• How Kantata is Helping Businesses Unleash the Power of Professional Services

    How Kantata is Helping Businesses Unleash the Power of Professional Services

    Powerful currents are reshaping the professional services industry. Only the best providers of Professional Services Automation (PSA) software are able to equip their clients with the solutions and expertise they need to stay ahead of the curve. The vendors that lead the charge into a new era of PSA will have the specialized focus and domain expertise to understand the evolving needs of professional services organizations, as well as the scale to turn those needs into ground-breaking solutions.

    That’s why Kimble and Mavenlink have joined forces to form Kantata, the #1 provider of PSA solutions according to SoftwareReviews’ Professional Services Automation Emotional Footprint Grid. In this blog, which accompanies our new report  “Unleash the Power of Professional Services with Purpose-Built Software,” learn more about why users say Kantata is uniquely able to drive transformational outcomes for their business, as well as how adopting the Kantata Professional Services Cloud can unleash the power of your professional services workforce. 

    How Does SoftwareReviews Determine Its Emotional Footprint Grid Champion?

     

    SoftwareReviews evaluates 27 aspects of the customer relationship using a net promoter methodology. These ratings include provocative, detailed questions on the experience of working with the vendor, creating a powerful indicator of overall user sentiment for their Emotional Footprint rating.

    Kantata was placed at the very top of the list of 13 PSA providers SoftwareReviews evaluated, earning Emotional Footprint Champion status with a 9.3 overall Customer Experience Score. Kantata also led the category with a +93 Net Emotional Footprint score, showing incredible performance across all aspects of the customer relationship.

    What Are Customers Saying About Kantata?

     

    Through SoftwareReviews’ evaluation, Kantata was shown to empower users and administrators, who are delighted with the support they receive. According to SoftwareReviews user review data,100% of users agree that:

    • Kantata is critical to their success
    • Kantata enables them to increase their performance
    • They love using Kantata

    As a purpose-built industry cloud designed for specific needs of professional services organizations, Kantata’s unique breadth and singular focus on the professional services industry puts it at the vanguard of innovation in the PSA category. From the introduction of Talent Network capabilities that transform how services businesses work with their external workforce, to ground-breaking innovations on the Salesforce platform that bring information in Sales Cloud, Revenue Cloud, Tableau, Slack, and so much more, Kantata is leading the way and unlocking transformative opportunities for its clients.

    SoftwareReviews’ results confirm Kantata’s role as a leader in the PSA category, with high marks for continuous improvement of their products, their capacity to help clients innovate, and ultimately their ability to inspire clients to reach new heights. This has enabled Kantata to exceed customer expectations by delivering exceptional value through a unique and comprehensive portfolio of industry-focused products and capabilities across multiple platforms. With the Kantata Professional Services Cloud, a better, faster, more efficient way of doing business is possible today.

    Unlocking Sustainable Growth and Long-Term Success With Kantata

     

    The professional services industry is rapidly changing, and the technology needs of professional services organizations are changing just as quickly – leading professional services teams across the globe are working with Kantata to stay ahead of the curve. With Kantata, today’s businesses can give their teams the clarity, control, and confidence they need to make true, lasting success in the constantly evolving professional services industry possible. What could adopting the Kantata Cloud mean for your professional services organization?

    Learn more about what’s possible with Kantata in our new report, “Unleash the Power of Professional Services with Purpose-Built Software,” which highlights key insights derived from the SoftwareReviews’ Professional Services Automation Emotional Footprint Report.

  • The Impact of Shifting Talent Pools on a Changing Labor Market

    The Impact of Shifting Talent Pools on a Changing Labor Market

    On the latest episode of Kantata’s Professional Services Pursuit Podcast, our hosts Brent Trimble and Banoo Behboodi discuss how the professional services talent pool is shifting, both in terms of what employees expect from the organizations they work for and the values they want to see when applying to work at a new company. This episode highlights recent research by McKinsey & Company, which reveals more and more individuals are leaning towards contract work or running their own business as opposed to taking a more traditional career path. This blog is a snapshot of some of the key themes of the episode, focusing on the three main drivers behind the shifting labor market. You can listen to the entire 30-minute episode or read the transcript here.

    Why is the Talent Pool Shifting?

    In reference to what McKinsey calls the “The Great Attrition,” co-host Brent Trimble lays out a few reasons behind the current state of the labor market and why businesses are experiencing such a high degree of change. One reason is that individuals in the workforce are reshuffling — they’re not only leaving their current position, they’re leaving their current industry entirely and starting a brand new career path. The second reason behind the shifting labor pool is the gravitational pull of temporary, contract, or consultant-like work, which is pulling people away from full-time employee roles

    1. More Flexibility

      When it comes to how employees think about flexibility, co-host Banoo Behboodi says, “ultimately they’re doing what they’re doing because they’re looking for either more flexibility, more meaningful work, compensation or a healthier work-life balance…we seem to have stepped into a completely revolutionized way of looking at what workplace flexibility means.” Working remotely has become the norm, with talent spread across locations, time zones, and even countries. Employees want flexible schedules and options outside of the traditional expectations of commuting to an office and reporting Monday through Friday, nine to five.

    2. Healthy Work-Life Balance

      Core to the change in the labor market is a fundamental shift in how people approach a healthy work-life balance. People want to be able to both work and take care of responsibilities at home without the fear of burn out. Brent refers to this evolution as people “reassessing the demands of life…re-evaluating and maybe looking at all this time that traditionally they would have spent either commuting or on business travel and now saying, let’s fill this with something else.” Being able to work not just remotely, but work for yourself or on a contract-basis gives individuals some time back that they did not have before. They are looking for jobs that allow them to have a healthy and balanced life outside of the “office.”

    3. Opportunities for Career Advancement

      In today’s market, individuals expect the opportunity to learn, evolve, and advance within an organization. Banoo points out that “one of the top motivators is career development and advancement and what companies are doing for them from that perspective…There should be paths for individuals that are comfortable being individual contributors to also make career advancements.” In McKinsey’s research, 41% of individuals being surveyed chose to leave their job due to lack of career advancement opportunities. You have to be willing and eager to invest in your employees if you would like to attract and retain top talent.

    How to Adapt to the Changing Labor Market?

    Nearly all industries are feeling the impact of the shifting labor market. While the task of adapting to new expectations and shifting values may seem daunting, Brent provides three suggestions for where to start.

    1. Sharpen your traditional employee value proposition and provide career development options.
    2. Build a non-traditional value proposition — flexibility, personal relationships between employees, positive and engaging culture, and different forms of career progression — and make it more personal.
    3. Broaden your talent sourcing approach, looking at people with non-traditional backgrounds.
    Want to Learn More?

    If you’re interested in learning more, you can listen to the entire 30-minute episode or read the transcript at this link. Subscribe to the The Professional Services Pursuit Podcast for expert advice, trends, and best practices surrounding professional services.

  • 5 Elements Every Project Proposal Should Include

    5 Elements Every Project Proposal Should Include

    If you’re working on writing a project proposal for a potential client or customer, you may be wondering what—exactly—your project proposal should include. While your actual project proposal will depend on the type of project, there are several key sections that every project proposal should include.

    Don't let these common project management failures stop you.
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    If you’re submitting a project proposal from an RFP (request for proposal), the request will typically specify the information, format, and submission requirements you need to follow. It’s important to review these requirements carefully. Failing to meet mandatory criteria may lead to your proposal being rejected.

    For proposals that aren’t tied to a specific RFP format, the following five-element framework covers the essentials of presenting a project opportunity, from defining the client’s needs and expected outcomes to outlining the scope, execution plan, and next steps. Because unlike an RFP response, which follows requirements established by the prospective client, an unsolicited project proposal provides greater flexibility to structure your recommendations around the client’s challenges, objectives, and expected outcomes.

    Depending on the project’s complexity and client requirements, additional sections such as an executive summary, relevant team credentials, or commercial terms may also be needed. While a proposal outlines the proposed approach, more detailed delivery requirements and contractual obligations can be documented in a statement of work (SOW) or contract, as appropriate.

    1. Project Background

    The project background is a persuasive element of your proposal. It reinforces the need for the project while introducing how you’ll be able to help attain the client’s goals. This section should explain the challenges or opportunities driving the project, the potential consequences of leaving them unaddressed, and the outcomes the proposed project aims to deliver.

    A useful way to structure the project background is to connect three elements: the current problem, the cost of inaction, and the proposed outcome.

    For example, an IT consulting firm proposing a systems integration project might highlight how disconnected business applications lead to duplicate data entry and time-consuming manual processes. Without integration, these inefficiencies may continue to affect productivity and data consistency. The proposed project would aim to streamline data flows between systems, reduce manual work, and improve access to reliable business information.

    2. Project Objectives

    The objectives in a project proposal communicate the desired outcomes of the project, whether these outcomes are deliverables or results. This part of your project proposal serves not only as an acknowledgment that you fully understand the goals of the project, but also acts as a guide for your own project planning. Consider these objectives the targets that every stage of your project should be aiming toward achieving.

    To make project objectives more actionable, connect each objective to a specific deliverable and a measurable success criterion. This helps align client expectations with the work required to achieve the desired outcomes, while providing a basis for evaluating project success.

    For example, an IT consulting firm proposing a systems implementation project might define its objectives as follows:

    Project Objective Deliverable Success Measure
    Improve access to customer information Centralized customer relationship management (CRM) system Authorized users can access customer records through the new system
    Reduce manual data entry Integration between the CRM and existing business applications Customer records synchronize successfully between the agreed systems
    Improve employee adoption User training and onboarding materials At least 90% of designated users complete training before launch

    These examples are illustrative. The specific deliverables and success criteria should be agreed upon with the client based on the project’s requirements.

    3. Project Scope

    The project scope includes details of what the project will entail. This is where you want to be especially specific, as this sets the direction for the project expectations that will be agreed upon when you write and sign the contract. The scope is usually one of the longer parts of a project proposal. It lets the client know what to expect as well as helps to establish you or your company as the right choice to complete the project.

    In the project scope, you should include:

    • Key deliverables and what each entails: Clearly define the outputs the client will receive. For example, an IT consulting project might include configuring a CRM system, migrating customer records, and providing user training.
    • Points of measurement or review: Identify when deliverables will be evaluated or approved. For example, the client may review and approve the CRM configuration before data migration begins.
    • Known risks and how they will be handled: Identify potential challenges and outline mitigation plans. For example, incomplete or inconsistent customer records may require data validation before migration.
    • Project exclusions, if any need to be defined: Specify what is not included in the agreed scope. For example, the CRM implementation may exclude integrations with third-party applications unless explicitly listed as deliverables.
    • Assumptions: Document the conditions the project plan depends on. For example, the client is expected to provide access to existing systems and required data before implementation begins.
    • Constraints: Define limitations that may affect delivery. For example, the implementation may need to be completed within an approved budget or scheduled around the client’s operational commitments.

    Clearly defined exclusions are particularly important for managing scope creep. When additional work falls outside the agreed scope, documented exclusions help teams identify changes early, assess their impact on timelines and budgets, and determine whether a formal change request is required.

    4. Plan, Timeline, & Budget

    This is the part of the project proposal where you lay out exactly how the project described in the project scope will be completed. Take the time to do this section right, as it will define the proposed timeline and budget requirements for the project. It’s important to go into this section knowing what resources you have available, what their availability looks like, and their costs, as well as a projection for any materials required to complete the project.

    A more accurate project budget starts with understanding the resources required to complete the work. Begin by identifying the roles and skills needed for each project phase, estimating the hours required, and checking resource availability against existing project commitments. Multiplying the estimated hours for each role by its hourly cost rate provides a starting point for calculating labor costs. Applying the appropriate bill rates helps determine the proposed fees and evaluate the project’s expected profit margin.

    Resource availability and skills are equally important when developing these estimates. Assigning work to employees who lack the required expertise may increase delivery time and costs, while relying on already over-allocated team members can create scheduling conflicts. Factoring in capacity and skills during the proposal stage helps professional services firms develop more realistic budgets and delivery timelines.

    Example: Estimating the Budget for an IT Consulting Project

    Consider an IT consulting firm preparing a proposal for a CRM implementation project. Before finalizing the budget, the firm estimates the hours required from each role and applies the corresponding internal cost rates.

    The following example illustrates how these estimates can be calculated:

    Project Role Estimated Hours Hourly Cost Rate Estimated Labor Cost
    Project Manager 40 $75 $3,000
    CRM Consultant 100 $90 $9,000
    Integration Specialist 60 $100 $6,000
    QA Specialist 30 $60 $1,800
    Total 230 $19,800

    If the firm proposes a fixed project fee of $30,000, the estimated labor-based profit would be $10,200, representing a 34% labor-based margin.

    These figures are illustrative and exclude other project costs, such as software licenses, subcontractors, and overhead. A complete project profitability estimate should account for all relevant delivery costs.

    Comparing the estimated hours with actual resource availability also helps determine whether the proposed project can be delivered within the agreed timeline without overallocating team members.

    Make sure to account for client reviews, revisions, and potential project risks when developing the proposed timeline and budget. Clearly defining review stages, expected feedback timelines, and approval responsibilities helps establish realistic delivery schedules and reduces the likelihood of unexpected delays.

    Contingency planning should also reflect the assumptions and risks identified in the project scope. For example, a systems implementation project involving complex data migration or dependencies on third-party vendors may require additional time or budget allowances to address potential complications. The appropriate contingency should be based on the likelihood and potential impact of these risks rather than applying a fixed percentage to every project.

    5. Next Steps

    If the client decides to move forward with the project, what should the next steps be? This may include a final consultation call or meeting, drafting and approving a contract, having an onboarding call, setting a start date, and more. Make it easy for the client to move from saying “Yes, let’s do this” to initiating the project.

    The next steps also mark the transition from sales to project delivery. Once the proposal is accepted, key details such as the agreed scope, deliverables, budget, timeline, and resource requirements need to be carried forward into the statement of work (SOW), contract, and project plan, as applicable.

    A clear handoff typically follows four stages: proposal acceptance → SOW or contract finalization → project setup → kickoff. During this process, keeping client commitments and project information consistent across sales and delivery teams helps reduce miscommunication and unnecessary rework. For professional services firms, connecting customer relationship management (CRM) and project management systems can also reduce duplicate data entry and help delivery teams access relevant information captured during the sales process.

    What Do You Use For Resource Management?

    Accurate resource data helps professional services firms develop more reliable project estimates by providing visibility into team availability, required skills, and associated costs. By understanding which resources are available and how much their time will cost, firms can create more realistic project budgets, identify potential scheduling conflicts, and evaluate expected profitability before committing to a proposal.

    Kantata’s resource management tools eliminate guesswork for more accurate, profitable project estimates.

    Frequently Asked Questions

    How detailed should the scope of a project proposal be?

    A project proposal should clearly define the expected deliverables, key milestones, assumptions, constraints, and any exclusions. Providing enough detail helps establish client expectations, identify potential risks, and reduce misunderstandings about the work included in the proposed engagement.

    How do you estimate the budget for a consulting project proposal?

    Start by identifying the roles and skills required, estimating the hours needed for each project phase, and checking resource availability. Applying role-based cost rates helps calculate estimated labor costs, while accounting for additional expenses, contingencies, and the proposed project fee helps assess expected profitability.

    What is the difference between a project proposal and a statement of work (SOW)?

    A project proposal presents the proposed solution, objectives, scope, timeline, and estimated budget to help a client evaluate an engagement. A statement of work (SOW) typically provides more detailed delivery requirements, including responsibilities, deliverables, milestones, and acceptance criteria. An SOW may form part of the contractual agreement.

    How can resource planning improve the accuracy of project proposals?

    Resource planning helps firms assess whether the required skills and team capacity are available within the proposed project timeline. By considering resource availability, estimated effort, and associated costs before committing to a project, firms can develop more realistic budgets, identify staffing constraints, and better evaluate expected project profitability.

  • Kantata brings natural-language reporting into services software

    Kantata brings natural-language reporting into services software
  • Kantata Launches Expertise Insights

    Kantata Launches Expertise Insights
  • Kantata Touts GenBI Solution for Professional Services Firms

    Kantata Touts GenBI Solution for Professional Services Firms
  • Exclusive: Kantata’s Expertise Insights answers services firms’ business questions in plain English

    Exclusive: Kantata’s Expertise Insights answers services firms’ business questions in plain English
  • Kantata Introduces Expertise Insights: The Real-Time Intelligence Engine Driving Professional Services

    Kantata Introduces Expertise Insights: The Real-Time Intelligence Engine Driving Professional Services

    Next-generation generative BI solution puts natural-language analytics in hands of services firms, empowering them to quickly move from business questions to answers and action

    IRVINE, CA – October 20, 2026 – Professional services organizations have the data they need to run a better business. But most of them can’t access it without a BI analyst, a services engagement, or a week of waiting. Kantata, a leading provider of Professional Services Automation (PSA) solutions, today unveiled Expertise Insights, which promises to change this. Built specifically for professional services, Expertise Insights combines contextualized business data with natural-language interaction and generative AI, giving services teams a simple way to ask questions, uncover insights, and act on what their data is telling them in real time. The news was announced during Technology & Services World in Las Vegas.

    “In professional services, the value of an insight depends on how quickly you can act on it. And all too often, by the time a report arrives, the moment has passed,” said Sarah Edwards, Chief Product Strategy Officer, Kantata. “Expertise Insights removes this constraint, allowing services leaders to move from a business question to a fully built, interactive dashboard that gives them the answers they need to move forward in an informed way with the speed their business demands.”

    Andy Newbill, Senior Manager, Professional Services Automation at Lumanity, agrees.

    “Professional services firms need to make decisions quickly, but critical business insights are often locked behind complex reporting processes and limited access to analytics expertise,” he said. “The ability to ask questions in natural language and get meaningful, actionable answers changes how firms can use their data to manage performance, optimize resources, and drive growth. Expertise Insights makes that intelligence more accessible, helping teams spend less time finding answers and more time acting on them.”

    Built for Professional Services, Not Bolted On

    Generic BI tools can surface data and generic AI can generate answers. But neither inherently understands how a professional services business operates, or what terms, metrics, and relationships matter to a particular firm.

    Built on Amazon Quick and delivered as part of Kantata’s Expertise Engine™, Expertise Insights is trained on professional services data that is contextualized for each business. Through a semantic layer that maps technical data fields to the language people actually use, including firm-specific terminology and concepts such as “chargeable utilization” versus “billable utilization,” the solution provides AI the understanding it needs to make data actionable.

    With Expertise Insights, users can ask questions in plain English and instantly receive an interactive chart, calculation, or executive summary without writing SQL, navigating complex menus, or waiting for an analyst. And beyond these charts and slides, they can get a completed board deck in minutes. That means a leader can move from asking “where are we seeing margin pressure?” to understanding the projects, resources, or financial drivers behind it and use that insight to determine what action to take.

    “Expertise Insights has made creating reports and dashboards so much easier, saving me a tremendous amount of time,” Newbill added. “What really stands out is being able to describe what I need in natural language and have the system help me build it. I recently created a complex report in an hour using calculated fields that would have taken hours. That’s a real shift in how quickly I can get from a question to the insight I need.”

    From Dashboards to Answers

    Expertise Insights includes four out-of-the-box dashboards covering the core dimensions of professional services performance:

    • Finance: margin, revenue/revenue per head, and cost leakage trends
    • Delivery: project health across overall status, schedule, scope, budget, and client health
    • Time: utilization and timesheet compliance
    • Resourcing: available skills, capacity, and demand

    Using Ask Insights capabilities within the solution, users can go beyond these predefined dashboards to generate new analysis on demand. And they can surface signals they may not have known to look for and determine what needs attention.

    Personalization is built in, too. Bookmarks and Pinboards functionality lets users save filtered views and one-off analyses without creating duplicate versions of master dashboards, addressing the dashboard sprawl that has long made traditional BI hard to manage.

    Connecting the Dots

    As enterprise AI evolves from passive reporting to autonomous execution, industry analysts confirm that context is the primary barrier to adoption. In the 2026 Gartner® “Agentic Analytics Fails Without Context and Decision Intelligence,”* research, analysts note: “As decision complexity increases, traditional BI plateaus because dashboards alone are insufficient. Decision intelligence reframes analytics around decisions, while agentic analytics leverages rich context through semantic layers and multiagent systems to enable better decisions.”

    Expertise Insights directly delivers on this paradigm shift.

    Instead of looking separately at utilization, project performance, and financial results, Expertise Insights can link capacity, margin, delivery risk, and financial performance to reveal relationships that might otherwise require multiple reports, or a highly experienced operator who knows where to look.

    “The promise of AI in professional services isn’t about generating more reports or putting a chatbot on top of existing data,” said Vikas Nehru, Chief Technology Officer, Kantata. “It’s about making the organization’s collective expertise available at the moment a decision needs to be made. That requires clean data, context, and AI that understands how the business works. Expertise Insights brings those pieces together so leaders can understand what’s happening, why it’s happening, and what to do next.”

    Availability

    Expertise Insights is available and in use by customers today. Click here to learn more about the solution and how it can help you bring the right information, in the right context, to the people making decisions.

    About Kantata

    Kantata is a leading provider of Professional Services Automation (PSA) solutions that help professional services organizations and agencies ensure consistent excellence and profitability across projects. More than 1,500 organizations worldwide rely on Kantata to instantly assemble the ideal team, easily amplify institutional knowledge, and confidently forecast outcomes. Recognized as a Leader on G2’s PSA Software Grid® and Resource Management Software Grid®, and consistently ranked among the top project management software products in G2’s annual Best Software Awards, Kantata supports the full services lifecycle — from scoping and staffing to delivery and forecasting. For more information, visit www.kantata.com.

    Media Contact:
    Kantata@pancomm.com

    *Gartner, Agentic Analytics Fails Without Context and Decision Intelligence, 15 May 2026. GARTNER is a trademark of Gartner, Inc. and/or its affiliates.
  • Why We Built Expertise Insights: Moving from Dashboards to Decision Intelligence

    Why We Built Expertise Insights: Moving from Dashboards to Decision Intelligence

    Kantata recently launched Expertise Insights, our next-generation generative BI solution powered by natural language, contextualized business data, and AI. While our official announcement covers the feature highlights, I want to share some behind-the-scenes insights on why we built it this way from an architectural standpoint.

    The Problem: Analytics Without Context is Just Noise

    For decades, enterprise software promised “data-driven decisions.” Yet when a services leader asks a fundamental question like “Where are we experiencing margin pressure?”, they face three bad options:

    • Wait a week for a BI analyst
    • Hire a consulting engagement
    • Manually stitch together CSVs

    The root issue here isn’t a lack of data; it’s a lack of domain context.

    Generic BI tools treat data as raw tables, and generic AI chatbots don’t understand services operations. An LLM doesn’t inherently know the difference between chargeable versus billable utilization, or how an unassigned rate card impacts project margin. Without context, AI hallucinates and dashboards become static graveyards.

    The Architecture: Domain Context Meets Governed AI

    Expertise Insights was built directly into the Kantata Expertise Engine™ to solve three core technical challenges:

    1. The Semantic Layer

    We mapped technical schema directly to the real-world operational language services teams use. When you ask a question in plain English, the system evaluates your firm’s specific terminology, rate cards, and resource hierarchies to deliver instant, accurate answers.

    2. Deterministic Authorization

    Authorization is one of the most fragmented problems in software. Giving AI ungoverned database access is a security risk. Expertise Insights enforces real-time, role-based business permissions before executing an analytics trace, ensuring the right people see the right data.

    3. Decision Intelligence Over Passive Reporting

    As Gartner notes in their research, Agentic Analytics Fails Without Context and Decision Intelligence (May, 2026), traditional BI plateaus because dashboards alone are insufficient. Expertise Insights links utilization, project health, and financial telemetry so leaders immediately see why margin is dropping and what to do next.

    Looking Ahead: The Bridge to Agentic AI

    The promise of AI isn’t about prettier charts or side-panel chatbots. It’s about making an organization’s collective intelligence available at the exact moment a decision is made.

    As enterprise AI evolves toward autonomous agentic execution, domain context and strict authorization become mandatory. Expertise Insights delivers both, giving services leaders the speed and clarity required to run a more agile, profitable business.

    Learn more about Kantata Expertise Insights and request a demo today.

  • Basware Moves Kantata AI Into Service Delivery

    Basware Moves Kantata AI Into Service Delivery
  • Why I’m Heading to London: Kantata Converge and the Future of Expertise

    Why I’m Heading to London: Kantata Converge and the Future of Expertise

    I’ll be honest. I’m tired of AI conversations that leave people out.

    I’ve sat in a lot of rooms with professional services leaders, and the talk almost always starts with the tools, the models, the technology. But the minute someone describes what’s actually working at their firm, the story changes. It’s the senior consultant whose instincts finally got captured so the whole team could use them. It’s the project lead who used AI to spot trouble early, then made the call no system could have made on its own.

    Every time, the people are the plot.

    These are the stories my team and I are excited to tell and the conversations I’m looking forward to having at Kantata Converge on November 17, 2026. Alongside a small group of PS leaders who live this story every day, we’ll dig into why it’s the human experience and expertise, not AI, that determine what comes next.

    Expertise Has Always Been the Product

    For generations, expertise has shaped how industries evolve. In professional services, it’s the entire business. Clients don’t hire your firm for its software stack. They hire you for what your people know, how they think, and the judgment they bring to the table when a project goes sideways.

    AI doesn’t change that. All it does is expand how far that expertise can travel. The firms pulling ahead are using AI to harness what their best people know and put that knowledge to work on every engagement. They’re building on their expertise instead of trying to automate around it.

    The Voices Defining the Future of PS

    The future of PS is already being written, and it’s the people doing the work day in and day out who are holding the pen. They’re the ones making hard calls about AI, talent, and delivery. They’re the people who have tried things, learned what breaks, and are willing to talk about it openly.

    And they’re the ones taking the stage at Converge.

    You’ll hear from leaders at Hitachi Vantara, Lumanity, Hippo Digital, and Anthesis who are working through these challenges inside their own organizations right now, as well as industry experts like Alessandro Perilli of IDC, who will bring an analyst’s view of how enterprises can pair the scale of AI with the depth of their human capital.

    “AI is transforming the enterprise, changing how organizations leverage expertise to make decisions and create value. I look forward to joining Kantata Converge to discuss how enterprises can harness the scale of AI and the wealth of their human capital to deliver meaningful business outcomes.”
    – Alessandro Perilli, Vice President, AI Economics, IDC

    You’ll also hear from Kantata’s CEO, Michael Speranza, and Chief Product Strategy Officer, Sarah Edwards, who will share where Kantata is headed and what we’re building alongside our customers to strengthen the future of PS.

    The best part? We’ve also built in what I consider the most valuable part of any event, and that’s time to really dig in during interactive breakout sessions.

    Led by Kantata customers and industry experts, these sessions will share practical, actionable advice on moving past process automation toward real business transformation. And we’re making space for the hard problems, the ones with messy answers — because that’s where the real magic happens.

    Small Room, Big Discussions: Why We’re Keeping it Intimate

    We’re keeping Converge intentionally small, and I love that. Big conferences have their place, but the conversations that change how you lead rarely happen in an auditorium of thousands.

    The best insights come from those peer-to-peer discussions, where you can get candid about what’s working and what isn’t with the people who understand your business, your successes, and your challenges.

    And the setting couldn’t be more perfect. The historic Stationers’ Hall was once home to a guild of printers and booksellers who protected their craft and passed it from master to apprentice. It’s a building made for preserving what experts know. Now it’s hosting a conversation about how to capture knowledge and scale it with AI.

    Because AI can process the past. But only experts can imagine the future. I can’t think of a better room for these conversations.

    Be Part of the Conversation

    Converge won’t be a day of sitting back and taking notes. The best ideas will come from the people in the room, and that includes you.

    So bring the questions that are keeping you up at night. Bring the AI pilot that worked, and the one that didn’t. And bring your answer to the question I keep coming back to: what do your people know that nobody else does?

    That’s your edge, that’s your sizzle. And I want to hear how you’re making it happen.

    Attendance is complimentary, and places are limited. Learn more and request your invite today.

  • Connected AI Compounds. Disconnected AI Depreciates.

    Connected AI Compounds. Disconnected AI Depreciates.

    Most firms measure their AI investment by volume. How many tools. How many agents. How many tasks automated. How many hours saved. And by that measure, a lot of firms have a lot of AI.

    But volume is the wrong measure. The question that actually matters is whether it’s connected.

    There’s a real difference between a firm that has a lot of AI and a firm that has AI that actually understands how its decisions connect to each other. That difference compounds over time, in one direction for firms that get this right, and the other direction for firms that don’t.

    The Fragmentation Problem

    Here’s the challenge with fragmented AI: the cost doesn’t show up when you make the decision. It shows up months later, when you’re already too far down the road to course correct cheaply.

    If I’m making a staffing decision on a project without the context of the customer relationship, the pricing, the talent availability, the delivery history on similar engagements, I’m making a decision without the collective knowledge to really know whether it’s the right call. I won’t find out it was wrong until the margin is already gone.

    That’s not a hypothetical. That’s what happens when AI is implemented in silos: each tool solving a local problem, none of them sharing context, all of them generating inputs without generating intelligence.

    “The cost of fragmentation just turns up too late, because you haven’t got the connected knowledge, and you’re not compounding that knowledge continuously as you sell and deliver to your customers.”

    The Knowledge Leak

    There’s another problem sitting underneath fragmentation that doesn’t get the attention it deserves: knowledge leak.

    When you work in silos, you learn in silos. You learn something valuable, maybe that a certain type of project consistently overruns in a particular phase, or that a certain kind of customer engagement creates delivery risk that doesn’t show up in the original scope. But because that knowledge lives in one part of the business, you can’t connect it. You can’t compound it.

    Which means the next time you scope a similar project, you’re starting from scratch instead of starting smarter. The lesson didn’t make it into the playbook. The risk that cost you margin last time is invisible again this time.

    Connected AI changes this. When knowledge flows across the delivery lifecycle, from scoping to resourcing to delivery to outcome, every project makes the next one better. Scoping gets tighter. Risk gets caught earlier. Margin improves not because you worked harder but because you got smarter.

    That’s compounding. And it’s the real return on AI investment that most firms are leaving on the table.

    50 Co-Pilots That Don’t Share Context

    Let me be specific about what “a lot of AI” actually looks like inside most firms right now.

    It looks like pockets of speed. Something quicker here, something more automated there. And that’s genuinely valuable, as far as it goes. But 50 co-pilots that don’t share context or knowledge? You’re not learning from that. You’re generating inputs, not intelligence. Volume without coherence.

    What you actually need is a system that orchestrates across the full delivery lifecycle. One that can answer: did we make the right pricing decision at the start? The right staffing decision? Where did margin get impacted? Where did risk occur? How does what we learned in this engagement inform how we scope and deliver the next one?

    Can your AI make those connections? Can it understand how a decision at scoping affected the outcome at delivery? If the answer is no, you have a lot of AI but not an AI advantage.

    The Cost Visibility Problem Nobody is Tracking

    Everyone knows agents aren’t free. Token costs, orchestration costs, the engineering investment in building and maintaining them…it all adds up. But most firms aren’t tracking these costs in the context of project delivery. They’re not connecting AI spend back to margin.

    This means at the end of the quarter, when someone asks what AI ROI actually looks like, the answer is: we don’t know. Sure, we know some things got faster. But we don’t know what that cost, what it saved, or what it tells us about where to invest next.

    Fragmented data also creates a data trust problem. Multiple sources of truth. Low confidence in the numbers. When leadership can’t trust the data, AI investment stalls, not because it isn’t working, but because no one can prove it is.

    Connected AI solves this as a natural consequence of integration. When agent activity is tracked in the same system as project delivery, you can see what’s contributing, what it costs, and what the margin impact is. That’s the foundation for managing AI as a business investment rather than a technology experiment.

    What Compounding Actually Looks Like

    When AI is connected across the services delivery lifecycle, learning from one project informs the next. Margin improves not because you cut costs, but because you scoped better, staffed better, caught risk earlier. Predictability improves, which means you can price with more confidence and start moving toward outcome-based models without taking on unmanageable risk.

    But perhaps most importantly, new propositions start to emerge. You start to see patterns across engagements that would have been invisible in silos. You understand which types of work are repeatable and ready to be productized. You identify where your expertise is genuinely differentiated and where AI can safely carry the load.

    That’s knowledge compounding into new value — and that’s how AI stops being a cost-reduction play and becomes a revenue-generation engine. It requires a system that orchestrates across your delivery lifecycle and accumulates what it learns. It doesn’t happen with 50 co-pilots running in parallel silos.

    The Question Worth Asking

    The question is no longer “how much AI do we have?” but “how connected is our AI?”

    Can your AI understand how a pricing decision connects to a delivery outcome? Can it surface the margin impact of a resourcing call? Can it take what you learned this quarter and make sure next quarter’s projects start with that knowledge built in?

    If the answer is no, you have a volume problem masquerading as an AI strategy. The cost of that, in knowledge leak, missed margin, and decisions made without the right context, constantly accumulates — whether you can see it on a dashboard or not.

    Connected AI compounds. Disconnected AI depreciates. The firms that understand the distinction and build towards it deliberately are the ones that will have a real advantage when the dust settles.