AI For Business Leaders

IBM has announced the global availability of IBM Bob, an AI-first development partner built for enterprise software teams. The platform goes beyond code generation to automate the full software development lifecycle, from planning and coding to testing, deployment, and modernisation, with governance, compliance, and security controls embedded at every step. Bob is built on a structured framework that embeds AI into every role across the development process through persona-based modes, enforced standards, reusable playbooks, tool calling, and human-in-the-loop governance. A key differentiator is its multi-model orchestration capability, which dynamically routes each task to a suitable model based on accuracy, performance, and cost, drawing on frontier models including Anthropic Claude, Mistral open source models, and IBM Granite, alongside specialised fine-tuned models for code reasoning and security. Simpler tasks go to lighter models, while complex ones go to more capable ones. Dinesh Nirmal, Senior Vice President, IBM Software, said, "Every business is racing to modernise. But speed without control and transparency is a liability. IBM Bob is how enterprises can move at AI speed without sacrificing the governance and security needs their businesses require. Bob was engineered by developers inside IBM for the millions like them worldwide, and it’s the foundation on which enterprises will become truly AI-first.” The aibl take: Most enterprise development teams are not moving slowly because their developers lack capability. They are moving slowly because the systems they are working on carry decades of accumulated complexity, undocumented logic, and technical debt that takes weeks just to understand before a single line can safely be changed. AI code generation tools that ignore that context do not solve the problem. They accelerate into it. For mid-market technology leaders, the 45% average productivity gain across 80,000 IBM employees is not the most important number in this announcement. The more significant signal is Blue Pearl completing weeks of engineering work in three days with zero post-deployment defects. Speed with that quality standard is what enterprise modernisation actually requires. Neel Sundaresan, Christopher Aiken, David Reich, Michael R. McCracken, Maj USAF (Ret), Anil Shankar, Chubala Rao, Jatinder (Rocky) Sachdeva, Mirella Batista, Richard M. Powell, Austin Imperato, Mark Vallone, Atul Sharma, MBA, PMP, Anna Topol, Sarah Bandy, Michael Bianchi, Dr. Liliana Horne, PhD, Al Hamid, Kirk Mettler, Sai Nageshwaran, Kelli Jordan, Bill Starke, Billy Seabrook 📰 We break down shifts like this in the aibl newsletter. Practical signals for mid-market leaders navigating AI adoption. Link in comments.

#workforceLIVE! is next week.... And we’re delighted to announce 100 school is partnering with aibl for workforceLIVE, an invite-only operator session bringing together senior People & HR leaders to move beyond AI hype and into practical adoption. Max H. will be hosting an off-the-record peer roundtable: “From 'Using AI' to 'Thinking with AI': Why high usage isn't producing high capability”, a practical, no-pitch discussion under Chatham House Rule. Wed 6 May 2026 (London) ⏳ Limited spaces remaining — apply to attend here : https://buff.ly/GZACu79 #workforceLIVE #aiblLIVE #LeadershipSeries #AIadoption #FutureOfWork #HRTransformation

Strategy and implementation are two different disciplines. A firm that's strong at discovery and architecture isn't always the right firm to put code into production. For most mid-market leaders, that distinction only becomes clear once a project is already underway. We're building the aibl AI Enablement Directory: a searchable, filterable database of firms doing AI work across the UK. No pay-to-play rankings. No editorial endorsement. This week we're mapping the AI Implementation & Deployment space. These are firms we've identified that focus on technical delivery: building, integrating, and deploying AI systems into existing environments. Virtusa — Global digital engineering firm with AI implementation across financial services, healthcare, and insurance. Exception— Scotland-based AI consultancy specialising in strategy, agent development, and project delivery for regulated organisations. Ignitho Inc.— Data engineering and agentic AI services for enterprise clients, with offices in London and New York. Radium AI — RPA monitoring and AI platform for managing digital workers across multiple automation technologies. Smart Business Datavid — London-based specialists in graph data engineering and knowledge graph design for regulated industries. Datasparq — UK data and AI consultancy working from strategy through to implementation, with clients including GSK, GXO, and easyJet. Shivacha Technologies — Blockchain and AI development company, providing innovative solutions in smart contracts, DApps, and AI/ML implementations across multiple sectors. Smart Vision Europe Ltd — London-based firm delivering predictive analytics implementation, training, and support. Airwalk Reply — Part of the Reply Group. Cloud adoption and AI readiness consultancy, rated among the UK's leading management consultants by the Financial Times. Softwire — London-based digital engineering consultancy with clients including the BBC, Starling Bank, and LNER. Ivenium Marketing — Marketing automation implementation specialists across Eloqua, Marketo, and Salesforce, with an AI adoption practice. Neurapses Technologies — AI and ML solutions provider covering document processing, information extraction, and data analytics. Applied Data Science Partners) — London-based AI and data science consultancy, ISO 27001 certified, working across private and public sector clients. Know someone who works with any of these firms? Tag them below. Think a firm is missing? Drop them in the comments. 👇 If you run an AI implementation business and want to be included, register here: https://buff.ly/N6kVB2u Sign up for the #aiblBRIEF newsletter to get access to all published playbooks: https://buff.ly/eUeMc2C #AIAdoption #PracticalAI #MidMarket #AIStrategy #BusinessLeaders #InnovationLeaders #AIEnablement

Daloopa has announced a new integration with Perplexity that allows joint customers to connect their existing financial data licenses directly into Perplexity and the always-on digital worker, Perplexity Computer. The integration introduces a bring-your-own-license (BYOL) model, enabling investment teams to access and analyze Daloopa's structured, audit-ready fundamental data within Perplexity's interface, without needing to configure APIs, manage entitlements, or switch between tools. Users can query their licensed data through Perplexity's answer engine, or use Computer to run complex financial analyses using Daloopa data alongside external market signals, all within a single workflow with fully traceable citations. Thomas Li, CEO of Daloopa, said, "As investment firms move AI into real workflows, they need systems that are not just fast, but also accurate, consistent, and traceable. By integrating with Perplexity, we're enabling teams to bring their existing data licenses into AI platforms, so they can move faster without sacrificing rigor." Dmitry Shevelenko, Chief Business Officer at Perplexity AI, said, "By connecting Perplexity to the financial data our customers already rely on, we're making it possible to run analysis, generate research, and answer complex questions in a single workflow, with full visibility into the underlying sources." The aibl take: Investment teams are not short of AI tools. They are short of AI tools they can actually trust with high-stakes analysis. Valuation work, earnings modelling, portfolio decisions - these are environments where a single data inconsistency does not just slow things down. It materially changes the outcome. That is a different risk profile from most enterprise AI use cases, and it demands a different standard of data quality. The limiting factor in financial services AI has never been model capability. It is whether the data those models are reasoning over is accurate, consistent, and traceable back to its original source. Speed built on unreliable data is not an advantage in investment workflows. It is a faster route to the wrong conclusion. Alexandra (Woodward) Paone, Deepak Agarwal, Shaurya Priya, Rohit Saboo, Sanu MS, Rishikesh Pathre, Amit Agarwal, Pankaj Kumar Singh, Prateek Jain, Jesse Dwyer, Johnny Ho, Siddharth Dhanasekar, Alex Nesic, Joshua Barimon Firima, Srinikhil Vemuri, Uchechukwu Ajuzieogu 📰 We break down shifts like this in the aibl newsletter. Practical signals for mid-market leaders navigating AI adoption. Link in comments.

Coming soon: Most mid-market AI investment is being driven by fear of missing out, not business purpose. And Paul O'Sullivan thinks that's the root cause of pilot collapse. Paul O'Sullivan is SVP of Solution Engineering at Salesforce. He's also spent the last 18 months watching organisations move fast into pilots and stall just as quickly. He knows exactly why. Next week, we are publishing an exclusive interview with Paul. He explains why the 95% pilot collapse rate is predictable, and what separates the organisations that push through it from those that don't. We discuss: • The imagination constraint: Why leaders default to point-to-point AI solutions and what it costs them • Conversation design: Paul's framework for deploying AI in customer-facing roles that actually holds up under pressure • The first 90 days: A tight, practical case for picking one use case, scoping it properly, and measuring what matters Subscribe to the aiblBRIEF for early access to the full interview: https://buff.ly/vWTGvZc #aiblBRIEF #aibl #AIadoption #MidMarket #AgenticAI #Salesforce

Registration for #workforceLIVE closes this week. workforceLIVE is deliberately small. If you lead HR, People, Talent, or Capability in a mid-market business, this is the morning that matters. 50 seats. Application only. No sales pitches, no broadcast panels, no sponsored keynotes. How do you actually build an AI-enabled workforce? Not the board deck answer. The operational reality. On Wednesday, • Kerri O'Neill Chartered FCIPD brings Ipsos in the UK's human-AI principles, built and tested at scale across a global organisation. • Dr. Laura Weis from WPP talks through what it takes to redesign work, not just retrain people. • Matt Phelan brings hard data from The Happiness Index's 2026 report on what keeps people willing when the pressure is relentless. • Ben Stocken (atherio) joins the room to add a practical operator’s view of what it takes to make this land. Then the room works. Chatham House Rule throughout. What's said on Tuesday stays on Tuesday. This is the last chance to register. 👉 Register here: https://buff.ly/ptLX4ux #workforceLIVE #aibl #HRLeadership #PeopleLeaders #AIAdoption #LeadershipSeries

𝗧𝗵𝗲 𝘁𝗵𝗿𝗲𝗲 𝗔𝗜 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 Forget the five-year AI roadmap. The most important work you can do right now is improving the questions you ask your leadership team. And this isn��t theoretical anymore, we’re now getting real-world data on which work is already being touched by these tools. In his article, Richard Breeden suggests three questions every leader should bring to the table on Monday morning: 1. Where are our highest-paid people doing £20-an-hour work? 2. Are we accidentally building an “experience gap” into our future talent pipeline? 3. Who in this building has the explicit permission to get this wrong? These questions are designed to provoke action, not just more meetings. We’ve detailed the logic behind them, and how to handle the answers, in our feature on aiblmedia.com. If you didn’t catch Richard’s article the first time round, it’s in the comments. #Leadership #Management #AI #Strategy #aibl

Netomi has announced $110 million in new funding led by Accenture Ventures, with participation from Adobe Ventures, WndrCo, SLW, NAVER Ventures, Metis Strategy, and Fin Capital. This round builds on early investments from AI pioneers Greg Brockman, Demis Hassabis, and Mustafa Suleyman. Netomi is the enterprise agentic CX platform built for the highest-stakes environments - operating at massive scale, under regulatory oversight. In addition to an investment, Accenture has also entered into a global alliance with Netomi to build a playbook for enterprise agentic CX deployment, bringing Netomi's platform to Accenture's enterprise clients across the world. Ndidiamaka Oteh, CEO, Accenture Song, said, "Agentic AI is opening an entirely new chapter for customer experience. One where brands can respond with greater empathy, consistency and intelligence at every touchpoint. Netomi’s platform doesn’t just make service faster; it strengthens the connection between people and the brands they trust. Together, we’re empowering our clients to reinvent how they serve their customers - seamlessly, responsibly and at scale, so they can grow with confidence in an era of continuous change.” At a moment when deployment of production-grade agentic AI is the most sought-after capability within enterprise, Netomi stands alone - the only CX platform with a track record of zero failures, zero broken guardrails, and zero brand violations. This foundation is what positions Netomi to pursue the customer experience: AI embedded directly inside the digital experience itself - observing every journey, reasoning in real time, and dynamically reshaping the experience around each customer as their journey unfolds. Not a disconnected 'concierge' layer on top. Not a chatbot floating in the corner. But intelligence woven into the very fabric of the digital experience. The aibl take: The more useful question for mid-market leaders investing in CX technology is not how quickly your AI can respond to a customer complaint. It is how much of that complaint volume exists because your digital experience was not intelligent enough to prevent it in the first place. For businesses operating at scale in regulated environments, the bar is not just response speed or containment rate. It is whether your AI can handle 40,000 concurrent requests during a peak event without a single governance failure. That is the production standard that separates pilots from platforms businesses can actually depend on. Alan Lepofsky, Preet Dhatt, MBA, Bobby Gupta, Puneet Mehta, Shavi Gupta, Logan Allin, Abhay Girnara, Bhawna Mittal, Justin Wexler, Rohit Handa, Mubeen Mohammad, Shobhit Agrawal, Matthew D. Mann, CFA, Ankit Gupta, Leo Nagdas, Shilpi Sardana, David Herrera 📰 We break down shifts like this in the aibl newsletter. Practical signals for mid-market leaders navigating AI adoption. Link in comments.

Anaconda, Inc. has announced the acquisition of Outerbounds (acq. by Anaconda), the company behind Metaflow. The acquisition marks a step in Anaconda's evolution for developing enterprise AI, to the first unified platform spanning the entire AI-native development software lifecycle. David DeSanto, CEO of Anaconda, said, "The future belongs to AI-native development, where the AI model is the core of how applications are built, not something bolted on at the end. The problem enterprises face today is that delivering on that vision requires stitching together tools, platforms, and governance components that were never designed to work as one, nor to even work with AI. Until now, no other platform has spanned the entire AI-native development lifecycle. For the first time, with Anaconda and Outerbounds, enterprises can securely scale complex, compound AI systems from idea all the way to production on the infrastructure they already trust.” Ville Tuulos, co-founder and CEO of Outerbounds, said, "Anaconda has spent more than a decade earning the trust of the world's largest enterprises, and that trust is exactly the foundation our customers need to take AI systems all the way to production with confidence. What makes this combination so powerful is a shared commitment to Python, reproducibility, and software engineering best practices. Together, we can give data scientists and AI engineers everything they need to move from secure environments to production-grade orchestration, and turn AI innovation into real, measurable outcomes." The aibl take: AI-generated code now accounts for nearly half of all new code in enterprise pipelines. It also produces significantly more defects than human-written code, and the majority of dependencies recommended by AI coding assistants carry known risks. The bottleneck has shifted. The problem is no longer writing code fast enough. It is governing everything that code depends on before it reaches production. For mid-market technology leaders, the honest risk assessment is not whether your team is adopting AI development tools. It is whether the infrastructure underneath those tools is governed well enough to catch what AI gets wrong before it becomes a production problem. Speed into an ungoverned pipeline is not an advantage. It is where the next wave of enterprise technical debt is being built right now. Jackie Campbell, ITIL v4, CSM, Peter Wang, Megan Niedermeyer, Susan Andrus, Laura Sellers, Matt Piotrowski, Jane Kim, Vanessa MacIlwaine, PHR-CA, Derek E. Weeks, Julian Ustiyanovych, Katie Nelson, Savin Goyal, Steve Vassallo, Aidan Polizois, Apoorva Pandhi, Oleg Avdeëv, Will Hart, Aryan Dhawan, Chase Murphy, Valay Dave, Gaurav Bhushan, Nixon Alsola 📰 We break down shifts like this in the aibl newsletter. Practical signals for mid-market leaders navigating AI adoption. Link in comments.

The most sophisticated computing system most of us will ever use is already running. We just keep underestimating it. Kerri O'Neill Chartered FCIPD's (Ipsos in the UK) point in this clip is simple, and it lands harder than most AI commentary. At a time when organisations are rushing to add AI to every workflow, she's asking people to remember what they're actually bringing to the table themselves. It connects directly to what she calls cognitive surrender: the risk that people stop checking AI outputs, stop questioning, and stop applying the judgment that makes them worth having in the room. A Wharton study she cites puts some rigour behind what she's been observing in her own teams at Ipsos. Watch the full interview here: https://buff.ly/YDe41IN The aibl take: • Cognitive surrender is probably already happening in organisations that haven't trained people to actively question AI outputs. It doesn't announce itself. • The organisations that manage this best will be the ones that make critical evaluation a habit, not an afterthought added to a prompt engineering course. • Kerri's instinct to give everyone AI access early, paired with a clear philosophical framework, is one of the more thoughtful approaches we've come across. Most rollouts do one or the other. Kerri is joining us at workforceLIVE on 6 May — a 50-person, invite-only morning for senior HR, People, Talent and L&D leaders in London. Free to attend. Apply here: https://buff.ly/lTMTeIS Is your organisation actively designing against cognitive surrender? What does that look like in practice? Subscribe to the #aiblBRIEF: https://buff.ly/vWTGvZc #aibl #aiblBRIEF #AIAdoption #workforceLIVE #CriticalThinking