7 proven steps to choosing the right software for your team

A seven-step process for choosing tools: keep workflows from breaking, prevent fragmented data and workaround processes, and stop your architecture becoming a tangled mess.

Tool stack
August 18, 2026
·
9 min
read
Abstract brand form on a purple field

TL;DR

  • Kickoff your tool investigation and selection by first writing it all down: what kind of jobs is the tool for, what the budget range is, who needs seats, what it has to connect to, and what could disqualify it.
  • Check what the company already owns. Another team may have a tool that covers your workflow, or a feature nobody ever switched on.
  • Build the team: the budget approver, the workflow owner, the people who run the systems it plugs into, anyone whose tool overlaps, and the functions that can block the purchase.
  • Read the online complaints as carefully as the ratings. The complaints tell you what you’ll be dealing with after purchase.
  • Test with your own messy data and your own permissions model.
  • Buy short-term where you can, do vendor and contract diligence before you commit, and keep track of the entire process, including who authorized which decisions.

Introduction

In the old days, which somehow means 2023 and before, people who worked with me often thought I had an unhealthy tool obsession.

They weren’t wrong about the obsession. But it actually has proven to be a huge asset for me. That obsession is one of the reasons I became so good at competitive intelligence, tool research, and solution evaluation.

The paradox of choice keeps getting more extreme. None of this scares or overwhelms me though because this obsession means I frequently go window shopping, and record my discoveries and learnings for a rainy day (i.e. in my tool directory).

Unfortunately, for most companies the paradox of choice becomes more overwhelming than ever, with an invisible pile of tools with unclear ownership, overlapping use cases, and weak return on investment (ROI). This is known as tool sprawl.

What is tool sprawl?

Zylo’s 2025 SaaS Management Index found that organizations waste an average of $21M a year on unused Software as a Service (SaaS) licenses, a 14.2% increase year over year. Zylo also puts 70% of SaaS spend with lines of business, against IT’s 26.1%, which explains the first number. When most buying happens outside the one function that can see the whole stack, five teams buy five tools for one job and nobody reconciles them.

Tool sprawl is exactly that problem: what happens when teams keep adding software without a clean view of what already exists, what overlaps, what’s still used, what’s integrated, and what should be retired.

CIO’s Tech Talk community found that 95% of respondents were planning to consolidate vendors in the next 12 months, with 83% citing moderate to high pressure to do so. What IT executives are saying about vendor consolidation | CIO

So when you need to choose a tool, or recommend one, how do you avoid feeding the tool-sprawl machine and still help your team work better? Let's get into it.

One important caveat before the steps: this is not always a clean, sequential process. Some steps will happen in parallel, and your company may have an official procurement, security, finance, or legal process that changes the order, adds gates, or requires you to document things in a specific way. The point is to make sure each question gets answered before the decision becomes expensive to reverse.

Step 1: Define the goals of your tool search & get to know the landscape

Start with the work you’re trying to change.

What’s broken? What’s slow? What’s being done manually? What information keeps getting copied from one system into another? What are people solving with side documents, Slack messages, spreadsheets, screenshots, exports, and the ten other tiny workarounds that usually mean a workflow has outgrown the system around it?

Write that down first.

Then write the job in plain language. The actual job. The thing the tool needs to make easier, faster, safer, or more measurable.

For most tool searches, I’d define ten things before I talk to anyone selling anything:

  • Job to be done (JTBD)
  • Business need
  • Total cost
  • Customer success and support
  • Team size and permissions
  • Scaling
  • Integrations and APIs
  • Compliance and risk
  • Ownership and adoption
  • Team expertise

The question behind each one, and why it matters, is in the whitepaper.

Start a spreadsheet

You're not going to finish this work before you start the next step. Steps will overlap and you'll need a place that is organized and updated on a rolling basis. Start a spreadsheet and add your requirements and tools as you work.

Now learn the landscape

Look around. Window shop. Read the websites. Check the comparison pages. Look at the promises vendors repeat until they start sounding normal. Keep translating everything back into your own words: does this solve the problem I wrote down?

What could actually answer the problem?

Categories are vendor language. They’re useful for finding tools, but they don’t map neatly to the work people are actually trying to do.

Put every serious option against the same problem, the same must-haves, the same integration requirements, the same ownership question, and the same risk constraints.

For a marketing or content team, some problems might look like this:

  • Customers can’t find accurate answers
  • Sales uses outdated content
  • Teams duplicate research
  • Marketing needs campaign visibility
  • Product updates don’t reach users

The tools you're checking depends on the workflow of course. If two tools from different categories solve the same problem, compare them. If two tools from the same category solve different problems, then they aren't really alternatives after all.

The “all-in-one” promise usually breaks down here.

I’ve come to the conclusion that the true all-in-one tool doesn’t exist. Not really. The promise that one platform will solve tool sprawl by replacing everything else is mostly marketing.

Requirements you have to pay attention to

Ask whether the tool fits into the system you already have, and whether your team has the resources to make that fit work. Resources could be the budget to hire an external team, available in-house manpower, or simpler integrations. And those are just some of the possibilities. Integrations belong in the must-haves.

“We’ll build the connector later” is how tools move to the graveyard fast (but you keep paying for them anyway).

You'll also need to track information that isn't always publicly available. Remember to ask about these points when you meet with the Sales reps for each tool:

  • Customer success and customer support. Check whether the vendor will help you implement, troubleshoot, train users, and resolve issues quickly. Bad support turns a good tool into a liability.
  • Ease of use. Pick a tool your team can learn fast. A powerful tool with a confusing interface still creates adoption risk.
  • Analytics. Look for clear reports on ROI, traffic, adoption, conversion, or the metric that proves the tool is doing its job.

Step 2: Investigate what already exists

Businesses have to enable flexibility, because different teams do different work. But flexibility doesn’t mean every team starts from zero. Adding is politically easy and structurally expensive. Every addition means more integration work, more access management, more renewal tracking, and more places for the same information to diverge.

If you’re a Director, Manager, team lead, product marketing manager (PMM), technical writer, customer success manager (CSM), marketer, analyst, designer, or anyone else trying to solve a local team problem, check what already exists:

  • what your own team has
  • what adjacent teams use
  • whether another function entirely already has a tool that could also serve your team

Start with a lightweight investigation:

  • Search your internal wiki, intranet, shared drive, and project management tool for anything you already pay for that might solve this.
  • Ask IT, Operations, Finance, or RevOps whether the company already pays for something adjacent.
  • Check whether a tool you already have covers this through a feature nobody ever switched on.
  • Pull the existing contracts, renewal dates, approved vendor lists, and security review rules.
  • Name who owns each of those tools today, and who would own a new one after purchase if your team stopped using it.
Organizations are wasting an average of $21M annually on unused SaaS licenses, a 14.2% increase year-over-year.

When I needed to choose an in-app communication widget for a product at one organization, I already knew our customer success tool offered a feature in the same competitive area. I didn’t think it fulfilled our defined requirements, but it still belonged in the comparison. Existing vendors don’t automatically win, but they do get evaluated before you add another vendor, another contract, and another tool your team has to maintain.

Step 3: Build the team that makes the decision with you

You’re not going to make this call alone.

Somebody controls the budget. Somebody owns the workflow. Somebody owns the system this has to plug into. Somebody may own a tool that already overlaps with the one you want to buy. Somebody in Security, Legal, Privacy, Finance, Procurement, or IT may need to approve the purchase before anyone signs anything.

Bring those people in now. I usually think about five groups:

  • The approver. Controls the budget line. Give them the cost, what it replaces, and what happens if you do nothing.
  • The owner. Owns the workflow, team, or outcome the tool is supposed to improve. If that isn’t you, they need to be part of the decision before the shortlist gets serious.
  • The system and integration people. This includes the system owner, the integrators, internal tech support, and anyone responsible for keeping connected tools working after launch. They can tell you whether the integration is real, supported, secure, and something the company can maintain.
  • The overlapping-tool owners. These are the people who own adjacent or competing tools already inside the company. Bring them in before you buy something their stack can already do, almost do, or should replace.
  • The required approvers. Security, Legal, Privacy, Finance, Procurement, IT, and any other function that can block the purchase or restrict how you use the tool. They are not obstacles. They are constraints you need early.

Before you finish this step, write down who needs to be involved, what each person has to answer, and where their input matters in the process. A decision with four silent participants has no owner, and in twelve months whoever happens to be in the room will handle the renewal. My recommendation: use a RACI for guidance.

Step 4: Read the ratings and the complaints

Read the complaints and you learn what kind of pain you’re buying.

Ratings are useful, but only if you separate praise from pain. A high average score can hide the exact failure mode that matters to your team.

Look for complaints about support quality, implementation friction, reporting gaps, renewal process, export limits, and whether the product works once real permissions, messy data, and cross-functional ownership enter the picture.

Step 5: Meet the vendors

By the time you reach this step, you probably already have a shortlist and you should only be setting up meetings with about three vendors for your top three choices. If you're not sure about an already existing internal tool, then that vendor should be one of the ones you meet with.

Bring your requirements to the call, along with the people from your team who are relevant. Your system owner will catch things in a demo that you won't, and the required approvers can rule something out on the spot instead of after you've committed.

Step 6: Test with your own data

Your work is real. Demo data isn’t.

Test before you choose, using your own content, permissions model, naming conventions, data quality, and at least one person who wasn’t in the buying room. Real data makes the test harder to fake, because your team will notice the weird fields, missing context, and permission problems the demo never had to handle.

Run the trial with data that you actually would be working on:

  • Import a real messy file.
  • Connect the system it has to integrate with.
  • Ask a non-admin user to complete the workflow.
  • Check whether permissions behave the way your organization needs.
  • Export the data and see what comes out.
  • Break the workflow on purpose and see how hard recovery is.

A tool that only works under ideal conditions will create work for the team later.

Step 7: Buy a month before you buy forever

Where you can, sign up for a single month first and commit once the tool proves it belongs.

Monthly pricing can be worth the premium while the decision is still open. You keep your exit cheap, and you avoid the psychological trap of making a tool work because the annual contract already started.

This is also where the final vendor and contract diligence belongs. Before you ask for budget, commit to an annual plan, or send anything into procurement, check:

  • Vendor viability. Who owns the company, how it’s funded, and whether the product you’re buying is still on the roadmap. Ask directly what happens to your data if somebody acquires them.
  • Security posture. Ask for the current SOC 2 or ISO 27001 report, the subprocessor list, and the breach notification terms. Read the subprocessor list, because your data may move through more companies than the one on the invoice.
  • Data and AI terms. Where your data is stored, who can access it, and whether the vendor trains models on your content. For an AI tool, get that answer in writing in the contract.
  • Exit terms. Auto-renewal windows, notice periods, price escalators, and what you actually get back when you export. Ask for a sample export file before you sign.
  • References. Talk to a customer at your size, in your market, and ask them what went wrong.

Key takeaways

The real takeaway is simple: don’t let the market, the demo, or the loudest stakeholder define the problem for you.

Every step here exists to keep that definition in your hands, from the moment you write the job down to the moment somebody signs. The vendor will happily do the defining for you, and the version they hand back will fit their product.

Free whitepaper

The full seven steps, with both comparison tables

Every criterion, the question behind it, and why it matters, plus the problem-to-solution-path table, the full vendor and contract diligence list, and the one-page approval summary. Fifteen pages, no form to fill in.

Download the detailed guide (PDF)

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data-augmentation
Data Augmentation
Creating extra training examples by altering existing ones, such as flipping, cropping, rotating or recolouring images, so the model sees more variety.
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workflow
Workflow
The ordered steps and hand-offs by which work moves from start to finish, who does each one and what triggers the next.
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verification
Verification
Confirming that a result, label or component is correct by checking it against a reference or a second method.
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vectorized-execution
Vectorized Execution
A query-engine technique that processes whole columns of values in one CPU instruction instead of row by row, which is what makes engines like DuckDB fast.
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vector-space
Vector Space
The multi-dimensional space where embeddings live; distance within it stands for how similar two items are.
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uncertainty-sampling
Uncertainty Sampling
An active-learning strategy that sends the examples the model is least sure about to human reviewers first, because those teach it the most.
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transformer
Transformer
A neural network architecture built on attention, which weighs every part of the input against every other part; it underpins modern language and vision models.
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traceability
Traceability
The ability to follow any output back through the data, labels, code and model versions that produced it.
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tokenization
Tokenization
Splitting text into the units a language model works with, which may be words, parts of words or characters.
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token-limit
Token Limit
The maximum number of tokens a language model can take in and produce in one request, which caps how much context it can hold.
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threading
Threading
Running several parts of a program at the same time on separate threads so work overlaps instead of waiting in line.
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temporal-analysis
Temporal Analysis
Studying how things change over time across frames or records, to detect motion, events, trends or anomalies.
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sla
SLA
Service Level Agreement: the committed level of service a provider promises, such as uptime, response time or support hours, with consequences if missed.
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simulation
Simulation
Generating realistic scenarios in software to test or train a system where real data is scarce, risky or expensive to collect.
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similarity-search-vector-search
Similarity Search (Vector Search)
Finding the items whose embeddings sit closest to a query's embedding, which returns things that look or mean alike rather than share keywords.
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setup
Setup
The initial configuration of a project: connecting data sources, choosing models and settings, and defining the workflow before work begins.
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semi-supervised-learning
Semi-Supervised Learning
Training on a small labelled set plus a much larger unlabelled one, using the labelled examples to guide what the model learns from the rest.
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semantics
Semantics
The meaning behind data rather than its surface form, which lets a system treat "car" and "automobile", or two photos of one scene, as the same thing.
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segmentation
Segmentation
Dividing an image into regions by labelling every pixel, so the exact shape of each object or area is known.
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search-attributes
Search Attributes
The fields a search can filter or rank on, such as labels, tags, dates, camera id or model confidence.
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search-and-retrieval
Search & Retrieval
Finding the items in a collection that best match a query, whether by keyword, metadata or visual and semantic similarity.
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scene-understanding
Scene Understanding
Interpreting a whole image or video as a scene: what objects are present, where they are and how they relate.
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saliency-map
Saliency Map
A heat map over an image showing which pixels most influenced a model's prediction, used to explain and debug it.
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reward-function
Reward Function
In reinforcement learning, the rule that scores each action so the agent learns which behaviour to repeat.
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review-batch
Review Batch
A set of items grouped for a human reviewer to check in one sitting, often chosen because the model was unsure about them.
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relational-database
Relational Database
A database that stores data in tables with rows and columns and links them through keys, queried with SQL.
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regularization
Regularization
Techniques that stop a model memorising its training data, such as penalising large weights or dropping random neurons, so it generalises better.
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refactoring
Refactoring
Restructuring existing code to make it cleaner and easier to change without altering what it does.
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recipe
Recipe
A saved, repeatable set of processing steps or settings that can be rerun on new data to get the same kind of result.
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rag
RAG
Retrieval-Augmented Generation: a language model that first fetches relevant documents and then writes its answer from them, which cuts hallucination.
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quality-assurance-qa
Quality Assurance (QA)
The checks and processes that make sure a product, dataset or model meets its standard before it ships.
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predictive-maintenance
Predictive Maintenance
Using sensor data and models to forecast when equipment will fail, so it is serviced just before it does rather than on a fixed schedule or after a breakdown.
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policy
Policy
In reinforcement learning, the strategy that maps each state to the action the agent should take.
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pixel
Pixel
The smallest unit of a digital image, a single point with a colour or intensity value; images are grids of them.
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pgvector
pgvector
A PostgreSQL extension that stores embeddings and runs similarity search inside the database, so vector search needs no separate system.
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one-shot-learning
One-shot Learning
Learning to recognise a new class from a single example, usually by comparing against a learned notion of similarity.
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object-storage
Object Storage
Storage that keeps files as objects with metadata in flat buckets rather than folders, built for huge volumes and cheap durability.
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normalization
Normalization
Rescaling values to a common range or distribution so no single feature dominates and models train more stably.
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noise
Noise
Random or irrelevant variation in data that hides the real signal, from sensor error, compression, lighting or sloppy labels.
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mvcc
MVCC
Multi-Version Concurrency Control: a database technique that keeps multiple versions of a row so readers and writers do not block each other.
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monitoring
Monitoring
Continuously watching a deployed system's health and output quality so drift, errors and slowdowns are caught early.
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model-versioning
Model Versioning
Tracking each trained model with its data, code and settings, so any version can be reproduced, compared or rolled back.
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metrics
Metrics
The numbers used to judge a model or system: accuracy, precision, recall, F1, latency and others chosen to match the goal.
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metric-learning
Metric Learning
Training a model so that similar items end up close together in its embedding space and dissimilar ones far apart.
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meta-learning
Meta-learning
Training a model to learn how to learn, so it adapts to a new task from very few examples.
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mask
Mask
A pixel-level map marking exactly which pixels belong to an object or region, the output of segmentation.
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markov-decision-process-mdp
Markov Decision Process (MDP)
The mathematical frame for reinforcement learning: states, actions, rewards and transition probabilities where the next state depends only on the current one.
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loss-function
Loss Function
The formula that scores how wrong a model's predictions are during training; training works to make this number as small as possible.
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latent-feature
Latent Feature
A hidden characteristic a model learns internally that is not directly observed in the data but helps explain it.
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label-noise
Label Noise
Errors and inconsistencies in labels, from mistakes, ambiguity or disagreeing annotators, which degrade what a model can learn.
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keypoint-detection
Keypoint Detection
Locating specific points of interest on an object, such as joints on a body or corners on a part, rather than drawing a box around the whole thing.
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iou
IOU
Intersection over Union: the overlap between a predicted box or mask and the true one, divided by their combined area. 1.0 is a perfect match.
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inverted-index
Inverted Index
An index that maps each term to the documents containing it, which is how full-text search answers queries quickly.
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instance-object-instance
Instance (Object instance)
One individual occurrence of an object in an image, as distinct from the class it belongs to.
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information-extraction
Information Extraction
Pulling structured facts such as names, dates, amounts or relationships out of unstructured text or images.
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indexing
Indexing
Building a lookup structure over data so searches find matches without scanning everything.
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image-processing
Image Processing
Operations applied to pixels, such as resizing, filtering, thresholding or colour correction, usually to prepare images for analysis.
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hyperparameter-selection
Hyperparameter selection
Choosing the settings that control how a model learns, such as learning rate, batch size or network depth, which are set before training rather than learned.
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grid-search
Grid search
Trying every combination of chosen hyperparameter values in turn and keeping the set that scores best.
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gradient
Gradient
The direction and size of change in a model's error for each weight, used during training to decide how to adjust that weight.
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gpu
GPU
Graphics Processing Unit: a chip built for many parallel calculations at once, which makes it the workhorse for training and running neural networks.
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generative-ai
Generative AI
AI that creates new content such as text, images, code or audio from a prompt, learned from patterns in large training sets.
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generalization
Generalization
A model's ability to perform well on data it has never seen, rather than only on its training examples.
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feedback-loop
Feedback Loop
A cycle in which a system's outputs, or human reactions to them, are fed back as training signal so it improves over time.
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feature-map
Feature Map
The output of one layer in a convolutional network: a grid showing where in the image a particular pattern was detected.
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feature-engineering
Feature Engineering
Creating or transforming input variables so a model can learn from them more easily, using domain knowledge rather than raw data alone.
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fairness
Fairness
A model behaving without systematic disadvantage to groups defined by traits such as gender, age or region.
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exploration-vs-exploitation
Exploration vs. Exploitation
The trade-off in learning systems between trying new actions to discover better options and repeating actions already known to work.
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evaluation
Evaluation
Measuring how well a model performs against held-out data using agreed metrics, before and after it is deployed.
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efficiency
Efficiency
Getting the same or better result with less time, compute, money or manual effort.
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ec2
EC2
Amazon Elastic Compute Cloud: AWS's service for renting virtual servers by the hour, including GPU machines for training.
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domain-shift
Domain Shift
When the data a model meets in production differs from the data it was trained on, so accuracy drops even though nothing in the model changed.
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dml
DML
Data Manipulation Language: the SQL commands that read and change rows, such as SELECT, INSERT, UPDATE and DELETE.
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dino
DINO
A self-supervised vision method from Meta that learns strong image features without labels by teaching a network to match different views of the same image.
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ddl
DDL
Data Definition Language: the SQL commands that create and change the structure of a database, such as CREATE TABLE and ALTER.
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database-reliability
Database Reliability
A database's ability to stay available, correct and recoverable under load, failure and change.
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data-quality
Data Quality
How fit data is for its purpose: accurate, complete, consistent, current and free of duplicates, noise and wrong labels.
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data-lineage
Data Lineage
The record of where data came from and every transformation it went through, so any result can be traced back to its source.
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data-leakage
Data Leakage
When information from the test set, or from the future, slips into training, so the model scores well in the lab and fails in production.
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data-lake
Data Lake
A store that keeps raw data in its original format at any scale, structured or not, for later processing and analysis.
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data-integrity
Data Integrity
Data that is complete, accurate and unaltered over its whole life, protected from corruption, loss or unauthorised change.
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data-ingestion
Data Ingestion
Bringing data from its sources into a system where it can be stored, processed and analysed, whether in batches or as a live stream.
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data-governance
Data Governance
The policies, roles and controls that decide who can access data, how it is kept accurate and how its use is tracked.
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data-consistency
Data Consistency
Data that stays correct and in agreement across copies, transactions and systems, so every reader sees the same truth.
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data-balance
Data Balance
How evenly examples are spread across classes or conditions in a dataset; imbalance teaches a model to favour whatever it sees most.
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cross-validation
Cross-validation
Evaluating a model by splitting the data into several folds, training on some and testing on the rest in turn, to get a more honest accuracy estimate.
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cosine-similarity
Cosine Similarity
A score between -1 and 1 for how closely two vectors point in the same direction, used to compare embeddings regardless of their length.
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convolutional-neural-network-cnn
Convolutional Neural Network (CNN)
A neural network built for images: it slides small filters over the picture to detect edges, textures and shapes, then combines them into higher-level features.
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connected-components
Connected Components
Groups of items linked by similarity so that every member connects to the group through at least one close neighbour, used to find clusters and duplicates.
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compliance
Compliance
Meeting the laws, standards and internal policies that govern how data is collected, stored, used and audited.
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collaboration
Collaboration
People and teams working on shared data, models and decisions with a common view of the work, rather than in separate tools and silos.
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cloud-native
Cloud Native
Software designed from the start to run in the cloud, typically built as containers and services that scale and recover automatically.
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cloud-computing
Cloud Computing
Renting computing resources such as servers, storage and databases over the internet, scaling up or down on demand instead of owning hardware.
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cloud-ai
Cloud AI
Running AI training or inference on a provider's infrastructure rather than your own hardware, paying for compute as you use it.
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clip
CLIP
A model from OpenAI that learns images and text in one shared space, so a sentence can be matched to pictures it describes without task-specific training.
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caching
Caching
Keeping a copy of frequently used data in fast storage so later requests are served without recomputing or refetching it.
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buy-vs-build
Buy vs Build
The decision between purchasing an existing product and developing your own, weighing speed, cost, control and long-term maintenance.
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business-value
Business Value
The measurable benefit a project delivers to the organisation: revenue, cost saved, risk reduced or time recovered.
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black-box-model
Black-box Model
A model whose internal reasoning cannot be inspected or explained, so you can see what it predicts but not why.
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big-data
Big Data
Data too large, fast or varied for traditional tools to store and analyse, which calls for distributed storage and processing.
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