AI has shortened the path from idea to proof of concept. It has also exposed a harder problem: many companies can experiment quickly, but struggle to turn those experiments into systems that improve daily decision-making.

At Acies Global, Founder and CEO Mukund Raghunath helps clients move from experimentation to operating impact through what he describes as the creation-scaling-consumption cycle. The process starts with defining the right problem, continues with building solutions that can work across real business scenarios, and ends with making intelligence usable inside the tools and workflows where decisions are made.

Acies brings together data engineering, data science, software engineering, and business context to help companies convert analytics into measurable outcomes. In this conversation with The Consulting Report, Raghunath discusses why intelligence needs to be built for consumption, how AI is changing client needs, and what kind of culture supports scalable problem-solving. This interview has been edited for length and clarity.

“Consumption involves enabling everyday decision makers to have access to the right information in the tools that they use to drive the business.”

The Consulting Report: Many companies have access to more data than ever. Where do they tend to struggle when trying to turn that information into better decisions?

Mukund Raghunath: As firms strive to make better decisions based on an increasing amount of information, it has become extremely important to think about how intelligence can be consumed at scale to drive the business. 

Any problem-solving exercise starts with a creation stage which involves defining the problem iteratively, identifying the information required to solve the problem, and experimenting on different approaches to arrive at a proof-of-concept solution. 

While creation lets you ascertain that a problem is solvable, it does not ensure that the problem can be addressed repeatedly for all possible scenarios. Scaling involves architecting the solution for the environment it has to operate in taking into consideration all the moving parts that are involved.

Solutions need to be consumed at the key points of business process interaction to deliver the original value that was envisioned. Consumption involves enabling everyday decision makers to have access to the right information in the tools that they use to drive the business. This leads to the next set of problems and questions that need to be answered leading to more creation activities. 

Acies Global enables our clients to navigate through the creation-scaling-consumption cycle seamlessly and infuse intelligence at scale into their business processes. We bring a mix of skill sets in data engineering, data science, software engineering, and a deep appreciation for business context that sets us apart from our competitors.

The Consulting Report: Are there one or two major client projects that demonstrate your firm’s capabilities?

Mukund Raghunath: For a large global industrial manufacturer, we developed an end-to-end system to identify and maximize after-market parts and service opportunities. We developed a slew of consumption-focused data products by combining data from diverse sources, including customer, equipment installation, service history, warranty, device telemetry, customer complaints, BOM, parts catalogs, and service manuals.

We then utilized those data products to build ML models to predict parts failure and obsolescence across different product categories and products. On an ongoing basis, weekly product telemetry data was scored against those models, and the capability to trigger alerts for preventive and preemptive maintenance to the service and dealer teams was built. We also developed an application that enabled internal service teams and dealers to monitor their installed base, manage the alerts received, and plan appropriate maintenance activities with the customer. The end-to-end solution enabled 10% growth in the client’s after-market parts and services business.

For a leading global travel aggregator, we developed a solution to maximize revenue and margin growth through activities that drive up the lifetime value of the customer. We built an automated system to calculate the value of each customer by assigning appropriate cost and revenue to each visit to the company’s website and application portal. 

We also analyzed the activities customers engaged in on each visit and the impact of each action on the customer’s value to the company’s revenue and margin. Scenario tools were built to enable the marketing and product teams to design campaigns that would drive value-generating behavior, while automated A/B testing and measurement systems enabled quick feedback to those teams. The solution enabled a 5% increase in repeat traffic and a corresponding increase in revenue.

The Consulting Report: What kind of culture supports Acies Global’s style of problem-solving?

Mukund Raghunath: Acies Global’s culture is characterized by a growth mindset, prioritizing continuous learning and employee empowerment. 

We encourage individuals to take on new challenges and learning opportunities beyond one’s comfort zone. The firm makes significant investment in training and continuous learning systems for each associate.

Intellectual humility is also central to our culture. The leadership team is supportive and approachable, and debate is encouraged across the organization without being constrained by hierarchy.

We also believe in giving young associates early opportunities to lead challenging engagements, supported by structured mentorship and guidance.

“Many companies hit a wall despite garnering quick wins because they struggle to convert the promise of a proof of concept into full-scale systems that can use AI to run operations at scale.”

The Consulting Report: How is AI changing the types of client needs Acies Global is seeing?

Mukund Raghunath: AI has allowed the speed of experimentation to be accelerated significantly. Proofs of concept, or creation as explained earlier, that would take a few months are now possible in a matter of days. Many companies hit a wall despite garnering quick wins because they struggle to convert the promise of a proof of concept into full-scale systems that can use AI to run operations at scale. We see two types of client engagements arising from that struggle.

Data enablement: Many clients find that their data is not in a shape that would allow them to make the best of the breadth and depth of the possibilities that can be explored using AI. We are being approached by clients to help them get their data AI-ready.

Scalable AI: Many proofs of concept are not designed with scale in mind. Building scalable AI solutions requires rearchitecting the solution while thinking thoroughly through all aspects of the problem space, business context, and computational constraints. Clients are increasingly approaching us to help them build scalable solutions using AI.