Grow Agency Revenue with AI

In this episode of "Full Tech Ahead," host Amanda Razani interviews Sarah Edwards, CPSO at Kantata. They discuss the strategic implementation of AI in the services industry (consulting firms, agencies, and B2B IT service teams). 

Edwards argues that measuring AI success solely through the lens of traditional productivity and speed is a massive mistake for services firms, as charging by hours while simply doing tasks faster inevitably leads to a financial "race to the bottom." 

Instead, she advocates for shifting toward an AI-native operating model that powers the "expertise economy." 

Key Quotes

• "Measuring productivity [is] the wrong way to measure AI success... if I'm just delivering things faster and faster, traditionally billing my time based on hours or days, well, how am I growing my revenue? That just becomes a race to the bottom."
• "Traditionally, expertise has been reliant on tribal knowledge... AI is disrupting all of that. For the first time, we can really compound that expertise across your business."
• "AI for me is not just about getting faster. It's how do I get better? Because unless I get better... I'm not going to win."
• "In services, quality has been something we've always struggled to measure... Now with the help of AI, we can truly start to gather and measure [sentiment] during project delivery."

Takeaways

• Ditch the Speed Metric for Quality: In professional services, utilizing AI to execute work faster shrinks billable hours without adding value. Firms must stop treating AI as a siloed efficiency tool and start measuring leading indicators of revenue growth, margin improvement, and transformed service delivery.
• Capitalize on Compounded Expertise: Historically, consulting firms were constrained by "heroics" and individual expertise, which created an operational ceiling. An AI-native model unlocks years of hidden project context and conversation logs, instantly upskilling every consultant to the level of the firm's best performer.
• Automate the Sales-to-Delivery Handover: One of the largest operational friction points is when sales teams "throw a project over the fence" to the delivery team. AI agents can eliminate this silo by parsing entire sales-cycle call data into comprehensive briefs, ensuring scope, stakeholder concerns, and project requirements are perfectly aligned.
• Transition to Outcome-Based Metrics: Instead of tracking static, trailing metrics like "on time" and "on budget" at the end of a lifecycle, firms can use AI to track real-time qualitative health indicators, such as client sentiment, delivery team mood, and continuous project drift, while the work is actively in flight.

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Transcript

SPEAKER_01 0:19

Hello and welcome to Full Tech Ahead. I am Amanda Razzani, and with me today, I'm so excited to have Sarah Edwards. She is the Chief Product Strategy Officer at Cantata.

SPEAKER_00 0:30

How are you doing? I'm good, thank you, Amanda. It's great to be here. I'm hot. We've got lots of hot weather in the UK, but it is lovely to be here.

SPEAKER_01 0:38

Well, I hope you're staying cool. I understand hot weather being here in Texas. So I'm excited to hear our topic today is about AI and how to use AI tools the most effectively. But before we go into that, can you share a little bit about Cantata and what services does the company provide?

SPEAKER_00 0:59

Yeah, absolutely. And Cantata provides solutions to purpose-built solutions for the services industry. So when you think about the services industry, they may be pure play consulting businesses, agency, IT services, or even services teams within B2B software companies. And we really help them power and predict and run their business. So we provide AI-driven solutions that really help if you think about all the way from scoping and winning and creating work to delivering it, to resourcing it, to managing your forecasts and financials, that whole end-to-end life cycle. And really, how do you sort of connect that and how do you make sure that every project you're delivering is the best? And that's really what we aim to work with our customers on.

SPEAKER_01 1:40

Fantastic. Well, so you've mentioned before that a lot of companies might be viewing AI in the wrong way. And you suggest a better way. So let's start with why is measuring productivity the wrong way to measure AI success?

SPEAKER_00 1:58

Yeah, I think, and particularly for the services industry. And it's an industry I've worked in for now about 30 years. And I think if you look back at, you know, services firms over the last probably 30 years, they didn't really change that much. And their model was really built on people and human delivering effort and me billing those in hours or days. I think the challenge is that now we've got AI, which is proving already, I mean, there's lots of successes out there that it can enable people to deliver faster. It is an efficiency play. But actually for a services firm, that actually has a bit of an impact because really that just becomes a race to the bottom. If I'm just delivering things faster and faster, yeah, I'm traditionally billing my, you know, time based on hours or days. Well, how am I growing my revenue and how am I really truly growing differentiation in the marketplace? And I think in our industry, it's more about, it's less about an efficiency play. It's about actually how do you change your whole operating model as how you operate as a business, as an AI-native firm with sort of AI at the core. And to me, as I say, it's not just about efficiency, it's about how am I measuring that we're getting better, that we're delivering more, that we're growing our revenue. And I think at the moment, there's been a lot of, I would say, quite siloed AI strategies where it's, you know, we're making people go quicker, we're delivering quicker for a services business. It's got to be more than that. And it's got to be about how does that help us generate more revenue and differentiate and win more customers.

SPEAKER_01 3:20

Yeah, and to that note, you've mentioned leaning more into pulling out the expertise economy. Can you explain a little bit about what that means?

SPEAKER_00 3:30

Yeah, absolutely. Again, in this industry for a long time, what has expertise been? It's mainly been reliant on uh tribal knowledge, on individual consultants. So, again, lots of consultants have built their career becoming an expert in something. And actually, the challenge has always been for services firms that actually that gives me a ceiling. You know, I've either got to grow my talent, I've got to bring more people on. It takes a lot of time for them to become an expert and over years become become an expert, and then I can sort of build them out as a, you know, an expert. Well, AI is disrupting all of that. And I think for the first time, what really excites me in this industry, for the first time, we can really compound that expertise. So to me, the power of AI and the opportunity of AI is actually how do you compound that expertise across your business? How do you move it so you're making every consultant their best? Everyone knows, you know, you're sharing all of that knowledge and all of those lessons and conversations that you have with clients. All of those are learning points. And actually, traditionally, we don't surface that up. It all remains in someone's head, and we're not sharing that expertise. So I think to me, you know, we talked about at ourselves pick-off, we have this theme about expertise to the power of 100. But I think to me, it's, you know, we now have the opportunity to really amplify and compound that expertise. And I think that's really exciting, particularly in an industry that probably is being constrained by, you know, reliant on heroics and individuals in the past.

SPEAKER_01 4:53

And can you go in a little further into how you're able to tap into that expertise? Do you mean from the data collection of the AI or by using a Gentic AI? Can you share a little bit further how to tap into that?

SPEAKER_00 5:07

Yeah, absolutely. Um, an example is, I mean, really a simple example is, you know, as a consultant or someone working within a services firm that's delivering one project, I don't know what's happening across all of those other projects, all of those many other hundreds of customers that you're talking to and people are talking to hourly and daily, all of those lessons that we're learning across all those projects. As a consultant or as a project manager on a project, I don't know that. Well, actually, now that can be compounded and that can be surfaced up to me through AI. So actually, we have the ability to learn from every project and every conversation. So even if I'm not part of a conversation, I think, you know, traditionally again, even if you look at project delivery, what have we tripped typically tracked? We track things like is the project on time? Are we delivering it to scope? Well, actually, now I think it's about how do we track and manage the quality of that delivery? What's the client experience? What's the sentiment of that customer? And actually, you know, now we've got AI and agent that can listen into the call, it can give me feedback based on the sentiment that call, it can look at trends in terms of, you know, what's the trends across all of our projects and our different types of customers with the expertise that we're deploying and helping me to surface risk to really improve the services quality and the experience that we're delivering to our customers. And I think if you look at the vastness of all of that data, that you know, when you've got projects that you're delivering to different types of customers with different people, different scopes, different outcomes, I can now collaborate and really learn from all of that data with the help of AI.

SPEAKER_01 6:35

Absolutely. I know there are still a few people that are hesitant about the quality of the data and the factualness. Can you speak to your experience with that and how do you know that you can trust the information?

SPEAKER_00 6:52

Yeah, I think that is a valid concern. And I think you know, we've all seen it with our use of AI. Again, to me, it comes back to getting some of the foundations right. If you don't have the data right in the first place, I mean, the key thing that Cantata brings is how do you connect that data and how do you build trust in that data? Because I think unless you've got that data that is connected and you've got trust in that data, um, then I think you'll never be able to leverage and have the confidence in terms of what AI is going to deliver. So I think it is about making sure that you know you have and how you deploy AI as well. I don't think this is about, I think there's a lot of customers that I've seen where they've gone very scattered and they're building specific agents. There's all these different agents, but actually, you know, I think it just becomes a swirl, it becomes a mess. Actually, what you want to do is have that connected intelligence. How do I really connect that intelligence and build intelligence that is importantly learning continuously? Because that's what I think will bring trust and confidence to businesses.

SPEAKER_01 7:50

Do you think that AI is helping to break down some of the silos between different departments and uh improve the communication?

SPEAKER_00 7:58

Absolutely. I mean, again, example in the world I work with with services, I would say that sales to delivery handover has always been a massive friction point. You know, the sales team go and sell something, they chuck it over the fence to delivery, and they've suddenly got to figure out, you know, how I deliver this to the budget and costs that it's been sold at. I think it's breaking down some of those barriers. Again, I think you can bring delivery in a lot more earlier, they can listen to and get all the insight from all the calls that happened, all the interactions we had throughout that sales cycle. So creating those, you know, one of the things we do is create that sort of sales to delivery handover brief. How do you make sure everyone on that team understands exactly what conversations, what stakeholders you engage with in the sales process, where there was pushback on scope or outcomes, and making sure that we're really set up for delivery success. And then importantly, again, learning from that and learning, are we scoping projects correctly in the first place? So I think absolutely, I think it breaks down the silos all the way across from sort of you know sales to delivery to success.

SPEAKER_01 8:56

So moving forward, we've seen that this technology has advanced quite rapidly in a short period of time. What do you expect is next for AI and its use cases?

SPEAKER_00 9:07

Yeah, I think, I mean, you're right. I mean, I think we learn something every year. You know, we launched our expertise engine, and I have to say, you know, I see with my team, we are pushing its boundaries every single day. And it's amazing, I think, what we're now showing that it can support. So I think, you know, the possibilities are, I think, you know, endless. I think the challenge has always been how do we link that to operationally, how we're changing how we run our business. I go back to what I said at the start. I think AI will be limited if you're just looking at it on a siloed advoc basis. You should be looking at AI in a business and say, operationally, how does this change the roles we've got in the business, the people we need in our business, how we operate, how we package and sell our services? And I think that's what's going to come next. I think people are now seeing actually, you know, I've now got the ability to connect all of that data and connect from that and learn from it. And actually, it's enabling me to transform my business.

SPEAKER_01 10:01

Absolutely. What do you think is the biggest mindset shift that leaders need to make when it comes to AI?

SPEAKER_00 10:10

Um, I maybe I'm reiterating, but I think, you know, to me, it is about like um looking at it operationally, how it's going to change your business. I think coming back to the start of this, it's not just an efficiency play. For me, and particularly in the industry that we serve, it can't be just an efficiency plate. Otherwise, it's just a race to the bottom. It has to be about how do we grow our revenue as a business, how do we innovate, how do we deliver new services in new ways, and how do we really transform how we deliver those services with no longer just people, but with people and agents. And I think it's the organizations that get that right, that really and then invest in getting some of those foundational components right. I think they'll be the firms that will really win and succeed in the future. And I think those that aren't doing it, I think you're going to be really challenged. And I think it will become a bit of a race to the bottom bottom.

SPEAKER_01 10:57

Definitely. It will be interesting to watch. Well, if there was one key takeaway you could leave our audience with today, what would that be?

SPEAKER_00 11:05

Um, it would be to rethink your operating model. I think look at the roles that you have in your business and look at it across the end-to-end sort of, you know, don't try and look at it in silos. I think you've got to bring, you know, right from the top down, look at your business as a whole and really think about a, do we have the connected data and do we have the trust in that data? Because if you don't have that, you know, I think you'll be really challenged to build the trust and the confidence that you need. I think also think about the ROIs that you the how do you measure success of this? Again, I think firms need to challenge stuff. The measure of success isn't just about efficiency and cost saving. The measure of success and how do I track some of those leading indicators that AI is transforming our business and having an impact in terms of, you know, it's not just about getting faster. I talked to a client the other day and it resonated with me. He said, AI for me is not just about getting faster, it's how do I get better? Because unless I get better, unless I'm delivering a better quality service to our customers, um, unless I'm getting better in terms of growing my revenue and yes, growing my margin as well, but unless I'm getting better, you know, I'm, you know, I'm not going to win. So I think to me, it's what are the metrics you can measure to show that you are getting not just quicker and faster, but you're getting better. And again, think about that continuous learning loop. How do you build and connect your data in a way that you can continuously learn from it? I spoke to one of our large customers a couple of weeks ago and they said to me, you know, they've been running every project through our system for the last seven years. And it's like, well, what does that tell me? You know, what does that tell me in terms of if I can truly unlock that potential and I'm learning from every conversation, every project we scope and deliver and resource, that can be really impactful in terms of how I grow my business successfully moving forwards.

SPEAKER_01 12:48

Yes, more about the quality, less about the speed. Yeah.

SPEAKER_00 12:52

Yeah. And I think in services, services quality has been something we've always struggled to measure. We've always looked at the hard metrics. Let's say, is it on time, is it on budget, etc. I think now more people are moving to outcome-based sort of services. And therefore, I need to measure the quality of that. What's the sentiment of the customer? You know, what's the sentiment of the team delivering it? And I think, you know, those metrics now we can start with the help of AI, we can truly start to gather and measure as we're delivering projects, not just at the end, but during project delivery to make sure that we are delivering the experience we need to.

SPEAKER_01 13:25

Yes. Well, I want to thank you so much for coming on the show today, Sarah, and sharing your insights with us.

SPEAKER_00 13:31

Great. It's been great to hear. I love talking about this. I think it's an exciting time for all of us. We learn something new every single day. And I think, you know, I think for me that's really exciting. And thanks, Amanda. It's been great to speak to you.

SPEAKER_01 13:43

Yeah, I agree. And likewise. And to our audience, if you have any questions or comments, leave those below and I'll try to respond as soon as possible. Have a wonderful day.