Rapid Adaptation: Gráinne Maycock adapted Acolad Group's strategy to meet the demands of AI's rapid evolution.
Revenue Pivot: AI-driven products shift revenue focus, enabling new streams through enhanced sales capabilities for sellers.
Forecasting Accuracy: AI improved forecasting accuracy by 75%, enhancing business decision-making and staffing strategies.
Outbound Efficiency: Automating outbound processes with AI reduces time by 60%, enhancing lead conversion rates significantly.
Sales Team Training: Successful AI integration requires comprehensive training and gamification to ensure seller adoption and effectiveness.
Gráinne Maycock became CRO at Acolad Group just as the AI revolution was taking off, which meant she had to immediately adapt the company's revenue organization. Luckily, she had already overcome similar challenges when machine learning first took off.
We sat down with her to learn how she is overhauling processes for the company's 100+ sellers. Here's what she had to say.
Rapid Adaptation To AI
I took on my current role as CRO of Acolad Group in January 2023 at the start of the AI transformation, which required rapid vision adaptation, as well as technology and sales evolution. I had taken on similar initiatives back when machine translation was a revolution, but AI outpaces that at an unprecedented scale.
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This necessitates not only the right market vision, technology strategy, big bets, and solutions structure, but also transforming Go-To-Market motions, sales enablement, RevOps metrics, and sales training and empowerment.
It also means large-scale evangelization in the market with your customers, in your brand, and across your GTM approach.
Acolad Group is a late-stage, PE-backed company in the language and technology space, serving companies expanding globally to scale growth across tech, life science, and B2B. We provide solutions in AI translation, interpreting, media localization, data services, and more.
Through our SaaS and human-in-the-loop solutions, we help companies optimize and scale global enterprise needs across products, web and demand generation, media and entertainment, interpreting, and data services.
With over 160 sellers (SDRs, AEs, AMs, BDRs), I lead Marketing, Solutions, Pre-Sales, Enablement, RevOps, Sales Academy, and overall GTM strategy.
How AI-driven Products Transform Revenue Generation
For us, the biggest change with AI has been a revenue pivot. We introduced a suite of products. These products empower our sellers to bring AI value to our customers via agentic orchestration, translation, interpreting, and myriad other use cases.
This has enabled a new SaaS and consumption revenue stream, which improves value and valuation.
How AI Can Be Used In Internal Revenue Functions
Internally, AI informs:
Deal scoring
Forecasting based on trends
Pricing
Churn risk
Profile mapping for outreach
Humans, however, still heavily drive territory planning and pipeline prioritization. And they are key to building trust, driving positive outcomes, and bringing value to our customer organizations.
How Forecasting Can Be 75% More Accurate With AI
Author's Tip
Forecasting was often inaccurate due to human error. Field forecasts consistently relied too heavily on the hope that certain deals would close within specific timelines. AI takes hope out — as well as fear — and predicts based on similar deal steps, movement, close rates, and trends based on past data.
Let's take forecasting as an example.
Forecasting was often inaccurate due to human error. Field forecasts consistently relied too heavily on the hope that certain deals would close within specific timelines. AI takes hope out — as well as fear — and predicts based on similar deal steps, movement, close rates, and trends based on past data.
This provides a more mathematically accurate future outlook. This, in turn, helps drive business decisions more impactfully, such as staffing and right-sizing across the business.
Quantitatively, I can say that AI predictive modeling increased forecasting accuracy by 75%. And win rates increased from 40% to 60%.
How AI Makes Outbound 60% Faster
Outbound is another key challenge for many organizations. We automated and redesigned the following areas:
Sourcing leads
Researching company goals
Mapping buyer DISC profiles
Crafting outreach
This removes blockers for sellers, accelerates outreach, and makes tweaks easier. Before, manual processes were enormously time-consuming; now, internal prompting for each step has reduced time by 60%+. It also made custom personalization feasible.
This all led to a 25% increase in outreach and a 10% increase in tier-one leads converted in some areas of the business. However much more to do to ensure harmonization across scopes.
My favorite tool for outbound is LinkedIn Sales Navigator and its new AI features.
How To Help Sales Teams Succeed With AI
The best thing you can do is gamification across sales — that is key as go-to-market motions dramatically change. The sales engine and organization need to be smooth, simple, repeatable, and scalable.
Do not underestimate adoption or the effort required to shift both the sales engine and the teams' approach. Tools without people and training investment do not work. It's an end-to-end strategy-to-execution workflow.
The best thing you can do is gamification across sales — that is key as go-to-market motions dramatically change. The sales engine and organization need to be smooth, simple, repeatable, and scalable.
We introduced an AI role-play system, setting metrics and publishing leader boards based on:
Adoption
Scores in role-plays
Impact based on these
Clarify what you are measuring, why, and how. Introduce gamification, and publish results regularly.
AI training also helped global sellers more comfortably sell new solutions and handle objections. If necessary, have your academy team overtrain and investigate why adoption is not meeting expectations.
Why Stakeholder Expectations Must Be Managed
Managing stakeholder expectations has become difficult with AI. AI is a tool, not a magic bullet. That's key.
Achieving the possible results requires investment in some areas.
Why AI Adoption Is Never Complete
AI can do so much; the challenge is balancing focus and teams on the right bets to avoid overwhelming everyone at once.
Because of that, adoption is an ongoing process. We have many more areas for AI to address.
For example, we know AI offers wins in multithreading with AI guidance, and we are in early-stage exploration. And we know AI can apply to social signals, but we have not fully applied it yet.
Where AI Is Underperforming
AI did not achieve the desired 20% decrease in sales cycle times — mostly because buyers determine many of the steps.
But we are still exploring other ways to drive acceleration.
Why AI Should Not Be The Goal
Gráinne's Thoughts
AI helps speed the ‘how’ and ‘how fast’ you get there, but as CRO, you need to know the right end goals and what to measure and deliver.
AI is not the goal; revenue acceleration and optimized go-to-market motions are.
You need to know your goals, market, and pathway options. AI helps speed the 'how' and 'how fast' you get there, but as CRO, you need to know the right end goals and what to measure and deliver.
Then, AI can speed up that result.
Why Revenue Leaders Must Incorporate AI In Objectives
Here's my advice: Incorporate AI empowerment, initiatives, and goals into your leaders' and teams' objectives. Ensure sales enablement channels share weekly AI wins. And drive community engagement by spotlighting successes.
Beyond that, it helps to have a wide CRO network — inside and outside your industry. Ensure you have sounding boards and can compare wins and challenges.
And pay close attention to who is succeeding, where they are succeeding, and how they are doing it.
