A Guide to AI in Professional Services
For most service companies technology has been a tool to drive business efficiency. It delivers data management, process automation and information reporting.
All important tasks for sure. But table stakes. There's little to no differentiation or competitive advantage found in efficiency. If you don't do it, you'll be at a disadvantage. But doing it just gets you to an equivalency with competitors.
Artificial intelligence has gained tremendous momentum because it shifts technology benefit from efficiency to effectiveness.
And by effectiveness, I mean using technology to make staff more successful in their roles and better solve for their clients. It can empower managers to make better decisions and help business leaders see and guide the future.
But adopting artificial intelligence can be a complex undertaking. This Guide to AI in Professional Services can make it less complex. Consider the following four steps to simplify your adoption of artificial intelligence and shift your technology strategy from efficiency to effectiveness.
Understand How AI in Professional Services is Unique
Artificial Intelligence for service companies is different than AI for product-based companies. Not because of the technology, but because of the outcomes the technology must deliver.
Consider how service companies are different.
The solutions are intangible. The buyers are skeptical. The sales cycles are longer. And the outcomes are uncertain.
All factors that can be positively influenced by communication, education and knowledge. Three things that can be created and delivered by AI.
Another differentiator is the client relationship. Strong client relationships directly correlate to revenue growth and profitability in the services industries; far more so than in product-based industries.
And again, AI can aid client relationship development by identifying which clients should be engaged, when, and with what types of content.
Many times, the people selling the services are also the people delivering the services. They are most often advisors first and salespeople second. Most don't have sophisticated sales training. They are not well versed in sale methodologies. But they take recommendations and advice well.
This technology can deliver relevant, personalized and contextual guided selling, or next-best-actions throughout the buyer journey. This is proven to help services advisors win more business.
Another difference is the focus on existing accounts. Repeat business and growing customer share are arguably more important, and definitely more profitable, than new client acquisitions.
AI that can show how to deliver improved customer experiences, grow customer affinity or predict clients that are flight risks, will aid this imperative.
Another item is services delivery, which can be fluid. A project or service can quickly change direction, but not always be detected as quickly.
So, AI that can deliver a 360-degree project view, identify risk mitigation techniques, or forecast deviations in advance, will improve services delivery.
Creating differentiation is another difference. Gaining a competitive advantage in the services industries means promoting the brand, positioning the people as thought leaders, and describing services as repeatable processes with predictable outcomes.
And again, AI can aid these goals with content generation targeted to specific roles and with precision delivery.
These are just a few examples that share how artificial intelligence can be a powerful technology for services industries.
The 3 Types of Artificial Intelligence
Artificial intelligence is not a single technology. There are different types of AI that serve different goals. There three main types are shown below.
Predictive AI forecasts future events by assembling models that analyze historical data patterns and trends.
Examples include things like propensity models. So, for any customer or customer segment, professional services sales reps can identify the cross-sale service they are most likely to purchase.
Or on the flip side, for each service, you can identify the individual customer, customer segment or target audience most likely to purchase that service. The technology can then assemble the combination of message, offer and channel for the highest conversions.
Sales forecasting is another common example.
Or client churn forecasting, which applies historical interactions and other like customer data to predict customers likely to churn. It then recommends retention levers.
Popular vendors in this category include SAS, Microsoft Azure ML and Salesforce Einstein.
Generative AI focuses on creating new and original content, such as text, images and other media.
Examples include creating emails, documents, case studies, proposals, SOWs or other agreements or contracts.
Market share leading vendors in this category include Open AI ChatGPT, Google Gemini (formerly Bard) and Microsoft Copilot.
Autonomous AI can make decisions and perform tasks without human intervention or consent.
It can draft services reports. It can capture time and determine which expenses are billable. It can perform services billing or calculate percent complete and update forecasts.
Leading vendors include Amazon Web Services, IBM and Salesforce.
The technology becomes particularly transformational when these different types of AI are used together. They can perform tasks that previously could only be performed piecemeal and with labor.
Service company Use Cases
The number of business processes aided, automated or replaced with AI is only bound by your creative thinking.
For example, a big time saver is to use Generative AI for automated RFP responses. For this, we often use Gen AI with an LLM and client content. The content includes information assets, marketing materials, knowledge management artifacts, and prior RFP responses. It’s all applied to draft new RFP responses.
Generative AI can similarly create proposals or SOWs. This solves a common challenge. With changing staff and the passage of time, people forget what's in prior proposals, so proposal content goes without reuse.
A Gen AI proposal can be instructed to include content such as industry challenges, market trends, company quals, case studies or relevant examples. And the technology can perform QA on the documents. It can verify required content, proper scope, assumptions, exclusions, or liability provisions. It can apply special QA considerations for first of a kind or higher risk services.
It can then manage the proposal approval process. In doing this with our clients we normally use bid metrics to automatically approve when within allowable guidelines or identify exceptions which require manual approvals.
AI can perform resume searching. You can inquire for skills, certifications or areas of expertise. And you can automatically reformat resumes, so they are consistent for all resources and relevant to the client.
Some additional use case examples are shown below.
When planning for AI, don't start with the technology. Start with the most important business outcomes and then work backwards using technology to achieve them.
When prioritizing use cases, weight them by time to value, complexity and payback.
And one more thing. If you seek business transformation, focus on use cases that most directly impact customer interactions. That means focusing on use cases that directly impact how customer facing staff work. If you improve how work gets done by the staff that sell, deliver and support your customers, your upstream objectives will be realized.
This bottom to top approach will also aid your corporate culture, reduce change management obstacles and facilitate scale.
An Implementation Roadmap
No guide to AI in Professional Services would be complete without an implementation approach.
Again, if you are looking for a big payback or business transformation, you will need to apply the technology holistically.
Piecemeal projects, departmental solutions or AI deployed in pockets may deliver an incremental benefit but seldom scale well. To achieve scale, AI should be holistic, intentional, and managed with cross functional teams and enterprise-wide governance.
We've written a separate post on a 5 step AI Professional Services Roadmap to accelerate your planning, implementation and operation of the technology, so won't repeat that here.
Consider This
I'll leave you with three final insights.
First, any part of your business that is not using AI is substituting labor for technology.
Second, AI used for efficiency is helpful. AI used for effectiveness is a differentiator that creates competitive advantage. Focus on use cases that make staff more successful in their roles, empower managers to make better decisions, and empower business leaders to see the future.
And third, the question for business leaders is not if AI will drive a linear impact to their business, but how and when.
The path is uncharted, and missteps may seem risky. But your biggest risk when it comes to AI in professional services is watching from afar.