The #1 buzzword in the tech space the past several years? It might be “AI”. And rightfully so. The potential and realized impacts of artificial intelligence have become apparent in such a short time. Companies are now racing to build their own models or tie into existing models like ChatGPT in order to create process efficiencies, understand their business more intelligently, and serve their customers more effectively.
For professional services teams in the SaaS and IT spaces, AI might be familiar as a part of their business’ products and solutions, but it seems very few services teams today are taking advantage of AI internally. As that begins to change, it begs the question, how will AI impact things like project management, pre-sales and quoting, and other internal processes for PS teams?
We decided to specifically explore the area of PS Go-To-Market operations (since this is our area of expertise) and dive into 5 ways AI might impact this for PS teams in the near future…
- Predictive Analytics using SOW Data
This is a fairly large topic, but in general predictive analytics refers to understanding insights within datasets to make intelligent decisions and predict outcomes. Many organizations have adopted some type of predictive analytics solution and are at the very least using it for things like sales forecasting and cost analysis. But the key here is that the more rich data an organization has access to, the more business outcomes it can accurately predict.
Looking at professional services operations specifically, one source of extremely rich data is found within services SOWs (proposals, DOWs, whatever you call your scope of work document).
Within these documents exist key pieces of information and data points about the specific service engagement, including deliverables, timelines, resources, and other more nuanced characteristics like how certain clauses or language are structured. Linking this data with the actual outcomes and financials, predictive analytics could help businesses understand which agreements are driving the best outcomes.
Imagine if you could answer the question “Based on this set of services and this level of complexity in project XYZ, how likely is it that this customer will result in this level of profitability for the business long-term?”.
Interesting food for thought. Again though, predictive analytics is a huge topic with many possible applications to services pre-sales and operations. Perhaps we can expand on this topic in a future article. - Chatbots for Selling PS
Chatbots are nothing new. While many vary in their sophistication and purpose, large language models have advanced far enough to create deep, immersive conversational experiences for users in a variety of use cases. Support use cases in particular have seen a dramatic impact, allowing users to rely on their natural language to communicate nuanced issues or ideas.
While most chatbot applications are reactive in nature, more and more are being used to provide proactive assistance, make recommendations, and even sell or upsell new products. We see this as a big opportunity for professional services teams to utilize this proactive ability to recommend or sell services.
Complex service engagements still require human-to-human conversations for the most part, but more productized and standardized services can be easily recommended by chatbots. To take this a step further, creating a seamless buying and selling experience that either directly integrates with or exists entirely within the chat prompt can boost service adoption and revenue greatly.
Overall, the idea here is that you’re utilizing AI to make it more convenient for customers to find and purchase the services they really need – a task that has traditionally required back-and-forth communication and difficulty getting to the root cause of issues and initiatives your customers are working on. - PS sales enablement
Now we just mentioned that chat bots could handle selling simple services. But for more complex projects and engagements, a sales rep or PS expert is often needed to talk through deliverables, pricing, and other negotiable details. However, many SaaS organizations rely on account executives and CSMs who fail to fully understand the technical details of PS and forgo attaching key services that could enhance the customer lifecycle. The issue here is that most sales and CS teams aren’t trained or equipped properly to sell services since most of their focus is on product. This is where sales enablement AI could help.
By ingesting conversational data, services docs, and other related content, an AI assistant could provide real-time talking points that guide non-technical roles to talk intelligently about services throughout their interactions with prospects. There are some great examples of guided selling tools like this today, many of which integrate into email or video-conferencing applications. But the key here is training a model to understand the nuance of your company’s specific services.
By integrating something like this company-wide, anyone from an SDR to a marketing manager to a finance analyst can understand what is being sold to clients. The result? More services sold, the right services sold, and more trust fostered with customers. - Sentiment Analysis for Services Discovery
Sentiment analysis – the process of analyzing text or speech to determine the attitude or emotional disposition of an individual or group – is already being used in an array of customer-facing applications today. But on the services side, what if customer sentiment could be used to identify new opportunities for service engagements?
The idea here is that sentiment gleaned from customer interactions across the entire business can give insight into customer needs and intelligently suggest service offerings – particularly those that involve consulting or support hours. Much like the chatbot example, this can create a responsive and tailored reaction to any and every angry, skeptical, confused, or otherwise unsatisfactory customer disposition that may arise.
The biggest challenge to achieving this is creating a fully integrated system that channels all types of sentiment data back into training the model. This is important because a client’s feelings may not always be obvious from one interaction or communication channel. With a fully integrated system, teams could harness an array of data like product usage patterns, speech and tone analysis on calls, and chatbot interactions to feed a sentiment engine. This would more accurately predict customer satisfaction and recommend effective services as potential remedies.
- Intelligent Service Creation
A great segway from sentiment analysis is the idea that AI can be used to intelligently suggest modifications to existing services or create completely new service offerings. Content like customer feedback can be combined with other key data sources – like team internal messaging, trending news or social media posts, financials, etc. – to recognize emerging opportunities and synthesize new offerings, pricing models, and deliverables.
To take this a step further, models that are trained well enough could go as far as predicting new service categories or disciplines that don’t yet exist but might be extremely profitable in the near future. For example, imagine a prediction engine building a roadmap for your entire business that suggests how the tech landscape will change and how your product, offerings, and GTM strategy should adapt. This could affect how the PS team trains, hires, and what services it creates, continues with, or terminates.
This idea of using AI to predict emerging opportunities is an interesting one. The rabbit hole might eventually lead one to believe entire businesses could be created and run by AI, but that’s a bit far down the road right now. Still, product/service synthesis via AI is something many would consider on the leading edge of AI applications in general, and one that might revolutionize how business in general is done in the future.
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