pitfalls that can sink an AI initiative, businesses need to build measurement and value-tracking into every solution from the start, rather than treating it as an afterthought.
What it takes to make AI measurable by design
Maximizing ROI becomes smooth sailing when businesses follow best practices for measurable AI architecture. This approach also makes it easier to proactively step in and deactivate or adjust an AI solution at the earliest sign it’s underdelivering.
Feedback loops are essential to provide immediate insights into AI value. They also facilitate a cycle of continuous improvement where AI solutions can learn and self-optimize to deliver more valuable results for the business. Then it’s about setting governance guardrails and conducting regular audits so the solution is continuously tracked and monitored.
The ultimate foundation for outcome-driven, business-first AI, however, is operational transparency – understanding where processes work, where bottlenecks exist, and how operations connect to business outcomes. End-to-end transparency allows businesses to:
- Design (and continuously redesign) AI for business-critical processes by mapping solutions directly to operations and workflows
- Identify the improvement opportunities with the greatest potential for measurable business impact, then target each AI deployment at a precise business outcome rather than generic efficiency uplift
- Seamlessly integrate AI solutions with the business systems, operations, and processes it needs to share accessible data and deliver positive value
- Establish an operational baseline against which to compare AI performance
Thankfully there’s a platform that can handle all of this (and more).
How Celonis enables measurable, scalable Enterprise AI
Businesses are steering clear of the trend of fruitless or underwhelming AI initiatives by working with Celonis to realize measurable value. It’s all thanks to the powerful capabilities of the Celonis Platform.
The Celonis Context Model (CCM) – the heart of the Celonis Platform – integrates process data and business context to create a living digital twin of business operations. Built on this foundation, Celonis provides capabilities that enable you to analyze, design, and operate measurable, AI-driven solutions. Here’s a quick tour:
- Analyze how operations are running and where AI can be deployed with validated business impact
- Design ideal processes and reengineer operations with AI solutions that are grounded in operational data and context
- Operate new processes that coordinate AI agents, humans, and systems, while monitoring their impact on processes and key business metrics
By combining business knowledge, process execution data, and enterprise architecture, the Celonis Context Model feeds AI the information it needs to understand how to deliver measurable business impact. This operational context – powered by the Context Model – serves as a governance layer for human and AI agent workers alike.
Move from AI investment to AI impact
CIOs and IT teams are feeling stakeholders’ expectations for the pot of gold at the end of the AI rainbow. To deliver the goods by scaling AI impact beyond pilots, businesses need a platform for end-to-end operational visibility,insights, and action. A platform that does this enables businesses not just to design and deploy AI that’s measurable by design, but to continuously track and optimize each solution for maximum ROI. With Celonis, businesses can start tying AI investments to tangible value, and stop initiatives treading water or going under.