Presented by Certinia

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In a world where industries like manufacturing, ecommerce and entertainment are already thriving with AI, the professional services industry stands on the brink of its own transformation. But at the same time, services organizations, and their employees, may be skeptical of the AI hype storm when it comes to their own operations and structure, straining to see if the risk is worth any potential reward.

It’s helpful to look at this through the three main priorities when it comes to service companies’ profitability:

• Satisfying customers continuously
• Retaining top resources
• Maintaining healthy profit margins

Yes, avoid AI for AI’s sake. But with a healthy dose of pragmatism, there is clear evidence that emerging AI use cases can help services businesses gain a competitive edge by optimizing the key business metrics sitting below the aforementioned profitability priorities: CSAT, NPS, and Gross or Project Margin.

Pragmatic AI, defined

Pragmatic AI is AI that focuses on real, tangible problems that businesses face today. It closes a loop through a virtuous cycle of capturing, aggregating, reasoning and surfacing data where people do work. Moreover, it is deployable with clear guidance on data, skill and digital maturity to ensure successful adoption and ROI.

In other words: Pragmatic AI targets real-world, current business problems rather than aspirational or flashy use cases. It gathers, processes and presents data in areas where people are doing work. It is user-friendly and designed for success, with measurable outcomes that help you calculate the return on your investment.

4 use cases that bust silos and drive profitable growth 

While profitability is the goal of every business, Pragmatic AI is uncovering new frontiers on the path towards profitability. Below are just four generalized outcome-based examples based on interactions with our own customers. These highlight where Pragmatic AI solutions can help close a loop on the journey towards services business growth.

1. Improving predictable services delivery

Outcomes: Increased CSAT, NPS, and Project Margin %

A leading IT services company has a major project deadline approaching, representing a material level of business for the quarter. The AI model analyzes past project performance, customer feedback, team dynamics, and external factors like industry trends to identify potential risks such as bottlenecks, resource allocation issues and even client dissatisfaction points before they happen. Based on these insights, services leaders can take proactive measures to reallocate resources, adjust project timelines and communicate transparently with clients, ensuring project success and client satisfaction.

2. Optimizing resource allocation

Outcomes: Increased CSAT, NPS, and Margin %

An embedded services organization in a software development company, like any, wants to ensure margin and utilization targets are met when assigning resources to work. But it also values the career aspirations of its employees, the type of work they enjoy, and their working style to build great implementation teams of driven, happy individuals. The AI model analyzes historical data on resource allocation, and metrics associated with delivery success. It matches that with forward-looking data such as employee attributes and career goals to find the best consultant-project match across resource pools for your business, clients and employees.

3. Improving services margins

Outcomes: Increased NPS and Margin %

A consulting firm offers various services packages to clients. The VP of Professional Services is planning for the next year and needs access to insights into which services offerings have the highest profit margins, and those that fare less well. The AI model analyzes historical data on resource allocation, time spent on each project phase and associated costs. It then suggests adjustments in resource allocation, project timelines or pricing structures to the packages on offer. As a result, the firm can optimize its services mix, focus on high-margin offerings and eliminate bottlenecks, leading to improved profitability. They can then build this into their headcount and other planning exercises to ensure upskilling and hiring to meet future demand.

4. Predicting and minimizing days to pay

Outcomes: Accurate cash flow forecasting

A software development company relies on steady cash flow to manage its operations. The AI model analyzes past payment patterns, client behaviors, economic indicators, and other relevant data to forecast potential delays in payment and identifies clients who might extend their payment schedules. Armed with this information, the finance leader can implement targeted communication strategies to expedite payments or allocate resources accordingly, ensuring a stable, predictable cash flow.

AI has prerequisites

Before you jump in, you need to take a temperature check on a couple of things. A clear signal you might not be AI-ready is if you lack “clean” data and a governance plan. If this is your situation, consider upskilling or seeking professional advice. Data is key to telling how your business has performed in the past. External data and benchmarking can be used and has its place, but the best information out there that truly understands the nuance of your business is your data. It’s important. Value it.

Quality and quantity of data carry equal weight. Consistent, clean data entry is required to make any sort of prediction possible; at least three to five years at a minimum. “Unclean” data often stems from fields that are rarely populated, or manually entered with possible typos, leading to data that may not be suitable in building any predictive model. Effort spent now on improving the user experience and validation at the point of data entry will help avoid costly data clean up exercises in the future.

Stay grounded in reality

The key to success is to take a pragmatic approach where AI is focused on solving problems your business faces today where the measurable outcomes outweigh the cost of deployment and roll-out. Look for solutions that can be integrated with your teams’ existing workflows and are designed for user friendliness.

The question is: will you embrace AI pragmatically, or be swept away by the allure of unchecked hype?

Chloe Stephenson is Senior Product Strategy Manager at Certinia.

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