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This interview is a component of <\/em>Workology\u2019s HR highlight series<\/em>, where we highlight influential HR leaders who’re shaping the longer term of labor through daring ideas and real-world impact. For this post, we\u2019re talking to Tom Hacquoil, CEO of Pinpoint.<\/em><\/p>\n Tom Hacquoil is the co-founder and CEO of Pinpoint<\/a>, an award-winning talent acquisition platform utilized by organizations in greater than 50 countries to draw, hire, and onboard top talent. A serial entrepreneur and HR technology innovator, Tom launched his first software business as an adolescent before happening to function Chief Digital Officer at Calligo. He’s a passionate advocate for improving hiring practices, constructing high-performing teams, and helping employers create higher candidate experiences. Under his leadership, Pinpoint has earned recognition from Gartner, G2, and Capterra, while becoming a trusted recruiting technology partner for corporations all over the world. Tom can be deeply committed to developing future talent and has played an lively role in supporting digital skills and entrepreneurship initiatives throughout his profession.<\/p>\n Before Pinpoint was founded, Tom was leading a coding cohort with just 16 spots that received a whole lot of applications. He needed a quick, flexible tool that might make it easier to discover the fitting candidates and provides them an excellent experience. However the applicant tracking systems available were either too clunky or too basic, which might make the choice process even harder. Out of this experience got here the concept for Pinpoint. Hacquoil, together with co-founders Bill Rogers (Chief Innovation Officer) and Tom Luce (Technical Fellow), got down to construct a greater ATS that might help organizations improve their hiring processes.<\/p>\n \u200b\u200bThe easiest way I can explain it’s with an example. Early on, we signed a postal service as a customer. The morning we did, their talent team was hiring two completely different kinds of individuals: couriers at scale, where success means moving quickly and getting someone trained and out on a route, and a brand new CEO, a process that might take months and be scrutinized by the board at every step. Same organization, same talent team, and on paper, the identical hiring process. But in point of fact, every part was different: the assessments, the timelines, the people involved, the definition of an excellent final result. Most organizations, in the event that they map it truthfully, are running three to 5 genuinely different shapes of hiring without delay. Multiply those shapes by geography, different compliance regimes and candidate expectations, and the number keeps growing.<\/p>\n The issue is that just about every ATS available on the market was built for one in all those shapes, often mid-career corporate hiring, because that\u2019s what demos well. It does that shape well and the others badly. So the teams it doesn\u2019t fit route around it: feedback results in email, reporting gets stitched together by hand, and the candidate experience drifts away from anything you\u2019d have approved. That\u2019s how fragmentation starts, and it compounds. We built Pinpoint to be the system that works for the entire organization. The higher query when evaluating any hiring system is which one works for the entire organization: every role type, every team, every region.<\/p>\n The fear is comprehensible. A recruiter\u2019s day is stuffed with coordination work that software is genuinely good at, and watching a tool do a piece of your job faster than you’ll be able to is unsettling. But I believe the fear misidentifies what AI is definitely targeting. What it\u2019s coming for is the coordination work in a recruiter\u2019s day, the parts no person got into this work to do: the scheduling, the chasing, the status updates, the \u201cjust circling back\u201d emails, the hundred small acts of keeping a process moving that fill a calendar without adding much. That\u2019s the predictable work, the work that ought to occur the identical way each time, and it\u2019s exactly what automation and AI are designed to soak up.<\/p>\n What\u2019s left when that goes is the part that was at all times the purpose: judgment, applied by people, about people. There are two layers to it. The primary is asking the person hire: reading a resume for what it doesn\u2019t say, sensing that a candidate who interviewed badly is likely to be the fitting person on a foul day, weighing two strong finalists when the numbers say they\u2019re even. The second layer sits with the leader: someone has to design the system that frames every hire, determine what attractiveness like, and determine how decisions get made. The recruiter owns the hire. The leader owns the system the hire happens inside. Talent acquisition is judgment work at every layer, and that\u2019s where the function\u2019s value now lives. The software handles the method. The recruiting, the actual recruiting, becomes judgment.<\/p>\n Most talent teams I speak to have run their first real experiment with AI in hiring by now. A whole lot of them got here away underwhelmed. The sourcing tool surfaced candidates that felt no higher than before. The interview summaries missed the things that really mattered. The shortlist looked right on paper however the candidates who scored highest weren\u2019t those who made the most effective hires. When that happens, the instinct is guilty the tool: perhaps the following version shall be smarter, perhaps a special vendor\u2019s AI is the one which finally delivers. That instinct will likely be improper.<\/p>\n AI amplifies the hiring process you have already got. Point it at an entire, current picture of how your organization hires and it might probably do genuinely useful work. Point it on the more common reality, where half the story lives in inboxes and side conversations slightly than within the system, and it amplifies the gaps with the identical enthusiasm. The disappointing pilot is nearly at all times an information problem. The feedback a hiring manager typed in a rush between meetings, the scorecard that claims \u201cstrong\u201d but not why, the quiet debate about two final candidates that happened in a hallway and never made it into the record: that\u2019s the fabric a useful AI must reason over. In most organizations, little or no of it actually lands within the system. The fix is getting the entire organization genuinely working in a single system first. Skip that and no AI tool closes the gap.<\/p>\n The confusion starts because most vendor pitches fold each under the word \u201cAI,\u201d which makes it almost inconceivable to think clearly about what you really need. Here\u2019s the excellence I find useful. Walk through any hiring process and you’ll be able to sort almost every task into one in all two buckets. The primary is predictable work: every applicant gets a reply, every interview gets confirmed, every SLA gets watched, the hiring manager who hasn\u2019t left feedback after every week gets a nudge. There\u2019s no judgment in any of this. The fitting answer is understood prematurely, and the one thing that ever goes improper is that an individual was too busy to do it consistently. The second bucket is judgment work: reading a resume and understanding what it\u2019s actually telling you, summarizing an interview all the way down to the three things that mattered, reasoning a few shortlist where two finalists are strong in completely other ways.<\/p>\n Those are different jobs, and so they need different tools. Rules-based automation is reliable precisely since it\u2019s rigid: give it a consistent input and it returns a consistent output, the identical way, each time. That rigidity is precisely what you wish for the predictable bucket. You don\u2019t desire a system that gets creative about whether to answer to a candidate. You wish it to answer, each time, identically. AI is beneficial for the alternative reason. It handles the judgment bucket well since it isn\u2019t rigid: hand it a messy, varied input and it might probably reason about it and provide you with something value acting on. The identical flexibility that makes it improper for \u201cdid the confirmation email send\u201d makes it right for \u201cwhat is that this resume really telling me.\u201d Most buyers walk into the market asking which platform has the most effective AI. The higher query is which platform handles each sorts of work, and points each on the tool actually built for it.<\/p>\n Most ATS evaluations start from the identical query: which one has the most effective features? You line up the demos, rating the capabilities, and pick a winner. The difficulty is that \u201cbest\u201d almost at all times means best on the one shape of hiring the demo focused on, often mid-career corporate recruiting, which is precisely how you find yourself with a system that\u2019s excellent for a part of the organization and a growing fan of spreadsheets and workarounds for every part else. Inside two or three years, the identical team that championed the system is quietly routing around it. No one decided to desert it. It just stopped fitting, a little bit at a time, until someday someone said out loud what everyone already felt.<\/p>\n The query most buyers don\u2019t ask is less complicated and more vital: will this still fit us in three years? Organizations change. They open recent regions, acquire brands, and add recent shapes of hiring they didn\u2019t anticipate once they went live. Most systems get configured once at implementation and left. The organization keeps moving; the configuration doesn\u2019t. The gap opens slowly, and 12 months three is roughly when it gets wide enough to feel. So before you sign anything, push on two things. Can this technique evolve because the organization does? And is there someone on the seller\u2019s side whose actual job, long after implementation is finished, is to aid you keep it current? If the honest answer to either is not any, that\u2019s the thing to repair in the following decision. Select for the last decade, not the demo.<\/p>\n Pinpoint Demo\u00a0<\/a><\/p>\n5 Questions With Tom Hacquoil\u00a0<\/h2>\n
Pinpoint is built around what you call \u201cmulti-stream hiring.\u201d Are you able to explain what which means, and why it changes how organizations should take into consideration their hiring tech?<\/h3>\n
There\u2019s real anxiety within the TA world straight away about AI and what it means for recruiting jobs. What\u2019s your actual view, and is that fear well-founded?<\/h3>\n
A whole lot of talent teams ran their first real AI experiment within the last 12 months or two and got here away disenchanted. What actually went improper?<\/h3>\n
We hear lots about automation and AI. What\u2019s the excellence between the 2, why does it matter, and what should TA leaders actually be using each for?<\/h3>\n
In the event you\u2019re a talent leader buying an ATS in 2026, what\u2019s the one query you\u2019re not asking that try to be?<\/h3>\n
RESOURCES<\/h2>\n