Omar Akar, VP METCA region at Pure Storage, lays out the three critical considerations for organizations looking to ensure their AI initiatives have tangible business impact
A little over two years on from the launch of ChatGPT and there has been a monumental shift in awareness, activity and desire to implement AI and GenAI projects. Decisions are still driven by FOMO (Fear Of Missing Out) and fear of falling behind competitors, as organizations want to explore the options and business leaders from all areas want to capitalize on the excitement to push through new initiatives.
The potential benefits often seem to outweigh the risks and lack of direction from AI projects: “It’s exploratory! Of course we don’t know what the results will be!” This has led to AI projects starting without deep consideration into what the end result will look like — what success is and how a team can take that project forward and implement it to business benefit.
Because of this profligate approach, we have reached the peak of frenzied, hype driven decision making when many projects have been green-lit without long term thought on strategy and ROI.
How to implement AI while maintaining business oversight
While AI continues to be a magnet for business budgets, enterprise adoption remains in the early stages of development and implementation. Because we are past the point of easy access to funding, organizations need to ensure an AI project delivers real ROI and isn’t driven by FOMO.
“Given AI’s dependency on data, one of the key capabilities that can support this uncertainty is a Storage as-a-Service model underpinned by a physical platform which can be scaled to deliver performance, throughput and capacity non-disruptively with instant impact.”
Omar Akar, VP METCA region at Pure Storage
In order to implement an AI initiative with business impact, here are three considerations for success:
Lens one: consider the cost and don’t get tied in
When the AI boom happened there were many market drivers to start new projects. Budgets were redirected to implement AI-driven initiatives within businesses — cost wasn’t the primary concern. However, this excitement and free spending has recently been reigned in as more organizations want to keep a handle on costs and understand where the ROI is coming from.
When embarking on an AI journey, key considerations include how to experiment without committing funding to a project that may not succeed; being able to scale up or down ensures organizations are not at risk of writing off expenses. Storage and computing resources are central to AI initiatives, ensuring these are fully utilized is key to maximizing the return on investment. If GPUs are bought, but not used for 18 months, that’s an expensive resource to be sitting idle. Better to have the ability to scale up and down to keep costs under control and deliver long term value.
Lens two: what’s the scope and scale, and is it likely to change?
If an AI project is an experiment — there’s likely to be uncertainty of how big it could grow. Both at the trial phase and looking further down the line to full scale implementation. Being able to grow from POC, through deployment, and then support a successful project is vital. Organizations need to be able to scale without having to throw away computing resources and start again. In this respect, the cloud is a good place to POC, to experiment and tweak, find out the potential, map the business value and then plan for the future. But, it can be expensive meaning that a successful project may lead to unsustainable costs.
The reality (which more and more are admitting to now) is that organizations don’t know where a project will go in the next 6-12 months. There are many directions and what success looks like varies. Not only is there business uncertainty, there’s the regulatory landscape, security considerations, customer expectations and requirements. While it could be a limitation to not know what the future holds, for organizations who have flexibility — it isn’t going to be an issue.
Consider as-a-service models, which take the guess work out of consumption. Given AI’s dependency on data, one of the key capabilities that can support this uncertainty is a Storage as-a-Service model underpinned by a physical platform which can be scaled to deliver performance, throughput and capacity non-disruptively with instant impact. It takes the guess work out and allows organizations to react very quickly to changing workloads. This applies not only to scaling up, but also back down. This is really appealing to organizations as they experiment but who don’t want to pay for infrastructure that isn’t being used.
Lens three: system reliability
For many organizations, once projects get past the exploratory stage, senior leaders are looking for ways to turn a project into a business critical service. For these situations, many will be looking for reliability and 100% uptime. But considering banks or online retail — customer facing applications are required to be reliable and available 24/7. These cannot be built on weak foundations. For reputational and customer retention reasons, not to mention regulation and cyber resilience, organizations can’t tolerate failure in their systems. It’s unacceptable to not have 100% uptime, so organizations should look to vendors who have the maximum reliability.
Calling time on profligate project funding
Not all AI implementations are profligate of course, but senior leaders have started calling time on the ones which are. Many wanted to start an AI project and thought it was business critical to do so. But bosses are no longer willing to hand out money in the hope of success.
Projects which don’t have a clear path to success or have no proper business case for implementation and success are being halted, or not started. Moving forward, there will be a lot more introspection before people give free reign on budgets for AI projects.
It’s time organizations have a back to basics approach to funding, rather than decisions being driven by FOMO. Consider what’s the business impact; what’s the chance of success — the likelihood of the project completing and delivering ROI; what’s the strategy behind the project; how will it improve customer interactions or processes; does the team have the necessary data? These are the questions that need to be asked before embarking on and funding a new AI journey. A built-out business case needs to be implemented with a formal check list before budget is committed.
