In brief
“Fetch stands out from other AI solutions because it helps us FIX our data, rather than requiring it to be perfect beforehand.”
In this short article – what they say about Fetch – we use a dog metaphor and real client feedback to showcase Fetch’s uniqueness in fixing data, not requiring perfection upfront.
We explain how Fetch is an orchestrated network of AI agents that automates CX data gathering, quality checking, KPI calculation, and secure submission to the CX Benchmark.
To finish, we demonstrate how Fetch is a practical, explainable tool that helps asset managers strengthen their data foundations, secure efficiency savings, and gain a competitive edge.
What they say about Fetch: fixing data, not requiring perfection
What they say about Fetch – a client actually said it last week, and I was pretty pleased to hear it, “Fetch stands out from other AI solutions because it helps us FIX our data, rather than requiring it to be perfect beforehand.”
Fetch is a network of AI agents – AI is not simply one technology, but rather a combination of multiple tools. Agents are one of these and you should think of them like your non-human workforce: faster at analysing data (completing weeks of work in seconds), more cost-effective, and better at pattern recognition and prediction. Like humans and dogs, though, agents are imperfect too, but in controllable ways. Specifically, we train Fetch to follow procedures and ensure it provides evidence of its actions so you can explain what happened.
What is ‘agentic AI’ – when multiple agents work together to achieve an objective (as in Fetch), we call it ‘agentic AI’, which excels at repetitive, narrowly defined tasks and only gets more powerful the more data you feed it, completing weeks of work in seconds.
What does Fetch do? Fetch has five use cases that combine to strengthen your data foundations, relieve pressure on internal resources, and let you demonstrate the ROI of your CX initiatives:
- Automates your end-to-end CX data gathering.
- Calculates your KPIs.
- Checks data quality for approval.
- Feeds clean data to your data hub
- Transmits your results securely to the CX Benchmark, so you can discover, in time and money, where your CX has out- and underperformed at engaging clients.
Imagine Fetch is a team of dogs – we train each Fetch dog (sub-agent) to know where in one of your systems to look for a specific stick, to recognize it when it sees it, and to alert you if it thinks something isn’t right. Because agents are based on robotic process automation (RPA) and trained on historic data, your Fetch service dogs have no incentive to bring you a ball if they can’t find a stick or to hallucinate a stick that doesn’t exist.
Why Fetch is useful – firms at the leading edge can gain efficiency savings by spending less time measuring CX and more time managing and improving it. However, the greater value will transfer to the firms that are currently handicapped by weak data – held back from capitalizing on CX as a source of competitive advantage by fragmented, inconsistent, or hard-to-access data. By helping firms fix their data and being applicable beyond CX, Fetch also provides a vital ingredient for firms’ broader AI ambitions: quality data. Without this, it’ll be worse than “garbage in, garbage out” – AI will magnify the risks of poor data.
Where AI is most powerful – crucially, Fetch applies AI where it is considered most powerful – at the intersect of data, judgement, and action (Bérubé et al, 2018) – while simultaneously avoiding the ‘black box problem’, by telling you what it has found, which KPIs have passed its quality checks, and which ones it recommends you investigate and why #explainablemetrics
Impact of Fetch on managers – by eliminating the data collection, cutting, and pasting that existed because systems couldn’t talk to each other and that managers are doing manually, Fetch will increase demand for managers who can connect technology, talent, and business outcomes. Specifically, it will give managers time for their real job: to synthesize and prioritize, arbitrate trade-offs, and make judgment calls under uncertainty.
Governance and controls – with agentic AI, it’s not just the use case that matters but also the execution and ongoing governance. To guard against this:
- Artefact has 11 years of global knowledge and experience in training AI and integrating it into business processes.
- Accomplish has a 5-year set of historic asset management CX data providing statistically valid, directly relevant, and real-world training data.
- And in line with its Agentic AI Risk Control Framework, Accomplish will not sign off on your Fetch agent for live deployment until testing proves it conforms with the Helix© CX data taxonomy that ensure consistent benchmarking inputs.
Great to see theory in action – real life often deviates from the theory, though, so I was pretty pleased to receive the client’s feedback last week: “Fetch stands out from other AI solutions because it helps us FIX our data, rather than requiring it to be perfect beforehand.”
See if you agree with what they say about Fetch – watch it complete 2 weeks of CX data work in 20 seconds.
Frequently asked questions
1. What do clients say about Fetch?
Asset managers at the leading edge of CX are impressed to see Fetch complete what they know is two weeks of work in 20 seconds. Meanwhile, asset managers with data issues or gaps express relief that Fetch will fix their data, rather than requiring it to be perfect beforehand.
2. Why does Fetch stand out from other AI tools?
The Fetch data collection agent stands out from other AI tools by being your agent, customized for you, and operating within your control environment. With its roots in robotic process automation, Fetch is trained to know what data to look for, where, and what constitutes ‘good’ data, alerting you to any data points that do not pass its quality checks. It comes pre-trained for CX data, but can process any data, making it the perfect component of a business intelligence (BI) tech stack, too.
3. How does Fetch fix data instead of requiring perfection?
Fetch will strengthen your data foundations by running all the data it processes through quality checks and alerting you to the few items that need your attention. This embeds the cleaning of key data into your routine, and that gives you the confidence to act on the insights the data yields. Because agents are based on robotic process automation and because Fetch is trained on historic data, Fetch has no incentive to bring you an incorrect data point or to hallucinate one that doesn’t exist.
