The hype around Artificial Intelligence has moved from the theoretical to the tangible. With businesses moving more of their experiments towards production use cases. While media reports of 90% AI project failure may be exaggerated, a significant number of promising initiatives still falter before reaching production. My experience helping large enterprises shows this isn’t due to a lack of enthusiasm, funding or technology, but a failure to build the necessary strategic scaffolding.
While I have seen much smaller numbers than 90% of these projects fail, we are seeing a few too many. Some projects finish on time, and budget, and deliver what was asked but not move into the production phase. The challenges we have identified are not a lack of funding, ambition or technology, it’s a failure to build the necessary strategic and technical scaffolding required to productionise AI. Moving from isolated pilot projects to scaled, AI is a journey and the people, and change aspects need to be well planned out.
Scaling AI successfully has a number of prerequisites:
- We need adequate programmes and change management.
- We need strong cloud foundations.
- It needs a bottom up use case
- Identify broad or deep AI and clearly communicate this
- It needs a central framework for evaluation and updates.
Programme & Change Management
When Cloud became a hot topic, large programmes of work were spun up with governance teams, and PMO offices, and migration were segmented into the 7 R’s, and then split further into waves. AI is going to be substantially more transformative than moving workloads to the cloud, and the impact on people is going to be far greater, but little thought has been given to the programme and change management aspects.
This requires a fundamental shift in thinking from treating AI as a series of disjointed tech experiments to embedding it as a core, strategic capability encompassing the tech teams, change teams, and spinning up the required PMO office and developing a strategy to manage the change that incorporates your people.
Cloud Foundations
For our clients, this often means building out a proper resource hierarchy in the cloud, defining identity and access management protocols, implementing robust security controls, and establishing clear cost governance mechanisms.
This gives everybody assurance that data will not be leaked, or used to train models on proprietary data, as well as being foundation for giving the right people the right access.
One thing that is still unclear to execs is what the infrastructure cost of AI is going to be, in our experience it is often magnitudes cheaper than exec’s are estimating, but those savings are quickly swallowed up by the required change management.
Use Case Identification
A focus on solving critical business problems is something that technologies forget about. Aviato are sure that a disciplined framework for identifying and prioritizing use cases is what separates the projects that realise enterprise value from those that end up being scrapped.
This does not need to be complex, and a bottom up approach seems to get the most traction, the employees on the coal face are acutely aware of what parts of their jobs they want to automate away.
What we have seen work:
- Where are the most significant bottlenecks, operational inefficiencies, or untapped growth opportunities? Put this into action by running a company wide survey.
- AI is fueled by data. A brilliant idea is useless if the required data is inaccessible, or of poor quality. Most white collar workers have access to the data that an AI Agent would need, however is there an API that the agent can use? Put this into action by adding a new column to your survey results and identify if the data is available.
- Evaluate potential projects on two axis, potential business impact and feasibility. Put this into action by stack ranking these, find the low hanging fruit that are not complex, but deliver reasonable returns. Build momentum from there for the more complex.
Broad or Deep
One area where expectations are often misaligned is around what the AI Implementation is supposed to do, Google Agentspace, or Glean is one example of a broad enterprise wide AI implementation that is familiar, it will summarise all of your companies knowledge and provide a Chat interface similar to Chat GPT or Gemini but trained on your company data. These agents are great at saving time for a broad number of use cases but are very unlikely to take autonomous actions.
The other hand we have the deep agents, these are very specific, they will help Peter from the legal team review contracts faster, or Mary from security find security details in logs, these agents will more likely take autonomous actions, and be very specific to a role.
When implementing a broad AI platform, a lot of people are expecting something that will take autonomous actions, and while Google’s Agentspace has a very exciting roadmap it is just not at the level of doing this deep work yet.
Centralised Framework
Once you have a production AI agent the job is not over, you now need a way to manage this, and bring a structured approach to optimising it. What happens when OpenAI or Google release a new model, do you switch immediately? Do the benchmarks they provide match your use case?
In the same way as we run software updates for the newest version of Java, a structured approach to life cycling system prompts and agents is required, and validating these against metrics that matter to your use case, latency is key for a chat bot, accuracy is key for a software engineering agent, and cost is a consideration to all use cases.
Google is definitely leading the way with their Vertex AI Evaluation tooling, but skipping over this and “YOLOing” changes to production agents is a problem waiting to happen.
Conclusion
Aviato are sure the future of the professional workforce is a partnership between humans and AI agents. However, this future won’t arrive by accident. It must be built with a disciplined, structured approach that treats AI not as a series of tech experiments, but as part of the core business strategy, and run as a transformation project.
